A system can efficiently control access to an interactive computing environment. The system can receive authentication data of an authentication attempt associated with an entity. The system can determine, for the entity, a historical vector including features that include sub-features. The historical vector can be determined by generating synthetic data, generating weights, and determining probabilities. The synthetic data can be based on historical authentication attempts by entities other than the entity. The weights can correspond to sub-features of the historical vector. The probabilities can indicate a likelihood that a corresponding sub-feature is involved in the authentication attempt. The system can compare the historical vector to the authentication data. The system can generate a responsive message based on the comparison for controlling access to the interactive computing environment.
Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
3.
ENHANCED RANK-ORDER FOR RISK ASSESSMENT USING PARAMETERIZED DECAY
A system can receive data about a target entity. The system can identify, for each data point included in the data, a set of parameters. The system can generate, based at least in part on the set of parameters, a rank-order list that includes the data. The rank-order list can be generated by a parameterized decay model. The system can provide the rank-order list to a user entity to control an interaction between the target entity and the user entity.
G06Q 10/04 - Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Search indexes can be automatically generated and used for expediting searching of a computerized database. For example, a system can access an inquiry dataset that includes relationships between prior inquiries and returned records from a database. The system can then generate a set of Boolean indexes based on the prior inquiries. The system can then identify frequent indexes that occur at least a threshold number of times in the set of Boolean indexes and that have estimated candidate sizes that are less than a threshold size. The system can then select the frequent index with the highest frequency from among the frequent indexes. The selected frequent index can be subsequently used to expedite searching of the database in response to receiving a search query associated with the frequent index from a client device.
In one example, a method includes building a first model including a first data set, the first data set excluding protected class. A second model is built including a second data set, the second data set including a subset of the first data set. The models may be used to generate and index score distributions. The distributions are compared to determine a self-report correlation. In response to determining the aggregate correlation is less than the first disparate impact threshold, the method generates a first prediction of disparate impact. The method then generates an aggregate correlation for attributes in the first model with the self-reported protected class attribute, the plurality of attributes being generated by the first model. In response to determining the aggregate correlation exceeds the second disparate impact threshold, the method generates a second prediction of disparate impact.
Various aspects of the present disclosure involve computing environments that provide third-party access-control support. For instance, an access-control computing system can access a secure identity repository having role history data from various contributor computing systems. The access-control computing system can compare an identified set of roles with a set of roles described by role history data for a target entity. The access-control computing system can determine, from the comparison, whether the target entity poses a security risk based on inconsistencies between the sets of roles, durations associated with the roles, or both. The access-control computing system can provide a client computing system with a dynamic access-control data structure that is generated based on the comparison. The dynamic access-control data structure allows the client computing system to output the security assessment to an end user or to otherwise facilitate further security measures with respect to the target entity.
H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
7.
SYSTEM AND METHOD FOR CONTROLLING ACCESS TO A RESOURCE BASED ON A COMPLIANCE SCORE
A system can generate one or more compliance graphs and a compliance score that can be used at least in risk assessment operations. The system can access a request to visualize target entity data and calculate a compliance score for the target entity. The system can access attribute data. The system can generate one or more compliance graphs and a compliance score using the attribute data. The system can compare the compliance score to a compliance threshold and transmit a message including the results of the comparison to a remote system for controlling access of the target entity to an interactive computing environment.
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 41/22 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
8.
DATA VALIDATION TECHNIQUES FOR SENSITIVE DATA MIGRATION ACROSS MULTIPLE PLATFORMS
Techniques for validating large amounts of sensitive data migrated across multiple platforms without revealing the content of the sensitive data are provided. For example, a processing device can transform data in a first data file stored on a first platform to common data formats. The processing device can generate a first set of hash values. The processing device can receive a second set of hash values for a second data file stored on a second platform. The processing device can compare the first set of hash values and the second set of hash values and cause the first data file or the second data file to be modified based on a difference between the sets of hash values.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 67/06 - Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
9.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR IDENTIFYING IDENTITY MANIPULATION
A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
10.
ARTIFICIAL INTELLIGENCE TECHNIQUES FOR IDENTIFYING IDENTITY MANIPULATION
A system can efficiently determine whether an identity is manipulated. The system can receive entity data and interaction data associated with a target entity. The system can determine, based on the entity data and the interaction data, one or more risk signals associated with the target entity using one or more artificial intelligence models. The system can generate a linked graph structure based on a first graph structure and a second graph structure each generated using the entity data and the interaction data. The system can apply the one or more risk signals to the linked graph structure to determine a risk indicator associated with the target entity. The system can provide a responsive message based on the risk indicator. The responsive message can be used to control access of the target entity to an interactive computing environment.
A method can be used to predict risk and provide explainable outcomes using machine learning based on wavelet analysis. A risk prediction model can be applied to time-series data for an attribute associated with a target entity to generate a risk indicator for the target entity. The risk prediction model can include a feature learning model and a risk classification model configured to generate the risk indicator as output. Parameters of the feature learning model can be accessed and a plurality of basis functions of a wavelet transformation can be applied on the parameters of the feature learning model to generate a set of parameter wavelet coefficients. Explanatory data can be generated for the risk indicator based on the set of parameter wavelet coefficients. A responsive message can be transmitted to a remote computing device including the risk indicator and the explanatory data for use in controlling access of the target entity to an interactive computing environment.
Certain aspects involve building timing-prediction models for predicting timing of events that can impact one or more operations of machine-implemented environments. For instance, a computing system can generate program code executable by a host system for modifying host system operations based on the timing of a target event. The program code, when executed, can cause processing hardware to a compute set of probabilities for the target event by applying a set of trained timing-prediction models to predictor variable data. A time of the target event can be computed from the set of probabilities. To generate the program code, the computing system can build the set of timing-prediction models from training data. Building each timing-prediction model can include training the timing-prediction model to predict one or more target events for a different time bin within the training window. The computing system can generate and output program code implementing the models' functionality.
In some aspects, a computing system can use a machine learning model for resource management. For example, the system can receive a request for a set of steps associated with a target model output of a machine learning model. The request can include a starting input feature set and a number of steps. For each of the number of steps, the system can calculate a change to one or more features from the starting input feature set to arrive at the target model output based on a current position in feature space of the machine learning model. The system can update a feature vector by applying the change to the features of the starting input feature set and transmitting the set of steps. The system can then cause a resource of the external computing system to transition toward a position defined by the target model output.
36 - Financial, insurance and real estate services
42 - Scientific, technological and industrial services, research and design
45 - Legal and security services; personal services for individuals.
Goods & Services
(1) Consulting services in the field of life, health, property and casualty insurance; consulting services in the field of account origination and portfolio management; credit information services, namely, providing credit information relating to consumer or commercial applicants for credit, mortgage loans, utility services and employment and to fraud prevention; providing credit application processing; credit inquiry and consulting services; real estate appraisal services; providing an on-line credit information database featuring information relating to insurance, credit, financial data, mortgage loans, and employment, and to debt servicing; credit evaluation, analysis and alert services; and providing information relating to insurance, credit, mortgage loans and debt management; and excluding foreign exchange services and money market trading services.
(2) Providing non-downloadable software for risk analysis, identity verification, data analysis, risk modelling and analytics, decision making and analysis, in the field of fraud protection.
(3) Providing information over the Internet in the fields of identity verification and fraud protection; providing credit and financial fraud detection services.
15.
MACHINE-LEARNING TECHNIQUES FOR PREDICTING UNOBSERVABLE OUTPUTS
In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.
In some aspects, a computing system can generate and optimize a machine learning model to estimate an unobservable capacity of a target system or entity. The computing system can access training vectors which include training predictor variables, training performance indicators, and task quantities. A training performance indicator indicating performance outcome corresponding to the predictor variables and a task quantity associated with a task assigned to the target entity that leads to the training performance indicator. The machine learning model can be trained by performing adjustments of parameters of the machine learning model to minimize a loss function defined based on the training vectors. The trained machine learning model can be used to estimate the capacity of the target system or entity for handling tasks and be used in assigning tasks to the target entity according to the determined capacity.
In some aspects, a machine learning (ML) model can be trained for risk assessment. The ML model can be trained to determine a risk indicator for a target entity from predictor variables associated with the target entity. The predictor variables are obtained from multiple sources with varying availability, and the training of the ML model is accomplished based on a multi-dimensional representation of common information from the set of data sources. Once generated, the risk indicator can be transmitted to a remote computing device in a responsive message for use in controlling access of the target entity to a computing environment.
In some aspects, a computing system can train a machine learning model for risk assessment. For example, the system can access a trained machine learning model to determine a final risk indicator of a target entity from a baseline data associated with the target entity and an alternative data associated with the target entity. The computing system can generate the final risk indicator of the target entity using the baseline data associated with the target entity and the alternative data associated with the target entity. The computing system can also transmit, to a remote computing device, a responsive message comprising at least the final risk indicator for use in controlling access of the target entity to one or more computing environments.
Various aspects involve explainable machine learning based on time-series transformation. For instance, a computing system accesses time-series data of a predictor variable associated with a target entity. The computing system generates a first set of transformed time-series data instances by applying a first family of transformations on the time-series data. Any non-negative linear combination of the first family of transformations forms an interpretable transformation of the time-series data. The computing system determines a risk indicator for the target entity indicating a level of risk associated with the target entity by inputting the first set of transformed time-series data instances into a machine learning model. The computing system transmits, to a remote computing device, a responsive message including the risk indicator. The risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.
A system can efficiently control access to an interactive computing environment using similarity hashing. The system can receive a first similarity-preserving hash based on an interaction request associated with a target entity for requesting access to an interactive computing environment. The system can receive a second similarity-preserving hash based on entity data relating to an entity and including interaction data. The system can determine, based on a comparison between the first similarity-preserving hash and the second similarity-preserving hash, a likelihood that the interaction request is legitimate. The system can provide a responsive message based on a threshold relating to the likelihood, the responsive message usable to control access to the interactive computing environment.
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
21.
TECHNIQUES FOR MITIGATING BACK PRESSURE, AUTO-SCALING THROUGHPUT, AND CONCURRENCY SCALING IN LARGE-SCALE AUTOMATED EVENT-DRIVEN DATA PIPELINES
Systems and methods for fine-tuned control over data transfer processes. An exemplary data transfer process may include: receiving a data stream at a storage service; receiving, at a first function, one or more notifications; in response to each notification, passing, by the first function, a message to a queue, the message comprising an address of a respective file within the storage service; receiving, at an invocation of a second function at a second computing service, one or more messages from the queue; retrieving, by the second function, data from one or more files based on the address in each of the one or more messages; and writing, by the second function, the data to a database. Systems and methods according to aspects of the present disclosure improve processes of transferring data from a data warehouse or database to a cloud-based database by mitigating back-pressure, auto-scaling throughput, and controlling concurrency scaling.
A system can generate an identity graph that can be arranged temporally for use at least in risk assessment operations and similar analyses. The system can receive a request to visualize entity data. The system can receive the entity data, which can include a set of identity data and a set of interaction data. The system can generate a temporal identity graph using the entity data. The temporal identity graph can temporally link the identity of the entity with the interactions associated with the entity. The system can generate a graphical user interface that may be configured to provide the temporal identity graph in response to the request to visualize the entity data. The graphical user interface can include interactive elements representing the temporal identity graph. Each interactive element can be selected by a user of the graphical user interface to display previously non-displayed information.
In some aspects, a record-matching computing system for detecting fragmented records is provided. The record-matching system is configured to identify a list of candidate records for merging from a set of data records. The record-matching system determines a matching decision for each pair of candidate records in the list and generates a graph. The graph includes nodes representing respective candidate records and edges connecting the nodes. Each edge represents a match between a pair of nodes connected by the edge according to the matching decisions. The record-matching system detects a connected component in the graph from which a qualified connected component is identified based on the minimum connectivity of the qualified connected component. The record-matching system updates the set of data records stored by merging candidate records represented by the nodes in the qualified connected component.
A system can generate a risk assessment associated with a target entity. The system can determine an attribute tier for each entity in a set of entities. For each attribute tier, the system can: generate a model configured to predict a percent change in the attribute over a time period for each entity in the respective attribute tier; determine the percent change in the attribute for each entity in the respective attribute tier using the model associated with the respective attribute tier; rank each entity in the attribute tier based on the predicted percent change in the attribute; and assign a score to each entity in the attribute tier based on the rank of the respective entity and on a preconfigured distribution. The system can determine a risk indicator based, in part, on the score.
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to receiving a query record and search in a data repository storing data records for a record that matches the query record. The record-matching computing system retrieves a reference record from the data records and generates multiple identifier scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and the reference record. The record-matching computing system generates an overall matching score by combining at least two of the identifier scores and determines the reference record as a match to the query record based on the overall matching score exceeding a threshold value.
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to search for a data record that matches a query record. The record-matching computing system retrieves a reference record from data records and generates multiple identifier attributes for the query record and reference record, including identifier scores and compound scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and reference record. A compound score is generated by combining two or more identifier scores. The record-matching computing system applies the identifier attributes to a machine learning model configured to predict a match classification based on input identifier attributes for a pair of data records. The record-matching server can identify the reference records as a match to the query record based on the match classification indicating a match.
In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.
The present disclosure involves systems and methods for identity authentication across multiple institutions using a trusted mobile device as a proxy for a user login. In one example, the operations include identifying a request to trust a particular user associated with a first entity in a digital ID network. A set of personally identifiable information (PII) associated with the user is obtained via the first entity and an identity verification (IDV)/fraud risk analysis is performed. In response to satisfying the analysis, instructions are transmitted to the user to verify the identity via a mobile trust application on an associated mobile device. Upon verification, the mobile device is bound to the user within the digital ID network along with a digital ID associated with the particular user. The digital ID can be used by other entities registered within the digital ID network to authenticate the user.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
In some embodiments, a data visualization system accesses data entries associated with entities and obtained from multiple data sources. The data visualization system generates a classification map for the entities by classifying the entities into groups based on the data entries from the multiple data sources. The groups are arranged in the classification map according to values of the data entries of the entities. The data visualization system determines one or more metrics for the groups. The data visualization system further determines visualizations based on the classification map, each visualization representing a metric or the data entries from one of the data sources. The data visualization system generates, for inclusion in a user interface of the system, selectable interface elements configured for invoking an editing tool for updating the visualizations. The selectable interface elements for the visualizations are arranged in the respective visualizations according to the classification map.
Techniques are described herein for applying natural language processing (NLP) techniques to time series data to derive attributes of an object for use with a machine-learning model. In one example, a system can receive a time series associated with an object over a time window, where the time series includes a set of discrete values. The system can then generate a time series encoding based on the time series. The system can provide the time series encoding as input to a trained natural language processing (NLP) model, which can generate one or more output embeddings based on the time series encoding. Next, the system can determine at least one attribute associated with the object based on the one or more output embeddings. The system can then provide the attributes for use with a machine-learning model, which may for example be configured to predict a future characteristic of the object.
Search indexes can be automatically generated and used for expediting searching of a computerized database. For example, a system can access an inquiry dataset that includes relationships between prior inquiries and returned records from a database. The system can then generate a set of Boolean indexes based on the prior inquiries. The system can then identify frequent indexes that occur at least a threshold number of times in the set of Boolean indexes and that have estimated candidate sizes that are less than a threshold size. The system can then select the frequent index with the highest frequency from among the frequent indexes. The selected frequent index can be subsequently used to expedite searching of the database in response to receiving a search query associated with the frequent index from a client device.
A system can generate a risk report associated with a target entity. For example, the system can receive a request for a risk report associated with a target entity. The system can retrieve a set of records associated with the target entity where each record is associated with a state. The system can generate a first GUI including a selectable list of states associated with each record of the set of records. The system can receive a selection of a first subset of states. The system can transmit, to a remote computing device, the risk report including at least information from a first subset of records associated with the first subset of states for use in controlling access of the target entity to one or more interactive computing environments. The risk report can be used for controlling access of the target entity to one or more interactive computing environments.
Systems and methods for secure resource management are provided. A secure resource management system receives from a client computing system associated with a resource provider a query for classifying a resource user associated with the resource provider. The secure resource management system determining a set of resource abuser criteria for classifying the resource user as a resource abuser. The secure resource management system determines that the resource user is a resource abuser or a potential resource abuser based on the set of resource abuser criteria and a resource transaction history associated with the resource user. Based on determining that the resource user is a resource abuser or a potential resource abuser, the secure resource management system generates and transmits a response to the query to the client computing system. The response can be used to restrict or deny access to the resource by the resource user.
Systems and methods for improving machine learning model predictive capabilities relative to time dependent unbalanced datasets. A separately trained (precursor) data classification machine learning model is utilized to refine training data by removing the time dependence of certain data samples in a given dataset, identify initially misidentified or misclassified data samples, and accordingly, modify a supervisory signal associated with the data used relative to subsequently training a main machine learning model. Systems and methods according to aspects of the present disclosure improve the ability of the main machine learning model to make accurate predictions with respect time dependent unbalanced datasets, while requiring less memory and processor resources for training.
Various aspects involve a monotonic recurrent neural network (MRNN) trained for risk assessment or other purposes. For instance, the MRNN is trained to compute a risk indicator from a predictor variable. Training the MRNN includes adjusting weights of nodes of the MRNN subject to a set of monotonicity constraints, wherein the set of monotonicity constraints causes output risk indicators computed by the RNN to be a monotonic function of input predictor variables. The trained monotonic RNN can be used to generate an output risk indicator for a target entity.
A telecommunications network server system provides a digital identifier to a user device. The digital identifier may include identification data corresponding to a user of the user device. In addition, the telecommunications network server system receives, from one or more third-party systems, requests to authenticate the user for an electronic transaction with the respective third-party system. The telecommunications network server system provides a unique electronic transaction code to each third-party system. Responsive to receiving from the user device one of the unique electronic transaction codes, the telecommunications network server system provides, to the respective third-party system, authentication of the user.
H04L 67/53 - Network services using third party service providers
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
A system can generate content recommendations and facilitate interactions using machine-learning. The system can receive a request from a provider entity. The system can receive entity data and interaction data associated with a target entity. The system can generate at least a first graph structure and a second graph structure. The system can generate a linked graph structure based on the first graph structure and the second graph structure. The system can determine among a plurality of operations, one or more target operations to perform on data included in the linked graph structure. The system can execute using a trained machine-learning model, the target operations to generate a content recommendation for facilitating an interaction. The system can provide a responsive message based on the content recommendation usable to facilitate the interaction.
In some aspects, a computing system can generate and optimize a hybrid machine learning model for risk assessment based on predictor variables associated with a target entity. The hybrid machine learning model can be trained using training vectors with sets of training predictor variables and training outputs corresponding to the respective sets of training predictor variables. The predictor variables associated with the target entity may include unknown values and the training predictor variables or trainings output may also include unknown values. Additionally, the computing system can generate explanatory data for the target entity to indicate relationships between changes in the risk indicator and changes in the predictor variables associated with the target entity. The risk indicator and the explanatory data can be used in controlling access of the target entity to interactive computing environments.
In some aspects, systems and methods for efficiently clustering a large-scale dataset for improving the construction and training of machine-learning models, such as neural network models, are provided. Clustering can include determining a number of clusters to be generated for the dataset. A dataset used for training a neural network model configured can be clustered into a set of clusters. The clustering can include determining the number of clusters, determining special features for the determined number of clusters, and re-clustering the dataset based on the special features. The neural network can be trained based on training samples selected from the set of clusters. In some aspects, the trained neural network model can be utilized to satisfy risk assessment queries to compute output risk indicators for target entities. The output risk indicator can be used to control access to one or more interactive computing environments by the target entities.
G06N 3/088 - Non-supervised learning, e.g. competitive learning
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
G06N 3/084 - Backpropagation, e.g. using gradient descent
40.
Techniques for prediction models using time series data
Various aspects involve a lagged prediction model trained for risk assessment or other purposes. For instance, a risk assessment computing system receives a risk assessment query for a target entity and provides an input predictor record for the target entity to a lagged prediction model. The input predictor record includes a first group of lagged values from a first time-series attribute associated with the target entity. The lagged prediction model is trained by implementing a group feature selection technique configured to select the first time-series attribute as input and to deselect a second time-series attribute associated with the target entity. The risk assessment computing system computes an output risk indicator from the input predictor record and transmits the output risk indicator to a remote computing system. The output risk indicator can be used to control access by the target entity to one or more interactive computing environments.
H04L 41/0631 - Management of faults, events, alarms or notifications using root cause analysisManagement of faults, events, alarms or notifications using analysis of correlation between notifications, alarms or events based on decision criteria, e.g. hierarchy, tree or time analysis
H04L 41/16 - Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks using machine learning or artificial intelligence
H04L 43/04 - Processing captured monitoring data, e.g. for logfile generation
H04L 43/067 - Generation of reports using time frame reporting
41.
Automated path-based recommendation for risk mitigation
Systems and methods for automated path-based recommendation for risk mitigation are provided. An entity assessment server, responsive to a request for a recommendation for modifying a current risk assessment score of an entity to a target risk assessment score, accesses an input attribute vector for the entity and clusters of entities defined by historical attribute vectors. The entity assessment server assigns the input attribute vector to a particular cluster and determines a requirement on movement from a first point to a second point in a multi-dimensional space based on the statistics computed from the particular cluster. The first point corresponds to the current risk assessment score and the second point corresponds to the target risk assessment score. The entity assessment server computes an attribute-change vector so that a path defined by the attribute-change vector complies with the requirement and generates the recommendation from the attribute-change vector.
Systems and methods for automated historical risk assessment for risk mitigation in online access control are provided. An entity assessment server can receive a request to assess a risk indicator change from a first risk indicator to a second risk indicator. For each attribute used to generate the first risk indicator and second risk indicator, a first impact can be determined for changing from the first risk indicator to a third risk indicator between the first risk indicator and the second risk indicator. A second impact similarly can be determined for changing from the third risk indicator to the second risk indicator. Aggregating the first impact and the second impact can determine a total impact of each attribute. Assessment results can be generated to include a list of attributes ordered according to the respective total impact and transmitted to a remote computing device for use in improving the risk indicator.
G06F 21/00 - Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
43.
Dual deep learning architecture for machine-learning systems
Certain aspects involve a machine-learning query system that uses a dual deep learning network to service queries and other requests. In one example, a machine-learning query system services a query received from a client computing system. A dual deep learning network included in the machine-learning query system matches an unstructured input data object, received from the client computing system, to an unstructured reference data object. The matching may include generating an input feature vector by an embedding subnetwork, based on the unstructured input data object. The matching may also include generating an output probability by a relationship subnetwork, based on the input feature vector and a relationship feature vector that is based on the unstructured reference data object. The machine-learning query system may transmit a responsive message to the client system.
G06V 10/82 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks
G06F 16/583 - Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using metadata automatically derived from the content
G06V 10/44 - Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersectionsConnectivity analysis, e.g. of connected components
G06V 10/764 - Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
G06V 40/16 - Human faces, e.g. facial parts, sketches or expressions
44.
REAL-TIME SERVICING OF VERIFICATION QUERIES USING HYBRID DATA SOURCES
In some aspects, a verification system can receive a verification query from a verifier computing system for requesting verification of characteristics of an entity involved in an online interaction. The verification query can include a unique identifier ("UID") of the entity. The verification computing system can query a verification repository in the verification computing system based on the UID. Additionally, the verification computing system can query an external-source cache using the UID. In response to determine a match for the UID in the external-source cache, the verification computing system can request external sensitive data records for the entity from an external source corresponding to theexternal-source cache. Generating consolidated sensitive data records can involveconsolidating the external sensitive data records and internal sensitive data records obtained through querying the verification repository. A verification result, generated using the consolidated sensitive data records, can be transmitted to the verifier computing system.
A durability assessment system may receive a request, from a computing system, for a durability index describing an entity. The durability assessment system may determine the durability index based on information about the resource usage by the entity, such as a resource availability score or a resource allocation score. The durability assessment system may compare the obtained resource availability score and resource allocation score to ranges associated with a set of durability indices. Based on the comparison, the durability assessment system may determine a durability index for the entity. The durability index may indicate an ability of the entity to return accessed resources. In some cases, the durability assessment system may provide the durability index to an allocation computing system that is configured to determine whether to grant access to resources based on the durability index.
H04L 47/74 - Admission controlResource allocation measures in reaction to resource unavailability
H04L 47/762 - Admission controlResource allocation using dynamic resource allocation, e.g. in-call renegotiation requested by the user or requested by the network in response to changing network conditions triggered by the network
In some aspects, a verification system can receive a verification query from a verifier computing system for requesting verification of characteristics of an entity involved in an online interaction. The verification query can include a unique identifier (“UID”) of the entity. The verification computing system can query a verification repository in the verification computing system based on the UID. Additionally, the verification computing system can query an external-source cache using the UID. In response to determine a match for the UID in the external-source cache, the verification computing system can request external sensitive data records for the entity from an external source corresponding to the external-source cache. Generating consolidated sensitive data records can involve consolidating the external sensitive data records and internal sensitive data records obtained through querying the verification repository. A verification result, generated using the consolidated sensitive data records, can be transmitted to the verifier computing system.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
A system can efficiently control access to an interactive computing environment using an entity profile. The system can receive entity data relating to a target entity. The entity data can include real-time data and external data. The system can extract features from the entity data. The system can generate signals based on the features. Each signal can include a subset of the features, and each signal can correspond to an amount of risk associated with the target entity. The system can generate, based on the signals, an entity profile. The system can provide a responsive message based on the entity profile that can be used to control access to an interactive computing environment.
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
A system can efficiently control access to an interactive computing environment using an entity profile. The system can receive entity data relating to a target entity. The entity data can include real-time data and external data. The system can extract features from the entity data. The system can generate signals based on the features. Each signal can include a subset of the features, and each signal can correspond to an amount of risk associated with the target entity. The system can generate, based on the signals, an entity profile. The system can provide a responsive message based on the entity profile that can be used to control access to an interactive computing environment.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
G06Q 10/0635 - Risk analysis of enterprise or organisation activities
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
50.
Facilitating queries of encrypted sensitive data via encrypted variant data objects
Various aspects of this disclosure provide digital data processing systems for using encrypted variant data objects to facilitate queries of sensitive data. In one example, a digital data processing system can receive sensitive data about an entity. The digital data processing system can create, in an identity data repository and from the sensitive data, a searchable secure entity data object for the entity. The searchable secure entity data object is usable for servicing a query regarding the entity. For instance, a transformed query parameter can be generated from a query parameter in the query. The query can be serviced by matching the transformed query parameter to tokenized variant data in the searchable secure entity data object and retrieving tokenized sensitive data from the searchable secure entity data object.
42 - Scientific, technological and industrial services, research and design
Goods & Services
Employment verification services Platform as a Service (PAAS) featuring non-downloadable software for use by employers, namely, providing a secure solution for facilitating completion, review, and retention of employer-related forms, employment verification, compliance with federal laws, management of tax credits, generation of reports, measurement of compliance. risk decisioning and analysis, creation of audit trails, and responding to audits; Computer services, namely, providing an interactive web site that allows users to manage Affordable Care Act compliance, employee benefits and workforce hours; Platform as a service (PAAS) featuring computer software platforms for managing Affordable Care Act compliance, tax credits, employee benefits and workforce hours, risk decisioning and analysis, creation of audit trails, and responding to audits; Providing a web hosting platform in the nature of providing a website featuring technology that enables users to manage affordable care act (ACA) compliance, employee benefits, and respond to audits
52.
FACILITATING ENTITY RESOLUTION, KEYING, AND SEARCH MATCH WITHOUT TRANSMITTING PERSONALLY IDENTIFIABLE INFORMATION IN THE CLEAR
In some aspects, an entity-resolution computing system for entity resolution is provided. The entity-resolution computing system includes an entity-resolution server configured for correlating data objects from an identity data repository that contains account or transaction data for entities based on the data objects including a common portion of the account or transaction data. The entity-resolution server updates the identity data repository to include an entity identifier that links the data objects and indicates that the data objects refer to a common entity. The entity-resolution server creates an entity-resolution data structure having the data objects with the entity identifier and a new variant data object containing a modified version of account or transaction data that match the common entity. The entity-resolution server encrypts the entity-resolution data structure and causes the encrypted entity-resolution data structure to be transmitted to a client computing system for use in augmenting client data.
A host computing system determines a wavelet transform that represents time-series values of predictor data samples. The host computing system applies the wavelet transform to the predictor data samples to generate wavelet predictor variable data comprising a first set and a second set of shift value input data for a first scale and a second scale. The host computing system computes a set of probabilities for a target event by applying a set of timing-prediction models to the first set and the second set of shift value input data. The host computing system determines an event prediction from the set of probabilities and modifies a host system operation based on the determined event prediction.
Methods described herein generate executable program code based on sequence codes, such that the executable program code is executable by a computer processor. According to such a method, a command is received to generate executable program code. Via a code-building interface, user input is received indicating an operation to be performed by the executable program code, a data type for to the executable program code, and a condition for applying the executable program code. A sequence code is constructed to represent the user input, where the sequence code includes a sequence of character sets. From the sequence code, a first character set corresponding to the operation, a second character set corresponding to the data type, and a third character set corresponding to the condition are extracted. Mapping data is applied to the first character set, the second character set, and the third character set to generate the executable program code.
Aspects and examples are disclosed for improving multi-factor authentication techniques to control access to secured electronic resources. In one example, a decisioning computer system evaluates, based on a passive-dimension decision process, an access request, received from a user device, for a secured electronic resource. The passive-dimension decision process can evaluate dimensions associated with the access request, such as identity or device characteristics, to determine whether the dimensions of the access request are outside of norms for the user. Based on the passive-dimension decision model, the decisioning computing device may communicate to the user device an access decision, the access decision describing one or more of an access authorization, a denial of access, or a supplemental authentication challenge.
In some aspects, a content-extraction system can receive a query from a client device and generate a result set of digital content responsive to the query. For instance, the content-extraction system can obtain, from a search system, a set of digital content matching one or more keywords. The content-extraction system can exclude digital content items lacking core content, digital content items with duplicative content, or both. In some aspects, the content-extraction system can determine, for one or more remaining digital content items, a content attribute score. The content-extraction system can select, as the result set of digital content, a subset of digital content based on the content attribute scores. The content-extraction system can output the result set to the client device.
Certain embodiments involve providing explainable risk assessment via multi-stage machine-learning techniques. A risk assessment server can determine, in response to a risk assessment query for a target entity, a first risk indicator for the target entity by applying a first risk assessment model to predictor variables associated with the target entity. Responsive to determining that the first risk indicator indicates a risk higher than a threshold value, the risk assessment server can generate explanatory data for the predictor variables and determine a second risk indicator for the target entity by applying a second risk assessment model to the predictor variables associated with the target entity. A response message can be generated and transmitted to include the first risk indicator, the explanatory data, and the second risk indicator, for use in controlling access to one or more interactive computing environments by the target entity.
Certain aspects involve updating data structures to indicate relationships between attribute trends and response variables used for training automated modeling systems. For example, a data structure stores data for training an automated modeling algorithm. The training data includes attribute values for multiple entities over a time period. A computing system generates, for each entity, at least one trend attribute that is a function of a respective time series of attribute values. The computing system modifies the data structure to include the generated trend attributes and updates the training data to include trend attribute values for the trend attributes. The computing system trains the automated modeling algorithm with the trend attribute values from the data structure. In some aspects, trend attributes are generated by applying a frequency transform to a time series of attribute values and selecting, as trend attributes, some of the coefficients generated by the frequency transform.
A system can efficiently control access to an interactive computing environment. The system can receive authentication data of an authentication attempt associated with an entity. The system can determine, for the entity, a historical vector including features that include sub-features. The historical vector can be determined by generating synthetic data, generating weights, and determining probabilities. The synthetic data can be based on historical authentication attempts by entities other than the entity. The weights can correspond to sub-features of the historical vector. The probabilities can indicate a likelihood that a corresponding sub-feature is involved in the authentication attempt. The system can compare the historical vector to the authentication data. The system can generate a responsive message based on the comparison for controlling access to the interactive computing environment.
A system can efficiently control access to an interactive computing environment. The system can receive authentication data of an authentication attempt associated with an entity. The system can determine, for the entity, a historical vector including features that include sub-features. The historical vector can be determined by generating synthetic data, generating weights, and determining probabilities. The synthetic data can be based on historical authentication attempts by entities other than the entity. The weights can correspond to sub-features of the historical vector. The probabilities can indicate a likelihood that a corresponding sub-feature is involved in the authentication attempt. The system can compare the historical vector to the authentication data. The system can generate a responsive message based on the comparison for controlling access to the interactive computing environment.
A system can efficiently control access to an interactive computing environment. The system can receive authentication data of an authentication attempt associated with an entity. The system can determine, for the entity, a historical vector including features that include sub-features. The historical vector can be determined by generating synthetic data, generating weights, and determining probabilities. The synthetic data can be based on historical authentication attempts by entities other than the entity. The weights can correspond to sub-features of the historical vector. The probabilities can indicate a likelihood that a corresponding sub-feature is involved in the authentication attempt. The system can compare the historical vector to the authentication data. The system can generate a responsive message based on the comparison for controlling access to the interactive computing environment.
In some aspects, a computing system can generate and optimize a neural network for risk assessment. Input predictor variables can be analyzed to identify common factors of these predictor variables. The neural network can be trained to enforce a monotonic relationship between each common factor of the input predictor variables and an output risk indicator. The training of the neural network can involve solving an optimization problem under this monotonic constraint. The optimized neural network can be used both for accurately determining risk indicators for target entities using predictor variables and determining explanation codes for the predictor variables. Further, the risk indicators can be utilized to control the access by a target entity to an interactive computing environment for accessing services provided by one or more institutions.
In some aspects, a gateway server can unlock or unfreeze access to data about a user by third parties without requiring the user to navigate completely away from a third-party website through which the user is executing an electronic transaction. The gateway server can receive a request to unlock or unfreeze data through the third-party website hosted by a third-party web server. The gateway server can output a user interface that is displayable simultaneously with the third-party website. Through the user interface, the gateway server can receive sign-in data such as log-in credentials of the user and consent to share data about the user with the third-party web server. The gateway server can output a command to unlock or unfreeze data about the user and to share the data with the third-party web server. Based on the shared data, the transaction can be completed at the third-party web server.
The present disclosure involves systems and methods for identity authentication across multiple institutions using a trusted mobile device as a proxy for a user login. In one example, the operations include identifying a request to trust a particular user associated with a first entity in a digital ID network. A set of personally identifiable information (PII) associated with the user is obtained via the first entity and an identity verification (IDV)/fraud risk analysis is performed. In response to satisfying the analysis, instructions are transmitted to the user to verify the identity via a mobile trust application on an associated mobile device. Upon verification, the mobile device is bound to the user within the digital ID network along with a digital ID associated with the particular user. The digital ID can be used by other entities registered within the digital ID network to authenticate the user.
H04W 12/30 - Security of mobile devicesSecurity of mobile applications
H04W 12/67 - Risk-dependent, e.g. selecting a security level depending on risk profiles
G06K 7/10 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation
G06K 7/14 - Methods or arrangements for sensing record carriers by electromagnetic radiation, e.g. optical sensingMethods or arrangements for sensing record carriers by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
G06Q 20/32 - Payment architectures, schemes or protocols characterised by the use of specific devices using wireless devices
G06Q 20/40 - Authorisation, e.g. identification of payer or payee, verification of customer or shop credentialsReview and approval of payers, e.g. check of credit lines or negative lists
In some aspects, a computing system is configured to use graph-based techniques to detect synthetic identities. The computing system can generate a collection of graphs based on account data and transaction data for online entities. The collection of graphs includes multiple graph communities, each graph community including nodes and edges. Each node represents a user and an edge between a first node and a second node indicates a user represented by the second node is an authorized user of the user represented by the first node. The computing system can identify a clique graph community in the collection of graphs and compare the identified clique graph community with a known clique graph community that includes synthetic identities. The computing system can determine nodes in the identified clique graph community to be synthetic identities based on determining that the identified clique graph community is equivalent to the known clique graph community.
A telecommunications network server system provides a digital identifier to a user device. The digital identifier may include identification data corresponding to a user of the user device. In addition, the telecommunications network server system receives, from one or more third-party systems, requests to authenticate the user for an electronic transaction with the respective third-party system. The telecommunications network server system provides a unique electronic transaction code to each third-party system. Responsive to receiving from the user device one of the unique electronic transaction codes, the telecommunications network server system provides, to the respective third-party system, authentication of the user.
H04L 67/53 - Network services using third party service providers
H04L 9/32 - Arrangements for secret or secure communicationsNetwork security protocols including means for verifying the identity or authority of a user of the system
An auditing system executes a first machine learning model on a first computing platform using input data to generate first output data. The auditing system executes a second machine learning model on a second computing platform using the input data to generate second output data. The second machine learning model is generated by migrating the first machine learning model to the second computing platform. The auditing system determines one or more performance metrics based on comparing the first output data to the second output data. The auditing system classifies, based on the one or more performance metrics, the second machine learning model with a classification. The classification comprises a passing classification or a failing classification. The auditing system causes the second model to be modified responsive to classifying the second model with a failing classification.
In some aspects, a computing system can determine a set of attributes based on analyzing input data using attribute templates written in a production-ready programming language. The computing system can generate attribute definitions for the set of attributes using the attribute templates and deploy the attribute definitions for the set of attributes to a production environment of a software program. The software program is written in a programming language compatible with the production-ready programming language. The computing system can monitor the performance of the set of attributes in the production environment of the software program and cause the attribute definitions of the plurality of attributes to be modified based on the monitoring.
G16H 10/20 - ICT specially adapted for the handling or processing of patient-related medical or healthcare data for electronic clinical trials or questionnaires
69.
MACHINE-LEARNING TECHNIQUES FOR RISK ASSESSMENT BASED ON MULTIPLE DATA SOURCES
Systems and methods for using machine-learning techniques to provide risk assessment based on multiple sources of data are described herein. Data about an entity can be received, and the data can be authenticated. Integrated risk data about the entity can be received. The integrated risk data can include traditional risk assessment data and nontraditional risk assessment data. An integrated risk assessment value can be determined based on the integrated risk data by aligning a first output from a first risk assessment model and a second output by a second risk assessment model. A responsive message including at least the integrated risk assessment value and associated information for the entity can be transmitted to a remote computing device for use in controlling access of the entity to one or more interactive computing environments.
G06F 21/57 - Certifying or maintaining trusted computer platforms, e.g. secure boots or power-downs, version controls, system software checks, secure updates or assessing vulnerabilities
70.
UNIFIED EXPLAINABLE MACHINE LEARNING FOR SEGMENTED RISK ASSESSMENT
Various aspects involve unified explainable machine learning for segmented risk assessment. For example, a computing device can determine, using a unified model built from segment models, a risk indicator for a target entity from predictor variables associated with the target entity. The target entity belongs to one of a plurality of entity segments each associated with a segment model of the segment models. The unified model is generated by: accessing training samples for the entity segments; training the segment models using respective training samples for the entity segments; constructing the unified risk prediction model by stacking the trained segment models; and training the unified risk prediction model using the training samples for the entity segments. The computing device can transmit, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to an interactive computing environment.
Various aspects involve unified explainable machine learning for segmented risk assessment. For example, a computing device can determine, using a unified model built from segment models, a risk indicator for a target entity from predictor variables associated with the target entity. The target entity belongs to one of a plurality of entity segments each associated with a segment model of the segment models. The unified model is generated by: accessing training samples for the entity segments; training the segment models using respective training samples for the entity segments; constructing the unified risk prediction model by stacking the trained segment models; and training the unified risk prediction model using the training samples for the entity segments. The computing device can transmit, to a remote computing device, a responsive message including at least the risk indicator for use in controlling access of the target entity to an interactive computing environment.
Systems and methods for consolidating data sources for expediting validation processes are described herein. A user interface can be provided to a first entity. First risk assessment data associated with the first entity can be received via the user interface. Second risk assessment data associated with the first entity can be received. The first risk assessment data and the second risk assessment data can be validated. The first risk assessment data and the second risk assessment data can be output for display on the user interface. A responsive message including the validated first risk assessment data and the validated second risk assessment data can be transmitted to a remote computing device for use in controlling access of the first entity to one or more interactive computing environments.
A system receives, from a remote computing device, a query for a timing of an adverse event associated with a target entity. The system determines, using a timing prediction model trained using a training process, the timing of the adverse event for the target entity from predictor variables associated with the target entity. The training process includes accessing an observational journal comprising historical panel data of the target entity including values of predictor variables for one or more time points and generating, from historical panel data, an augmented time series by augmenting the historical panel data with values of predictor variables for at time points for which the historical panel data does not include values of predictor variables. The system transmits to the remote computing device, a responsive message including at least the timing of the adverse event for use in controlling access of the target entity to one or more interactive computing environments.
A method can be used to predict risk and provide explainable outcomes using machine learning based on wavelet analysis. A risk prediction model can be applied to time-series data for an attribute associated with a target entity to generate a risk indicator for the target entity. The risk prediction model can include a feature learning model and a risk classification model configured to generate the risk indicator as output. Parameters of the feature learning model can be accessed and a plurality of basis functions of a wavelet transformation can be applied on the parameters of the feature learning model to generate a set of parameter wavelet coefficients. Explanatory data can be generated for the risk indicator based on the set of parameter wavelet coefficients. A responsive message can be transmitted to a remote computing device including the risk indicator and the explanatory data for use in controlling access of the target entity to an interactive computing environment.
A method can be used to predict risk and provide explainable outcomes using machine learning based on wavelet analysis. A risk prediction model can be applied to time-series data for an attribute associated with a target entity to generate a risk indicator for the target entity. The risk prediction model can include a feature learning model and a risk classification model configured to generate the risk indicator as output. Parameters of the feature learning model can be accessed and a plurality of basis functions of a wavelet transformation can be applied on the parameters of the feature learning model to generate a set of parameter wavelet coefficients. Explanatory data can be generated for the risk indicator based on the set of parameter wavelet coefficients. A responsive message can be transmitted to a remote computing device including the risk indicator and the explanatory data for use in controlling access of the target entity to an interactive computing environment.
Techniques for validating large amounts of sensitive data migrated across multiple platforms without revealing the content of the sensitive data are provided. For example, a processing device can transform data in a first data file stored on a first platform to common data formats. The processing device can generate a first set of hash values. The processing device can receive a second set of hash values for a second data file stored on a second platform. The processing device can compare the first set of hash values and the second set of hash values and cause the first data file or the second data file to be modified based on a difference between the sets of hash values.
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
77.
EXPLAINABLE MACHINE LEARNING BASED ON TIME-SERIES TRANSFORMATION
Various aspects involve explainable machine learning based on time-series transformation. For instance, a computing system accesses time-series data of a predictor variable associated with a target entity. The computing system generates a first set of transformed time-series data instances by applying a first family of transformations on the time-series data. Any non-negative linear combination of the first family of transformations forms an interpretable transformation of the time-series data. The computing system determines a risk indicator for the target entity indicating a level of risk associated with the target entity by inputting the first set of transformed time-series data instances into a machine learning model. The computing system transmits, to a remote computing device, a responsive message including the risk indicator. The risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.
Various aspects involve explainable machine learning based on time-series transformation. For instance, a computing system accesses time-series data of a predictor variable associated with a target entity. The computing system generates a first set of transformed time-series data instances by applying a first family of transformations on the time-series data. Any non-negative linear combination of the first family of transformations forms an interpretable transformation of the time-series data. The computing system determines a risk indicator for the target entity indicating a level of risk associated with the target entity by inputting the first set of transformed time-series data instances into a machine learning model. The computing system transmits, to a remote computing device, a responsive message including the risk indicator. The risk indicator is usable for controlling access to one or more interactive computing environments by the target entity.
Techniques for validating large amounts of sensitive data migrated across multiple platforms without revealing the content of the sensitive data are provided. For example, a processing device can transform data in a first data file stored on a first platform to common data formats. The processing device can generate a first set of hash values. The processing device can receive a second set of hash values for a second data file stored on a second platform. The processing device can compare the first set of hash values and the second set of hash values and cause the first data file or the second data file to be modified based on a difference between the sets of hash values.
G06F 21/62 - Protecting access to data via a platform, e.g. using keys or access control rules
G06F 16/27 - Replication, distribution or synchronisation of data between databases or within a distributed database systemDistributed database system architectures therefor
G06F 21/64 - Protecting data integrity, e.g. using checksums, certificates or signatures
80.
Data validation techniques for sensitive data migration across multiple platforms
Techniques for validating large amounts of sensitive data migrated across multiple platforms without revealing the content of the sensitive data are provided. For example, a processing device can transform data in a first data file stored on a first platform to common data formats. The processing device can generate a first set of hash values. The processing device can receive a second set of hash values for a second data file stored on a second platform. The processing device can compare the first set of hash values and the second set of hash values and cause the first data file or the second data file to be modified based on a difference between the sets of hash values.
H04L 9/06 - Arrangements for secret or secure communicationsNetwork security protocols the encryption apparatus using shift registers or memories for blockwise coding, e.g. D.E.S. systems
H04L 67/06 - Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
H04L 67/1097 - Protocols in which an application is distributed across nodes in the network for distributed storage of data in networks, e.g. transport arrangements for network file system [NFS], storage area networks [SAN] or network attached storage [NAS]
81.
MACHINE-LEARNING TECHNIQUES FOR RISK ASSESSMENT BASED ON CLUSTERING
Systems and methods for predicting future risk for a target entity are provided. A risk assessment system receives historical risk assessment data of the target entity and identifies a target cluster that matches the historical risk assessment data. The target cluster is identified from a group of clusters determined using high dimensional clustering based on risk assessment data of a set of entities. The risk assessment system identifies a set of nearest neighbors of the target cluster and determines a prediction of future risk for the target entity based on the target cluster and the set of nearest neighbors. The risk assessment system transmits a responsive message, which can include the prediction of future risk, to a remote computing device for use in controlling access of the target entity to one or more interactive computing environments.
Bayesian modeling can be used for risk assessment. For example, a computing device determines, using a Bayesian prediction model, a risk indicator for a target entity from predictor variables associated with the target entity. The Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model. The Bayesian prediction model is generated based on an initial training dataset. The initial training dataset includes training records and predictor variables. The Bayesian prediction model can be generated by calculating the set of parameters based on the initial training dataset. The Bayesian prediction model can be updated by updating the set of parameters using an additional training dataset. The computing device transmits, to a remote computing device, the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.
Bayesian modeling can be used for risk assessment. For example, a computing device determines, using a Bayesian prediction model, a risk indicator for a target entity from predictor variables associated with the target entity. The Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model. The Bayesian prediction model is generated based on an initial training dataset. The initial training dataset includes training records and predictor variables. The Bayesian prediction model can be generated by calculating the set of parameters based on the initial training dataset. The Bayesian prediction model can be updated by updating the set of parameters using an additional training dataset. The computing device transmits, to a remote computing device, the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.
Bayesian modeling can be used for risk assessment. For example, a computing device determines, using a Bayesian prediction model, a risk indicator for a target entity from predictor variables associated with the target entity. The Bayesian prediction model determines the risk indicator based on a set of parameters associated with the Bayesian prediction model. The Bayesian prediction model is generated based on an initial training dataset. The initial training dataset includes training records and predictor variables. The Bayesian prediction model can be generated by calculating the set of parameters based on the initial training dataset. The Bayesian prediction model can be updated by updating the set of parameters using an additional training dataset. The computing device transmits, to a remote computing device, the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.
Certain aspects involve providing automated performance monitoring of statistical models. For example, a processing device is used for performing a statistical analysis on information in an archive to extract historical data, scores, and attributes. The processing device calculates performance metrics based at least in part on the historical data, scores, and attributes. The processing device pre-calculates summary performance data based at least in part on the performance metrics. The summary performance data is stored in files with predefined layouts, which are stored in a non-transitory, computer-readable medium. Segmented data is presented from a file to a user through a graphical user interface (GUI). In some aspects, various reports of the segmented data are presented interactively by detecting a selection by the user of a segmentation and displaying the corresponding segmented data.
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record- matching computing system is configured to search for a data record that matches a query record. The record-matching computing system retrieves a reference record from data records and generates multiple identifier attributes for the query record and reference record, including identifier scores and compound scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and reference record. A compound score is generated by combining two or more identifier scores. The record-matching computing system applies the identifier attributes to a machine learning model configured to predict a match classification based on input identifier attributes for a pair of data records. The record-matching server can identify the reference records as a match to the query record based on the match classification indicating a match.
In some aspects, a record-matching computing system for detecting fragmented records is provided. The record-matching system is configured to identify a list of candidate records for merging from a set of data records. The record-matching system determines a matching decision for each pair of candidate records in the list and generates a graph. The graph includes nodes representing respective candidate records and edges connecting the nodes. Each edge represents a match between a pair of nodes connected by the edge according to the matching decisions. The record-matching system detects a connected component in the graph from which a qualified connected component is identified based on the minimum connectivity of the qualified connected component. The record-matching system updates the set of data records stored by merging candidate records represented by the nodes in the qualified connected component.
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to receiving a query record and search in a data repository storing data records for a record that matches the query record. The record-matching computing system retrieves a reference record from the data records and generates multiple identifier scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and the reference record. The record-matching computing system generates an overall matching score by combining at least two of the identifier scores and determines the reference record as a match to the query record based on the overall matching score exceeding a threshold value.
In some aspects, a computing system can improve a machine learning model for risk assessment by removing or reducing bias in the machine learning model. The training process for the machine learning model can include training the machine learning model using training samples, obtaining data for a protected attribute, and calculating a bias metric using the data for the protected attribute and data obtained from the trained machine learning model. Based on the bias metric, bias associated with the machine learning model can be detected. The machine learning model can be modified based on the detected bias and re-trained. The re-trained machine learning model can be used to predict a risk indicator for a target entity. The predicted risk indicator can be transmitted to a remote computing device and be used for controlling access of the target entity to one or more interactive computing environments.
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record- matching computing system is configured to search for a data record that matches a query record. The record-matching computing system retrieves a reference record from data records and generates multiple identifier attributes for the query record and reference record, including identifier scores and compound scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and reference record. A compound score is generated by combining two or more identifier scores. The record-matching computing system applies the identifier attributes to a machine learning model configured to predict a match classification based on input identifier attributes for a pair of data records. The record-matching server can identify the reference records as a match to the query record based on the match classification indicating a match.
In some aspects, a record-matching computing system for detecting fragmented records is provided. The record-matching system is configured to identify a list of candidate records for merging from a set of data records. The record-matching system determines a matching decision for each pair of candidate records in the list and generates a graph. The graph includes nodes representing respective candidate records and edges connecting the nodes. Each edge represents a match between a pair of nodes connected by the edge according to the matching decisions. The record-matching system detects a connected component in the graph from which a qualified connected component is identified based on the minimum connectivity of the qualified connected component. The record-matching system updates the set of data records stored by merging candidate records represented by the nodes in the qualified connected component.
In some aspects, a record-matching computing system for matching records to facilitate database search and fragmented records detection is provided. The record-matching computing system is configured to receiving a query record and search in a data repository storing data records for a record that matches the query record. The record-matching computing system retrieves a reference record from the data records and generates multiple identifier scores. Each identifier score measures a degree of matching between the corresponding identifiers in the query record and the reference record. The record-matching computing system generates an overall matching score by combining at least two of the identifier scores and determines the reference record as a match to the query record based on the overall matching score exceeding a threshold value.
In some aspects, a computing system can improve a machine learning model for risk assessment by removing or reducing bias in the machine learning model. The training process for the machine learning model can include training the machine learning model using training samples, obtaining data for a protected attribute, and calculating a bias metric using the data for the protected attribute and data obtained from the trained machine learning model. Based on the bias metric, bias associated with the machine learning model can be detected. The machine learning model can be modified based on the detected bias and re-trained. The re-trained machine learning model can be used to predict a risk indicator for a target entity. The predicted risk indicator can be transmitted to a remote computing device and be used for controlling access of the target entity to one or more interactive computing environments.
A power graph convolutional network (PGCN) can be used for explainable machine learning. For example, a computing device can determine, using a PGCN, a risk indicator for a target entity from predictor variables associated with the target entity. The PGCN includes a convolutional layer configured to generate modified predictor variables by multiplying each of the predictor variables with a respective interaction factor generated based on an adjacency weight matrix and the predictor variables. The PGCN also includes a dense layer configured to generate the risk indicator. The training process involves adjusting a set of weights in the adjacency weight matrix and a set of weights in a weight vector of the dense layer based on a loss function of the PGCN. The computing device transmits a responsive message including the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.
A power graph convolutional network (PGCN) can be used for explainable machine learning. For example, a computing device can determine, using a PGCN, a risk indicator for a target entity from predictor variables associated with the target entity. The PGCN includes a convolutional layer configured to generate modified predictor variables by multiplying each of the predictor variables with a respective interaction factor generated based on an adjacency weight matrix and the predictor variables. The PGCN also includes a dense layer configured to generate the risk indicator. The training process involves adjusting a set of weights in the adjacency weight matrix and a set of weights in a weight vector of the dense layer based on a loss function of the PGCN. The computing device transmits a responsive message including the risk indicator for use in controlling access of the target entity to one or more interactive computing environments.
In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.
Certain aspects involve an automated recommendation for risk mitigation. An entity assessment server, responsive to a request for a recommendation for achieving a target status of a risk indicator, accesses a set of input attributes for the entity and obtains a quantity of available resource useable for modifying at least resource-dependent attribute values of the entity. The entity assessment server generates a resource allocation plan for the available resource according to a first risk assessment model and updates the set of input attribute values based on the resource allocation plan. The entity assessment server further determines an updated value of the risk indicator for the entity based on the updated set of input attribute values according to a second risk assessment model and generates the recommendation to include the resource allocation plan of the available resource if the updated value of the risk indicator achieves the target status.
In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.
In some aspects, techniques for creating representative and informative training datasets for the training of machine-learning models are provided. For example, a risk assessment system can receive a risk assessment query for a target entity. The risk assessment system can compute an output risk indicator for the target entity by applying a machine learning model to values of informative attributes associated with the target entity. The machine learning model may be trained using training samples selected from a representative and informative (RAI) dataset. The RAI dataset can be created by determining the informative attributes based on attributes used by a set of models and further extracting representative data records from an initial training dataset based on the determined informative attributes. The risk assessment system can transmit a responsive message including the output risk indicator for use in controlling access of the target entity to an interactive computing environment.
Certain aspects involve evaluating modeling algorithms whose outputs can impact machine-implemented operating environments. For instance, a computing system generates, from a comparison of a set of estimated attribute values of an attribute to a set of validation attribute values of the attribute, a discretized evaluation dataset with data values in multiple categories. The computing system computes, for a modeling algorithm used to generate the estimated attribute values, an evaluation metric. The computing system provides a host computing system with access to the evaluation metric, one or more modeling outputs generated with the modeling algorithm, or both. Providing one or more of these outputs to the host computing system can facilitate modifying one or more machine-implemented operations.
G06V 10/75 - Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video featuresCoarse-fine approaches, e.g. multi-scale approachesImage or video pattern matchingProximity measures in feature spaces using context analysisSelection of dictionaries
