Abstract: Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Abstract: Parallelized computation by a real-time transaction scoring system that incorporates global behavior profiling of transacting entities includes dividing a global profile computing component of a transaction scoring model of a real-time behavioral analytics transaction scoring system into a plurality of global profile component instances. The transaction scoring model uses a plurality of global profile variables, each of the plurality of global profile component instances using its own global profile partition that contains the estimate of global profile variables and being configured for update by a dedicated thread of execution of the real-time transaction scoring system, each dedicated thread being configured for receiving and scoring a portion of input transactions.

Abstract: Systems and methods for efficient association of related entities. The method may comprise accessing a database of records, using a processor, to identify a set of unoptimized entities represented by one or more nodes in a graph model, a connection between a first node and a second node in the one or more nodes representing an association between a first entity represented by the first node and a second entity represented by the second node; determining the first entity is unoptimized; and determining a set of related entities for the unoptimized first entity in the graph model, the graph model having at least one common entity with a corresponding label model.

Abstract: A model governance framework uses a shared ledger on the back of a blockchain. The solution tracks various analytic tracking documents (ATDs) and associated assets, like requirements and sprints, through various stages of an ATD lifecycle. Data schema and data distributions are also tracked. The decision models, corresponding variables and execution codes are also tracked. Existing variables and execution codes are made available via a preexisting asset ATD for reuse. Various transactions provide mechanism for accessing and manipulating the various assets through a recorded ledger of events and approvals. A system provides tracking of the approvals and the approvers of all model assets that are touched by any participant, and further provides access control and security for multi-user access. An application layer provides graphical access to the various aspects of the blockchain.

Abstract: Computer-implemented machines, systems and methods for managing missing values in a dataset for a machine learning model. The method may comprise importing a dataset with missing values; computing data statistics and identifying the missing values; verifying the missing values; updating the missing values; imputing missing values; encoding reasons for why values are missing; combining imputed missing values and the encoded reasons; and recommending models and hyperparameters to handle special or missing values.

Abstract: Computer-implemented machines, systems and methods for providing insights about a machine learning model, the machine learning model trained, during a training phase, to learn patterns to correctly classify input data associated with risk analysis. Analyzing one or more features of the machine learning model, the one or more features being defined based on one or more constraints associated with one or more values and relationships and whether said one or more values and relationships satisfy at least one of the one or more constraints. Displaying one or more visual indicators based on an analysis of the one or more features and training data used to train the machine learning model, the one or more visual indicators providing a summary of the machine learning model's performance or efficacy.

Abstract: Systems and methods for utilizing an image capture device to scan facial features of a user, responsive to recognition of a plurality of beam projection points on the face of the user. The first data captured from scanning the facial features may be authenticated against a facial depth map stored as a data structure in a data storage medium. In response to successful authentication, the facial features of the user may be continually scanned to detect facial movements indicative of the user's liveness. Access may be granted to the user, in response to verifying the user's liveness.

Abstract: In one aspect there is provided a method. The method may include collecting one or more functions that implement the decision logic of a solution. A snapshot of the one or more functions can be generated. The snapshot can executable code associated with the one or more functions. The solution can be deployed by at least storing the snapshot of the one or more functions to a repository Systems and articles of manufacture, including computer program products, are also provided.

Abstract: In one aspect, a computer implemented method for translating and executing rules using a directed acyclic graph is provided. The method includes transforming a ruleset into a directed acyclic graph. The directed acyclic graph includes a plurality of nodes and a plurality of branches. The method further includes identifying similarities across the plurality of branches. The method further includes grouping branches of the directed acyclic graph based on the identified similarities. The method further includes creating a modified directed acyclic graph based on the grouping. The method further includes selecting and using a method of processing a group of the modified directed acyclic graph based on an aspect of the group.

Abstract: A system and method for analyzing coverage, bias and model explanations in large dimensional modeling data includes discretizing three or more variables of a dataset to generate a discretized phase space represented as a grid of a plurality of cells, the dataset comprising a plurality of records, each record of the plurality of records having a value and a unique identifier (ID). A grid transformation is applied to each record in the dataset to assign each record to a cell of the plurality of cells of the grid according to the grid transformation. A grid index is generated to reference each cell using a discretized feature vector. A grid storage for storing the records assigned to each cell of the grid is then created. The grid storage using the ID of each record as a reference to each record and the discretized feature vector as a key to each cell.

Abstract: A system and method for scalable, adaptive, real-time generation of personalized offers is disclosed. A profile of a user is generated, the profile being a summarized representation of historical behavior of the user, the profile containing recursively updated variables. The profile is updated for each new transaction and/or a time dependent event, the new transaction and/or time dependent event including purchase transaction data, user, item hierarchy, and offer data. A affinity scores is generated for the user based on the updated profile, and for each new transaction, one or more offers are generated for the user based on the updated user profile and the affinity scores.

Abstract: Computer-implemented systems and methods for selecting a first neural network model from a set of neural network models for a first dataset, the first neural network model having a set of predictor variables and a second dataset comprising a plurality of datapoints mapped into a multi-dimensional grid that defines one or more neighborhood data regions; applying the first neural network model on the first dataset to generate a model score for one or more datapoints in the second dataset, the model score representing an optimal fit of input predictor variables to a target variable for the set of variables of the first neural network model.

Abstract: In one aspect, a computer implemented method for efficient value lookup in a set of scalar intervals is provided. The method includes determining, in response to a query for a scalar value, that the scalar value is located in a set of scalar intervals, wherein each of the scalar intervals comprises a left bound and a right bound. The method further includes sorting the scalar intervals based on left bounds. The method further includes comparing, in response to the sorting, a pair of scalar intervals to determine if the pair of scalar intervals overlaps. The method further includes identifying, based on the comparing indicating that the pair overlaps, a method of processing the scalar intervals.

Abstract: Computer-implemented methods, systems and products for character string frequency analysis. The method includes a set of operations or steps, including parsing a plurality of character strings into one or more tokens, categorizing the one or more tokens into one or more token frequency categories, and generating a first similarity score between one or more pairs of character strings of the plurality of character strings. The method further includes calculating one or more degrees of commonality or rarity of the plurality of character strings based on the categorizing, generating one or more penalties for token pairs of the one or more pairs of character strings associated with the first similarity score based on the one or more degrees of commonality or rarity and the categorizing, and generating a second similarity score based the first similarity score and the one or more penalties.

Abstract: An automated system for detecting risky entity behavior using an efficient frequent behavior-sorted list is disclosed. From these lists, fingerprints and distance measures can be constructed to enable comparison to known risky entities. The lists also facilitate efficient linking of entities to each other, such that risk information propagates through entity associations. These behavior sorted lists, in combination with other profiling techniques, which efficiently summarize information about the entity within a data store, can be used to create threat scores. These threat scores may be applied within the context of anti-money laundering (AML) and retail banking fraud detection systems. A particular instantiation of these scores elaborated here is the AML Threat Score, which is trained to identify behavior for a banking customer that is suspicious and indicates high likelihood of money laundering activity.

Abstract: A configuration package receives user-generated input that configures a decision service to generate decision data. The configuration package includes artifacts and the user-generated input selects the artifacts from an artifact library in a configuration database. A configured decision service is generated, where the generating includes receiving, by a decision service factory, the configuration package. Also, the decision service factory receives a decision template including configurable decision elements and non-configurable decision elements. Further, the decision service factory receives a user configuration specifying a parameter in the corresponding artifact. The artifact from the configuration package, the user configuration and the decision template are combined to generate the configured decision service. The configured decision service receives, from a client computer, input for each of the configurable decision elements.

Abstract: A computer-implemented method and system for visualizing card transaction fraud analysis is presented. Transaction data and account data related to one or more payment card accounts is stored in a database. The transaction data includes a fraud score. A computer processor generates one or more of a plurality of visualizations of activity of at least one suspicious account from the one or more payment card accounts for display in a graphical user interface, each of the plurality of visualizations providing at least a graphical representation of the transaction data and which is selectable from a menu provided by the computer processor in the graphical user interface. The visualizations assist in case judgment of the one or more payment cards.

Abstract: In transactional systems where past transactions can have impact on the current score of a machine learning based decision model, the transactions that are most responsible for the score and the associated reasons are determined by the transactional system. A system and method identifies such past transactions that maximally impact the current score and allow for a more effective understanding of the scores generated by a model in a transactional system and explanation of specific transactions for automated decisioning, to explain the scores in terms of past transactions. Further an existing instance-based explanation system is used to identify the reasons for the score, and how the identified transactions influence these reasons. A combination of impact on score and impact on reasons determines the most impactful past transaction with respect to the most recent score being explained.

Abstract: The subject matter disclosed herein provides methods, apparatus, systems, techniques, and articles for false positive reduction in abnormality detection models. A date and time of a first transaction by a transaction entity and associated with a transaction characteristic can be stored. Data representing subsequent transactions associated with the transaction characteristic can be stored. A history marker profile specific to the transaction characteristic and transaction entity can be generated and can include the transaction characteristic, the date and time of the first transaction, and maximum and mean abnormality scores. A date and time of a current transaction can be determined. A current abnormality score for the current transaction can be received. A tenure of the observed transaction characteristic can be computed.

Abstract: A system and method are disclosed, to distinguish fraudulent transactions from a legitimate transaction, predicated on the notion that the card is considered likely to be in state of fraud. The disclosed system and method can be activated as soon as an account has suspicious activity that causes a high score for potential fraud, but before a bank either can or needs to confirm fraud. The system or method is able to pinpoint the actual fraudulent transactions inside a window of potential fraudulent activity, using a specialized model referred to as the pinpoint model.