Abstract: A method, computer system, and a computer program product for labeling optimization is provided. The present invention may include receiving a plurality of labeled historical transaction timeline image clusters based on a plurality of historical transaction timeline images clustered using unsupervised machine learning. The present invention may further include training an image recognition model using supervised machine learning based on the received plurality of labeled historical transaction timeline image clusters. The present invention may also include receiving, by the trained image recognition model, a current transaction timeline image. The present invention may further include assigning a corresponding label to the received current transaction timeline image based on matching the received current transaction timeline image to one of the received plurality of labeled historical transaction timeline image clusters.

Abstract: Embodiments can provide a computer implemented method for simulating customer data using a reinforcement learning model, including: generating an artificial customer profile by combining randomly selected information from a set of real customer profile data; providing standard customer transaction data representing a group of real customers having similar transaction characteristics as a goal; performing a plurality of iterations to simulate the standard customer transaction data; and combining the artificial customer profile with the simulated customer transaction data to form simulated customer data. In each iteration, the method includes conducting an action including a plurality of simulated transactions; comparing the action with the goal; providing a feedback associated with the action based on a degree of similarity relative to the goal; adjusting a policy based on the feedback.

Abstract: Computer vision and deep learning techniques are leveraged to detect behavior patterns in transaction histories. A transaction timeline is built for a series of transactions, e.g., financial, and a graphic image is constructed representing the transaction timeline. The graphic image is then matched to a known behavior pattern using a cognitive system. The cognitive system is trained with historical timeline images having associated labels. In one example the graphic image is a bar chart and each financial transaction is represented as a bar in the bar chart having a height proportional to a transaction amount, the bar being located along a time axis of the bar chart according to the transaction date and being color coded according to the transaction type.

Abstract: Embodiments can provide a computer implemented method for simulating transaction data using a reinforcement learning model, the method including: generating an artificial customer profile by combining randomly selected information from a set of real customer profile data; providing standard customer transaction data representing a group of real customers having similar transaction characteristics as a goal; conducting, by the intelligent agent, an action including a plurality of simulated transactions; comparing, by the environment, the action with the goal; providing, by the environment, a feedback associated with the action based on a degree of similarity relative to the goal; adjusting, by the policy engine, a policy based on the feedback; the step of conducting an action to the step of adjusting a policy are repeated until the degree of similarity is higher than a first predefined threshold; and combing the artificial customer profile with the action to form simulated customer data.

Abstract: A method, system, and computer programming product for checking that clusters representative of transactional activity of a group of persons exhibits sufficient variability including: receiving transactional data; forming clusters from the received transactional data representing groups of persons that behave similarly; determining that a cluster representing a group of persons that behave similarly is not sufficiently variable; and increasing, in response to the cluster representing the group of persons behaving similarly not being sufficiently variable, the variability of the cluster. Further including, in an embodiment, creating a superset cluster consisting of both the cluster and the parent of the cluster; creating test data using the superset as a baseline; injecting the test data into the superset cluster; determining if the superset cluster rejects the injected test data as an indication of insufficient variability.

Abstract: Embodiments can provide a method for identifying a behavioral pattern from simulated transaction data, including: simulating transaction data using a reinforcement learning model; identifying a behavioral pattern from the simulated transaction data; comparing the behavioral pattern with standard customer transaction data to determine whether the behavioral pattern is present in the standard customer transaction data. If the behavioral pattern is present in the standard customer transaction data, the behavioral pattern is applied in a model implemented on the cognitive system.

Abstract: A system, platform, computer programming product, and/or method includes providing a trained intelligent agents to predict simulated transactional activity of a simulated person; pairing a person to the trained intelligent agent based upon the transactional activity of the person; predicting, by the paired trained intelligent agent, simulated transactional activity of the simulated person for a measured period; scoring the simulated transactional activity for the measured period; scoring the transactional activity undertaken by the paired person for the measured period; determining if the score of the simulated transactional activity for the measured period is different than the score of the paired person transactional activity for the measured period; and generating, in response to determining that the score of the simulated transaction activity for the measured period is different than the score of the paired person transactional activity for the measured period, a report.

Abstract: An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a plurality of features in common, and determine that a cluster represents a standard customer, and store a plurality of standard customer profiles based on the determined standard customers, wherein the standard customer profiles comprise a plurality of data distributions for the plurality of features in common. The abstraction system also derives additional standard customer profiles by applying a boundary limiter to the customer data. The abstraction system additionally provides the standard customer profiles and the additional standard customer profiles to a cognitive system for generating synthetic transaction data.

Abstract: A method, system, and computer programming product that includes providing a trained intelligent agent to predict the transactional activity of one or more persons; predicting, by the trained intelligent agent, simulated transactional activity of a person, simulated person, or a set of simulated persons, for a measured period; scoring the simulated transactional activity for the measured period; injecting testing data into the simulated transactional activity to form testing transactional behavior for the measured period; scoring the testing transactional behavior for the measured period; determining if the score of the simulated transactional activity is different than the score of the testing transactional activity; and generating, in response to the score of the simulated transaction activity deviating from the score of the testing transactional activity for the measured period, a report.

Abstract: Embodiments can provide a computer implemented method for simulating transaction data using a reinforcement learning model including an intelligent agent, a policy engine, and an environment, the method including: providing, by a processor, standard customer transaction data representing a group of customers having similar transaction characteristics as a goal; conducting, by the intelligent agent, an action including a plurality of simulated transactions; comparing, by the environment, the action with the goal; providing a feedback, by the environment, the action based on a degree of similarity relative to the goal; and adjusting, by the policy engine, a policy based on the feedback; the step of conducting an action to the step of adjusting a policy are repeated until the degree of similarity is higher than a first predefined threshold.

Abstract: An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network and perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common. The abstraction system performs unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common, and repeats the unsupervised learning on the plurality of sub-clusters produced to produce further sub-clusters with a plurality of features in common. The abstraction system determines that a sub-cluster represents a standard customer and stores a plurality of standard customer profiles based on the determined standard customers. The abstraction system provides the standard customer profiles to a cognitive system for generating synthetic transaction data.

Abstract: Embodiments can provide a computer implemented method for simulating transaction data using a reinforcement learning model including an intelligent agent, a policy engine, and an environment, the method including: providing standard customer transaction data representing a group of customers having similar transaction characteristics as a goal; and performing a plurality of iterations to simulate the standard customer transaction data, wherein the plurality of iterations is performed until a degree of similarity of simulated customer transaction data relative to the standard customer transaction data is higher than a first predefined threshold.

Abstract: An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, the customer data including information for a plurality of customers and perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a plurality of features in common, determine that a cluster represents a standard customer and store a plurality of standard customer profiles based on the determined standard customers. The abstraction system may also provide the standard customer profiles to a cognitive system for generating synthetic transaction data based on the standard customer, generate a detection model for detecting activity based on the synthetic transaction data, and distribute the detection model to each of the computing devices over the network.

Abstract: An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network and perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common. The abstraction system performs unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common, and repeats the unsupervised learning on the plurality of sub-clusters produced to produce further sub-clusters with a plurality of features in common. The abstraction system determines that a sub-cluster represents a standard customer and stores a plurality of standard customer profiles based on the determined standard customers. The abstraction system provides the standard customer profiles to a cognitive system for generating synthetic transaction data.

Abstract: An abstraction system for generating a standard customer profile in a data processing system has a processing device and a memory. The abstraction system may receive customer data from a computing device over a network, perform unsupervised learning on the customer data to produce a plurality of clusters of customers with a plurality of features in common, and determine that a cluster represents a standard customer, and store a plurality of standard customer profiles based on the determined standard customers, wherein the standard customer profiles comprise a plurality of data distributions for the plurality of features in common. The abstraction system additionally provides the standard customer profiles and the additional standard customer profiles to a cognitive system for generating synthetic transaction data.

Abstract: Embodiments can provide a method for identifying a behavioral pattern from simulated transaction data, the method including: simulating transaction data using a reinforcement learning model; and identifying a behavioral pattern from the simulated transaction data. The step of simulating transaction data further includes: providing standard customer transaction data representing a group of customers having similar transaction characteristics as a goal; and performing a plurality of iterations to simulate the standard customer transaction data, wherein the plurality of iterations is performed until a degree of similarity of simulated customer transaction data relative to the standard customer transaction data is higher than a first predefined threshold.

Abstract: A method, computer system, and a computer program product for labeling optimization is provided. The present invention may include receiving a plurality of labeled historical transaction timeline image clusters based on a plurality of historical transaction timeline images clustered using unsupervised machine learning. The present invention may further include training an image recognition model using supervised machine learning based on the received plurality of labeled historical transaction timeline image clusters. The present invention may also include receiving, by the trained image recognition model, a current transaction timeline image. The present invention may further include assigning a corresponding label to the received current transaction timeline image based on matching the received current transaction timeline image to one of the received plurality of labeled historical transaction timeline image clusters.

Abstract: A system, computer program product, and method are presented for facilitating determinations of risk including behavior classifications and predictions through timeline reshaping and rescoring of structured data. One embodiment of the method includes receiving, for one or more target focal objects, at least a portion of a transaction history including a plurality of sequential transactions, where the portion of the transaction history is associated with a first temporal range. The method also includes generating a first transaction timeline image representative of the portion of the transaction history, where the first temporal range includes a first temporal scaling. The method further includes labeling, through a machine learning (ML) model, the first transaction timeline image. The method also includes reshaping the first transaction timeline image, including rescaling the first temporal range, thereby generating a rescaled transaction timeline image, and labeling the rescaled transaction timeline image.

Abstract: One or more computer processors select a plurality of key-events contained in a dataset. The one or more computer processors determine a plurality of chart parameters based on the dataset. The one or more computer processors generate a plurality of charts utilizing the determined plurality of chart parameters, selected key-events, associated data, and a timeline generator. The one or more computer processors cluster the generated plurality of charts into a one or more chart macro-clusters. The one or more computer processors decompose the one or more chart macro-clusters into one or more chart micro-clusters.