Abstract: Embodiments can provide a computer implemented method for identifying a match between a buyer and a seller for a real estate transaction, comprising: receiving, from the buyer, a service request; receiving, from the buyer, historical information stored in a buyer immutable record; receiving, from the buyer, one or more real estate requirements; determining a buyer need profile based on the historical information and the real estate requirements; receiving, from the buyer, one or more answers in response to one or more first questions raised by the processor; refining the buyer need profile based on the one or more answers; identifying a match between the buyer need profile and an available real estate profile from the seller; and providing a ranked list of real estate properties and supporting evidence for each real estate property to the buyer.

Abstract: Embodiments can provide a computer implemented method for identifying a match between a buyer and a seller for a real estate transaction, comprising: receiving, from the seller, a service request; receiving, from the seller, historical information stored in a real estate immutable record; receiving, from the seller, one or more real estate property facts; determining a real estate profile based on the historical information and the one or more real estate property facts; receiving, from the seller, one or more answers in response to one or more first questions raised by the processor; refining the real estate profile based on the one or more answers; identifying a match between an available buyer need profile and the real estate profile from the seller; and providing a ranked list of buyer candidates and supporting evidence for each buyer candidate to the seller.

Abstract: Embodiments provide a computer implemented method, including: ingesting, by the processor, a data file including financial transaction data; generating, by the processor, a schema from the financial transaction data; identifying, by the processor, a data type of each data field in the schema; performing, by the processor, feature selection to select a plurality of data fields from the schema; performing, by the processor, data sampling to select candidate rows of the schema based on the selected data fields; clustering, by the processor, the sampled data to different categories; selecting, by the processor, a set of analytical models for risk prediction, wherein each analytical model corresponds to a data type of each selected data field, and each analytical model generates a risk score; and generating, by the processor, a single risk score indicating the fraud risk by combining all the risk scores generated by the set of analytical models.

Abstract: A local node for updating detection models while maintaining data privacy has an aggregation module, a retraining module, an instructions module, and a sharing module. The aggregation module aggregates the data into features that describe the contents of the data. The retraining module retrains the detection model using the features by implementing an algorithm that includes at least one selected feature and a threshold for triggering an activity alert. The instructions module determines instructions for calculating the at least one selected feature from a different collection of data. The sharing module generates a package having the instructions for calculating the at least one selected feature from the different collection of data and the threshold, and transmits the package to a local node for implementation of the retrained detection model with data stored at the local node.

Abstract: An abstraction system for generating a standard customer profile may receive customer data 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. Generating synthetic transaction data includes selecting a standard customer profile as a goal, simulating a plurality of transactions, comparing the plurality of transactions with the goal, providing feedback, adjusting a policy based on the feedback, repeating until a degree of similarity between the plurality of transactions and the goal is higher than a predefined threshold, and outputting the resulting plurality of transactions as the 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, 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: 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: 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: 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: 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: 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: 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: 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: 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 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: A local node for updating detection models while maintaining data privacy has an aggregation module, a retraining module, an instructions module, and a sharing module. The aggregation module aggregates the data into features that describe the contents of the data. The retraining module retrains the detection model using the features by implementing an algorithm that includes at least one selected feature and a threshold for triggering an activity alert. The instructions module determines instructions for calculating the at least one selected feature from a different collection of data. The sharing module generates a package having the instructions for calculating the at least one selected feature from the different collection of data and the threshold, and transmits the package to a local node for implementation of the retrained detection model with data stored at the local node.

Abstract: The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.

Abstract: A local node for updating detection models while maintaining data privacy has a sharing module configured to receive instructions for calculating at least one general feature from data stored at the first local node, a retraining module configured to retrain the detection model to include a detection component that uses the instructions, a data collection module configured to collect data comprising customer data and transaction data stored at the first local node, and a performance module. The performance module is configured to determine a value for the at least one general feature from the collected data using the instructions, and trigger a suspicious activity alert based on the determined value and the instructions.

Abstract: The present application relates to systems for updating detection models and methods for using the same. The systems and methods generally comprise at least one local node comprising a monitoring module, a diagnosis module, and an evaluation module The system receives at least one model update, and analyzes the model update and current models and data present in the local node, and determines if the update should be applied. In some embodiments, a local node can generate a model update for use in other local nodes, while not sharing private data present in the local node.