System and method for unsupervised abstraction of sensitive data for realistic modeling

1. A computer-implemented method for generating a standard customer profile in a data processing system comprising a processing device and a memory comprising instructions which are executed by the processing device, the method comprising:

receiving customer data from a computing device over a network, the customer data including information for a plurality of customers;

performing, by the processing device, unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common;

performing, by the processing device, unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common;

repeating, by the processing device, the unsupervised learning on the plurality of sub-clusters to produce further sub-clusters with a plurality of features in common;

determining, by the processing device, that a sub-cluster represents a standard customer;

providing a plurality of artificial customer profiles to a cognitive system, wherein the plurality of artificial customer profiles are generated by combining randomly selected information from real customer profile data including address, first name, second name, phone number, email address, credit score, and wage;

generating, by the cognitive system, simulated transaction data in imitation of real transaction data through a reinforcement learning model, wherein the step of generating the simulated transaction data further comprises:

providing standard customer transaction data representing a group of real customers having similar transaction characteristics as a goal;

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;

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;

combining one of the plurality of artificial customer profiles with the simulated transaction data to form simulated customer data; and

training a predictive model for identifying an abnormal customer behavior, using the simulated customer data.

2. The method of claim 1 , wherein the information for the plurality of customers comprises identifying information and transaction information.

3. The method of claim 1 , further comprising filtering the customer data prior to performing unsupervised learning.

4. The method of claim 3 , wherein the filtering comprises a recency, frequency, monetary value (RFM) analysis to group customers.

5. The method of claim 1 , wherein determining that a sub-cluster represents a standard customer comprises applying one or more rules.

6. The method of claim 5 , wherein the one or more rules comprise a size determination indicating a minimum or maximum number of customers in a sub-cluster that is determined to be a standard customer.

7. An abstraction system comprising a processing device and a memory comprising instructions which are executed by the processing device for generating a standard customer profile in a data processing system configured to:

receive customer data from a computing device over a network, the customer data including information for a plurality of customers;

perform by the processing device, unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common;

perform, by the processing device, unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common;

repeat, by the processing device, the unsupervised learning on the plurality of sub-clusters produced by the unsupervised learning to produce further sub-clusters with a plurality of features in common;

determine, by the processing device, that a sub-cluster represents a standard customer;

provide a plurality of artificial customer profiles to a cognitive system, wherein the plurality of artificial customer profiles are generated by combining randomly selected information from real customer profile data including address, first name, second name, phone number, email address, credit score, and wage;

generate, by the cognitive system, simulated transaction data in imitation of real transaction data through a reinforcement learning model, wherein the step of generating the simulated transaction data further configures the processing device to:

provide standard customer transaction data representing a group of real customers having similar transaction characteristics as a goal;

conduct an action including a plurality of simulated transactions;

compare the action with the goal;

provide a feedback associated with the action based on a degree of similarity relative to the goal;

adjust 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;

combine one of the plurality of artificial customer profiles with the simulated transaction data to form simulated customer data; and

train a predictive model for identifying an abnormal customer behavior, using the simulated customer data.

8. The abstraction system of claim 7 , wherein the information for the plurality of customers comprises identifying information and transaction information.

9. The abstraction system of claim 7 , further comprising filtering the customer data prior to performing unsupervised learning.

10. The abstraction system of claim 9 , wherein the filtering comprises a recency, frequency, monetary value (RFM) analysis to group customers.

11. The abstraction system of claim 7 , wherein determining that a sub-cluster represents a standard customer comprises applying one or more rules.

12. The abstraction system of claim 11 , wherein the one or more rules comprise a size determination indicating a minimum or maximum number of customers in a sub-cluster that is determined to be a standard customer.

13. A computer program product comprising non-transitory memory storing software that when executed by a processor performs a method comprising:

receiving customer data from a computing device over a network, the customer data including information for a plurality of customers;

performing, by a processing device, unsupervised learning on the customer data to produce a plurality of clusters of customers with a first feature in common;

performing, by the processing device, unsupervised learning on the plurality of clusters of customers to produce a plurality of sub-clusters of customers with a second feature in common;

repeating, by the processing device, the unsupervised learning on the plurality of sub-clusters produced by the unsupervised learning to produce further sub-clusters with a plurality of features in common;

determining, by the processing device, that a sub-cluster represents a standard customer;

providing a plurality of artificial customer profiles to a cognitive system, wherein the plurality of artificial customer profiles are generated by combining randomly selected information from real customer profile data including address, first name, second name, phone number, email address, credit score, and wage;

generating, by the cognitive system, simulated transaction data in imitation of real transaction data through a reinforcement learning model, wherein the step of generating the simulated transaction data further comprises:

providing standard customer transaction data representing a group of real customers having similar transaction characteristics as a goal;

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;

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;

combining one of the plurality of artificial customer profiles with the simulated transaction data to form simulated customer data; and

training a predictive model for identifying an abnormal customer behavior, using the simulated customer data.

14. The computer program product of claim 13 , wherein the information for the plurality of customers comprises identifying information and transaction information.

15. The computer program product of claim 13 , further comprising filtering the customer data prior to performing unsupervised learning.

16. The computer program product of claim 15 , wherein the filtering comprises a recency, frequency, monetary value (RFM) analysis to group customers.

17. The computer program product of claim 13 , wherein determining that a sub-cluster represents a standard customer comprises applying one or more rules.

18. The computer program product of claim 17 , wherein the one or more rules comprise a size determination indicating a minimum or maximum number of customers in a sub-cluster that is determined to be a standard customer.