Intelligent agent to simulate customer data

1. A computer implemented method in a data processing system comprising a processor and a memory comprising instructions, which are executed by the processor to cause the processor to implement the method for simulating customer data using a reinforcement learning model including an intelligent agent, a policy engine, and an environment, the method comprising:

generating, by the processor, an artificial customer profile by combining randomly selected information from a set of real customer profile data, wherein the real customer profile data includes an address of a customer, a name of a customer, contact information, credit information, and income information;

generating, by the processor, simulated transaction data in imitation of real transaction data, wherein generating the simulated transaction data further comprises:

providing, by the processor, standard customer transaction data as a goal, wherein the standard customer transaction data is a statistical representation of a group of real customers having similar transaction characteristics extracted from real customer transaction data;

performing, by the processor, 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 transaction data relative to the standard customer transaction data is higher than a first predefined threshold;

in each iteration:

conducting, by the intelligent agent, an action, wherein the action comprises 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; and

adjusting, by the policy engine, a policy based on the feedback for determining the next action;

generating, by the processor, simulated customer data by combining the artificial customer profile with the last action; and

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

2. The method as recited in claim 1 , wherein each simulated transaction includes transaction type, transaction amount, transaction time, transaction location, transaction medium, a second party associated with the simulated transaction.

3. The method as recited in claim 1 , wherein the environment includes a set of all previous actions conducted by the intelligent agent.

4. The method as recited in claim 3 , further comprising:

removing, by the processor, a plurality of previous actions having the degree of similarity lower than a second predefined threshold, from the environment.

5. The method as recited in claim 1 , further comprising:

acquiring, by the processor, the standard customer transaction data from raw customer transaction data through an unsupervised clustering approach.

6. The method as recited in claim 1 , wherein the feedback is a reward or a penalty.

7. A computer program product for simulating customer data using a reinforcement learning model including an intelligent agent, a policy engine, and an environment, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

generate an artificial customer profile by combining randomly selected information from a set of real customer profile data, wherein the real customer profile data includes an address of a customer, a name of a customer, contact information, credit information, and income information;

generate, by the processor, simulated transaction data in imitation of real transaction data, wherein generating the simulated transaction data further comprises:

provide standard customer transaction data as a goal, wherein the standard customer transaction data is a statistical representation of a group of real customers having similar transaction characteristics extracted from real customer transaction data;

perform 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 transaction data relative to the standard customer transaction data is higher than a first predefined threshold;

in each iteration:

conduct, by the intelligent agent, an action, wherein the action comprises simulated transactions;

compare, by the environment, the action with the goal;

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

adjust, by the policy engine, a policy based on the feedback for determining the next action;

generate simulated customer data by combining the artificial customer profile with the last action; and

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

8. The computer program product of claim 7 , wherein each simulated transaction includes transaction type, transaction amount, transaction time, transaction location, transaction medium, a second party associated with the simulated transaction.

9. The computer program product of claim 7 , wherein the environment includes a set of all previous actions conducted by the intelligent agent.

10. The computer program product of claim 9 , wherein the program instructions executable by the processor further cause the processor to remove a plurality of previous actions having the degree of similarity lower than a second predefined threshold, from the environment.

11. The computer program product of claim 7 , wherein the program instructions executable by the processor further cause the processor to:

acquire the standard customer transaction data from raw customer transaction data through an unsupervised clustering approach.

12. The computer program product of claim 7 , wherein the feedback is a reward or a penalty.

13. A system for simulating customer data using a reinforcement learning model including an intelligent agent, a policy engine, and an environment, the system comprising:

a processor configured to:

generate an artificial customer profile by combining randomly selected information from a set of real customer profile data, wherein the real customer profile data includes an address of a customer, a name of a customer, contact information, credit information, and income information;

generate simulated transaction data in imitation of real transaction data, wherein generating the simulated transaction data further comprises:

providing standard customer transaction data as a goal, wherein the standard customer transaction data is a statistical representation of a group of real customers having similar transaction characteristics extracted from real customer transaction data;

in each iteration:

conducting, by the intelligent agent, an action, wherein the action comprises simulated transaction;

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 for determining the next action;

generate simulated customer data by combining the artificial customer profile with the last action to form simulated customer data; and

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

14. The system of claim 13 , wherein the environment includes a set of all previous actions conducted by the intelligent agent.

15. The system of claim 14 , wherein prior to the step of adjusting a policy, the processor is further configured to add the action into the environment.

16. The system of claim 14 , wherein the processor is further configured to remove a plurality of previous actions having the degree of similarity lower than a second predefined threshold, from the environment.

17. The system of claim 13 , wherein the feedback is a reward or a penalty.