GB2605054A

GB2605054A - Intelligent agent to simulate customer data

Info

Publication number: GB2605054A
Application number: GB2207340.7A
Authority: GB
Inventors: Harris Brandon; Irving Kelton Eugene; Vollmer Chaz
Original assignee: International Business Machines Corp
Current assignee: International Business Machines Corp
Priority date: 2019-11-05
Filing date: 2020-11-02
Publication date: 2022-09-21
Also published as: JP2023500698A; WO2021090142A1; JP2023501343A; CN114730359A; DE112020005484T5; GB202207340D0; WO2021090141A1; CN114616546A

Abstract

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 combining the artificial customer profile with the action to form simulated customer data.

Claims

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; providing, by the processor, 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 combining, by the processor, the artificial customer profile with the last action to form simulated customer data.

2. The method as recited in claim 1, wherein the real customer profile data includes one or more of an address of a customer, a name of a customer, contact information, credit information, and income information.

3. 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.

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

5. The method as recited in claim 4, further comprising: removing, by the processor, a plurality of previous actions having the degree of similarity lower than a second predefined threshold.

6 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.

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

8. 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; provide standard customer transaction data representing a group of customers having similar transaction characteristics as a goal; conduct, by the intelligent agent, an action including a plurality of 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; adjust, 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 combine the artificial customer profile with the last action to form simulated customer data.

9. The system of claim 8, wherein the real customer profile data includes one or more of an address of a customer, a name of a customer, contact information, credit information, and income information.

10. The system of either of claims 8 or 9, wherein the environment includes a set of all previous actions conducted by the intelligent agent.

11. The system of claim 10, wherein prior to the step of adjusting a policy, the processor is further configured to add the action into the environment.

12. The system of either of claims 10 or 11, wherein the processor is further configured to remove a plurality of previous actions having the degree of similarity lower than a second predefined thresholds.

13. The system of any of claims 8 to 12, wherein the feedback is a reward or a penalty.

14. A computer program product for simulating transaction data, the computer program product comprising: a computer readable storage medium readable by a processing circuit and storing instructions for execution by the processing circuit for performing a method according to any of claims 1 to 7.

15. A computer program stored on a computer readable medium and loadable into the internal memory of a digital computer, comprising software code portions, when said program is run on a computer, for performing the method of any of claims 1 to 7.