SYSTEM AND METHOD FOR FACILITATING PREDICTION OF A LOAN RECOVERY DECISION

• US 20140052606A1
• Filed: 08/15/2013
• Published: 02/20/2014
• Est. Priority Date: 08/16/2012
• Status: Abandoned Application

• Current Assignee: Infosys Limited
• Original Assignee: Infosys Limited
• Inventors: Balakrishnan, Rajesh, Vasudevan, Bintu G., Parvathy, Anju G., Kumar, Abhishek
• Application Number:
  US13/968,277
• Publication Number:
  US 20140052606A1
• Time in Patent Office: Days
• US Class Current: 705/38
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof

Abstract

A system for facilitating prediction of a loan recovery decision pertaining to a customer of a financial institution is provided. The system comprises one or more databases comprising customer interaction data, customer profile data, and economic data. The system further comprises a Behavioral History Sequence (BHS) module configured to generate behavioral history sequence data associated with the customer. The BHS module generates the BHS data by sanitizing the customer interaction data and classifying the sanitized customer interaction data into predefined categories. The system further comprises a prediction module that is configured to predict payment behavior of the customer based on the BHS data, the customer profile data, and the economic data. The prediction module is further configured to predict the loan recovery decision pertaining to the customer, wherein the predicted loan recovery decision is based on the predicted payment behavior of the customer.

Claims

1. A system for facilitating prediction of a loan recovery decision pertaining to a customer of a financial institution, the system comprising:

one or more databases comprising customer interaction data, customer profile data, and economic data;

a Behavioral History Sequence (BHS) module configured to generate behavioral history sequence data associated with the customer, wherein the BHS module comprises;

a text sanitization engine configured to;

filter out unwanted text from the customer interaction data, and

correct spellings in the customer interaction data; and

a categorizer module configured to classify the sanitized customer interaction data into predefined categories to generate the BHS data associated with the customer, wherein the pre-defined categories correspond to payment behavioral states of the customer; and

a prediction module configured to predict payment behavior of the customer based on the BHS data, the customer profile data, and the economic data, the prediction module further configured to predict the loan recovery decision pertaining to the customer, wherein the predicted loan recovery decision is based on the predicted payment behavior of the customer.

2. The system of claim 1, wherein the customer interaction data is unstructured data and comprises at least one of:

call center notes, text messages from the customer, chats with the customer, emails from the customer, blogs written by the customer, call transcripts associated with the customer, feedback forms filled by the customer, and surveys filled by the customer.

3. The system of claim 1, wherein the customer profile data is structured data and comprises name of the customer, age of the customer, gender of the customer, employment details of the customer, bank account details of the customer, contact details of the customer, details of medical state of the customer, details of natural calamities associated with the customer, credit score of the customer, and details of delinquencies by the customer in repaying the loan in last one year.

4. The system of claim 1, wherein the economic data is structured data and comprises Gross Domestic Product (GDP) data, inflation data, and interest rates of the financial institution.

5. The system of claim 1, wherein the text sanitization engine uses a Domain Specific Acronym (DSA) list, a Domain Dictionary (DD), and an English language dictionary to correct the spellings in the customer interaction data.

6. The system of claim 1, wherein the categorizer module uses naive Bayes classification algorithm to classify the customer interaction data.

7. The system of claim 1, wherein the payment behavioral states of the customer comprise at least one of:

‘

Promise to Pay’

, ‘

Negotiation Fail’

, and ‘

Not Available’

.

8. The system of claim 1, wherein the BHS module further comprises a staging database, the staging database stores the generated BHS data with domain specific rules and heuristics.

9. The system of claim 1, wherein the prediction module employs a Bayesian network with plurality of nodes to predict the payment behavior of the customer and the loan recovery decision pertaining to the customer, wherein each node of the plurality of the nodes is associated with two or more states.

10. The system of claim 9, wherein the payment behavior of the customer and the loan recovery decision is based on one of:

state of each node of the plurality of the nodes and predicted next state of at least one node of the plurality of the nodes.

11. The system of claim 10, wherein the prediction module employs a neural network to predict the next state of the at least one node of the plurality of the nodes.

12. The system of claim 1, wherein the customer is a delinquent customer of the financial institution.

13. The system of claim 1, wherein the predicted payment behavior of the customer is one of:

Likely to Pay, Negotiable and Defaulter.

14. The system of claim 1, wherein the prediction module further facilitates performing root cause analysis, sensitivity analysis, and variability analysis of the predicted payment behavior of the customer.

15. The system of claim 1, wherein the predicted loan recovery decision pertaining to the customer is one of:

a strict follow-up with the customer and a lenient follow-up with the customer.

16. A method for facilitating prediction of a loan recovery decision pertaining to a customer of a financial institution, the method comprising:

sanitizing customer interaction data obtained from one or more databases, wherein the sanitization comprises;

filtering out unwanted text from the customer interaction data; and

correcting spellings in the customer interaction data;

classifying the sanitized customer interaction data into predefined categories to generate BHS data associated with the customer, wherein the pre-defined categories correspond to payment behavioral states of the customer;

predicting payment behavior of the customer based on the BHS data, customer profile data, and economic data;

wherein the customer profile data, and the economic data are obtained from the one or more databases; and

predicting the loan recovery decision pertaining to the customer, wherein the predicted loan recovery decision is based on the predicted payment behavior of the customer.

17. The method of claim 16, wherein the customer interaction data is unstructured data and comprises at least one of:

call center notes, text messages from the customer, chats with the customer, emails from the customer, blogs written by the customer, call transcripts associated with the customer, feedback forms filled by the customer, and surveys filled by the customer.

18. The method of claim 16, wherein the payment behavioral states of the customer comprise at least one of:

‘

Promise to Pay’

, ‘

Negotiation Fail’

, and ‘

Not Available’

.

19. The method of claim 16, wherein the customer profile data is structured data and comprises name of the customer, age of the customer, gender of the customer, employment details of the customer, bank account details of the customer, contact details of the customer, details of medical state of the customer, details of natural calamities associated with the customer, credit score of the customer, and details of delinquencies by the customer in repaying the loan in last one year.

20. The method of claim 16, wherein the economic data is structured data and comprises GDP data, inflation data, and interest rates of the financial institution.

21. The method of claim 16, wherein the prediction of the payment behavior of the customer and the loan recovery decision pertaining to the customer is done by employing a Bayesian network with plurality of nodes, further wherein each node of the plurality of the nodes is associated with two or more states.

22. The method of claim 21, wherein the payment behavior of the customer and the loan recovery decision is based on one of:

state of each node of the plurality of the nodes and predicted next state of at least one node of the plurality of the nodes.

23. The method of claim 22, wherein the prediction of the next state of the at least one node of the plurality of the nodes is done by a neural network.

24. The method of claim 16, wherein the customer is a delinquent customer of the financial institution.

25. The method of claim 16, wherein the predicted payment behavior of the customer is one of:

Likely to Pay, Negotiable and Defaulter.

26. The method of claim 16 further comprises performing root cause analysis, sensitivity analysis, and variability analysis of the predicted payment behavior of the customer.

27. The method of claim 16, wherein the predicted loan recovery decision pertaining to the customer is one of:

a strict follow-up with the customer and a lenient follow-up with the customer.

28. A computer program product for facilitating prediction of a loan recovery decision pertaining to a customer of a financial institution is provided, the computer program product comprising:

a non-transitory computer-readable medium having computer-readable program code stored thereon, the computer-readable program code comprising instructions that when executed by a processor, cause the processor to;

sanitize the customer interaction data obtained from one or more databases, wherein the sanitization comprises;

filtering out unwanted text from the customer interaction data; and

correcting spellings in the customer interaction data;

classify the sanitized customer interaction data into predefined categories to generate BHS data associated with the customer, wherein the pre-defined categories correspond to payment behavioral states of the customer;

predict payment behavior of the customer based on the BHS data, customer profile data, and economic data;

wherein the customer profile data, and the economic data are obtained from the one or more databases; and

predict the loan recovery decision pertaining to the customer, wherein the predicted loan recovery decision is based on the predicted payment behavior of the customer.