Method and system for an integrated approach to collections cycle optimization

• US 8,473,391 B2
• Filed: 03/25/2010
• Issued: 06/25/2013
• Est. Priority Date: 12/31/2008
• Status: Expired due to Fees

• Current Assignee: Altisource Holdings, S.A.R.L. (Altisource Portfolio Solutions SA), Andrei Iancu, Ashley Moody Florida Office of The Attorney General and Successors Thereto, Jeanette Nunez Lieutenant Governor of Florida and Successors Thereto, Joan D. Heuer, Laurel M Lee Florida Secretary of State and Successors Thereto, Steven Mnuchin United States Secretary of The Treasury and Successors Thereto, Timothy E Gribben Commissioner For Bureau of The Fiscal Service Agency of The United States Department of The Treasury and Successors and Assigns Thereto
• Original Assignee: Altisource Solutions SARL (Altisource Portfolio Solutions SA)
• Inventors: Erbey, William C., Faris, Ronald M., Pandey, Ashish, Saluja, Amanjeet, Dhayanithy, Deepak, Chawla, Saurav, Carter, Seth
• Primary Examiner(s): Robinson, Kito R
• Application Number:
  US12/731,888
• Publication Number:
  US 20110060671A1
• Time in Patent Office: 1,188 Days
• Field of Search: 705/35, 705/38
• US Class Current: 705/35
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 10/06398 Performance of employee with respect to a job function
• CPC Class Codes: G06Q 20/102 Bill distribution or payments
• CPC Class Codes: G06Q 40/00 Finance; Insurance; Tax strategies; Processing of corporate or income taxes
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03
• CPC Class Codes: G06Q 40/03 Credit; Loans; Processing thereof
• CPC Class Codes: G08B 25/08 using communication transmission lines G08B13/19658, G08B21/0286, G08B25/016 take precedence

Abstract

Methods and systems are provided for an integrated approach to collections cycle optimization including optimizing personnel, communications, and collection resolutions, resulting in reducing the variability of the collections cycle, reducing instances of foreclosure in collections, while increasing borrower satisfaction with the collections process. Candidate loan officers are selected based on behavioral attributes, personnel history, and human resources information, among other factors. Scripts that improve the effectiveness of communication between loan collection staff and borrowers are generated. An optimized collection program to best suit the needs of a particular borrower is determined.

Claims

1. A method for optimizing a collections loan cycle between a lender and a borrower using at least one processor and memory coupled to the processor, the lender having a collections officer, the method comprising:

Measuring, via the at least one processor, personality traits of the collections officers;

categorizing, via the at least one processor, the personality traits into two or more categories;

optimizing, via the at least one processor, a persuasiveness of communications between the lender and the borrower by choosing the collections officer based on the two or more categories of personality traits of the collections officer; and

determining, via the at least one processor, an optimal resolution of the collections loan cycle to be communicated by the collections officer to the borrower, the optimal resolution being identified via a cash flow model.

2. The method of claim 1, further comprising:

generating an estimate of the effectiveness of the collections officer based on the categorized personality traits.

3. The method of claim 2, wherein the two or more categories include at least one category selected from a group consisting of a behavioral category, a cognitive category, a personality category and a demographic category.

4. The method of claim 1, wherein optimizing the persuasiveness of communications further comprises:

generating a script.

5. The method of claim 4, wherein the script is generated based on borrower behavioral information.

6. The method of claim 4, wherein the script is generated by a scripting engine.

7. The method of claim 4, wherein the script further comprises optional conversation branches.

8. The method of claim 6, further comprising:

inputting best collections practices into the scripting engine.

9. The method of claim 4, further comprising:

determining the effectiveness of the generated script based on statistical analysis.

10. The method of claim 1, wherein determining the optimal resolution further comprises:

modifying a collections plan for the borrower.

11. The method of claim 10, wherein the collections plan for the borrower is modified using at least one factor selected from a group consisting of interest rate, loan principal, delay of payment and alternative payment plan.

12. The method of claim 1, cash flow model comprising:

comparing cash flows generated by at least two options; and

generating a prediction based on the comparison.

13. The method of claim 4, further comprising:

comparing cash flows generated by at least two options; and

generating a prediction based on the comparison.

14. A system for optimizing a collections loan cycle between a lender and a borrower, the lender having a collections officer, the system comprising:

at least one processor;

a first module operatively coupled to the at least one processor, the first module for optimizing an effectiveness of the collections officer by measuring personality traits of the collections officer;

a second module operatively coupled to the at least one processor, the second module for optimizing communications between the lender and the borrower and comprising a module for generating a script based on borrower behavioral information for use by the collections officer in communicating with the borrower; and

a third module operatively coupled to the at least one processor, the third module for determining an optimal resolution of the collections loan cycle and comprising a module for determining the effectiveness of the generated script and the effectiveness of the collections officer based on statistical analysis.

15. The system of claim 14, wherein the first module for optimizing the effectiveness of the collections officer further comprises:

a module for categorizing the personality traits into two or more categories; and

a module for generating an estimate of the effectiveness of the collections officer based on the categorized personality traits.

16. The system of claim 15, wherein the two or more categories include at least one category selected from a group consisting of a behavioral category, a cognitive category, a personality category and a demographic category.

17. The system of claim 14, wherein the module for generating the script comprises a scripting engine.

18. The system of claim 14, wherein the script further comprises optional conversation branches.

19. The system of claim 17, further comprising:

a module for inputting best collections practices into the scripting engine.

20. The system of claim 14, further comprising:

a module for determining the effectiveness of the generated script based on statistical analysis.

21. The system of claim 14, wherein the collections plan for the borrower is modified using at least one factor selected from a group consisting of interest rate, loan principal, delay of payment and alternative payment plan.

22. The system of claim 14, wherein the optimal resolution is identified via a cash flow model.

23. The system of claim 14, further comprising:

a module for comparing cash flows generated by at least two options; and

a module for generating a prediction based on the comparison.

24. A computer program product comprising a non-transitory computer usable medium having executable control logic stored therein for causing a computer having at least one processor to optimize a collections loan cycle between a lender and a borrower, the lender having a collections officer, the executable control logic comprising:

computer readable program code means for optimizing, via the at least one processor, an effectiveness of the collections officer by measuring personality traits of the collections officer;

computer readable program code means for optimizing, via the at least one processor, communications between the lender and the borrower by generating a script for use by the collections officer in communicating with the borrower, the generated script being generated based on statistical analysis of the effectiveness of the script; and

computer readable program code means for determining, via the at least one processor, an optimal resolution of the collections loan cycle to be communicated by the collections officer to the borrower via the generated script.