METHOD AND SYSTEM FOR AN INTEGRATED APPROACH TO COLLECTIONS CYCLE OPTIMIZATION

• US 20140379555A1
• Filed: 09/12/2014
• Published: 12/25/2014
• Est. Priority Date: 12/31/2008
• Status: Abandoned Application

• Current Assignee: 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 S.A R.L. (Altisource Portfolio Solutions SA)
• Inventors: Erbey, William C., Faris, Ronald M., Pandey, Ashish, Saluja, Amanjeet, Dhayanithy, Deepak, Chawla, Saurav, Carter, Seth Evans
• Application Number:
  US14/485,160
• Publication Number:
  US 20140379555A1
• Time in Patent Office: Days
• US Class Current: 705/38
• 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. Candid ate 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 at least one collections loan cycle between a lender and at least one borrower using at least one processor and memory coupled to the at least one processor, the lender having a plurality of collections officers, the method comprising:

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

determining, via the at least one processor, an optimal resolution of the at least one collections loan cycle for the at least one borrower;

selecting, via the at least one processor, a collections officer to communicate to the at least one borrower from the plurality of collections officers, wherein the selection is based on the personality traits of the selected collections officer and the optimal resolution of the at least one collections loan cycle; and

delivering, via the at least one processor, a communication of the optimal resolution to the selected collections officer.

2. The method of claim 1, further comprising:

generating an estimate of the effectiveness of each collections officer in the plurality of collections officers based on categorized personality traits.

3. The method of claim 2, wherein the 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, further comprising:

generating a script for the selected collections officer to communicate the optimal resolution of the at least one collections loan cycle to the at least one borrower;

wherein, delivering the communication of the optimal resolution to the selected collections officer comprises delivering the script to a display unit from which the selected collections officer may access it.

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 further comprises optional conversation branches.

7. The method of claim 4, further comprising:

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

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

modifying a collections plan for the at least one borrower.

9. The method of claim 8, wherein the collections plan for the at least one borrower is modified using at least one factor selected from a group consisting of interest rate, loan principal, delay of payment en and alternative payment plan.

10. The method of claim 1, wherein the optimal resolution is determined using a cash flow model, and wherein using the cash flow model comprises:

comparing cash flows generated by at least two options; and

generating a prediction based on the comparison.

11. The method of claim 1, further comprising determining the communication of the optimal resolution according to behavioral information of the at least one borrower gathered in the at least one collections loan cycle.

12. A system for optimizing at least one collections loan cycle between a lender and at least one borrower, the tender having at least one collections officer, the system comprising:

at least one processor;

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

a second module operatively coupled to the at least one processor, the second module for determining an optimal resolution of the at least one collections loan cycle for the at least one borrower;

a third module operatively coupled to the at least one processor, the third module for selecting from the at least one collections officer a selected collections officer to communicate to the at least one borrower, the selection being based on at least one personality trait of the selected collections officer and the optimal resolution of the at least one collections loan cycle; and

a fourth module operatively coupled to the at least one processor, the fourth module for delivering a communication of the optimal resolution to the selected collections officer.

13. The system of claim 12, wherein the first module 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 at least one collections officer based on the categorized personality traits.

14. The system of claim 12, further comprising a fifth module operatively coupled to the at least one processor, the fifth module for generating a script via a scripting engine for the selected collections officer to use in delivering the optimal resolution of the at least one collections loan cycle to the at least one borrower.

15. The system of claim 14, further comprising:

a sixth module operatively coupled to the at least one processor, the sixth module for inputting best collections practices into the scripting engine.

16. The system of claim 12, wherein the optimal resolution of the at least one collections loan cycle comprises a collections plan for the at least one borrower, the collections plan using at least one factor selected from a group consisting of interest rate, loan principal, delay of payment and alternative payment plan.

17. The system of claim 12, wherein the optimal resolution of the at least one collections loan cycle is identified via a cash flow model.

18. The system of claim 12, further comprising:

a fifth module operatively coupled to the at least one processor, the fifth module for comparing cash flows generated by at least two options; and

a sixth module operatively coupled to the at least one processor, the sixth module for generating a prediction based on the comparison.

19. 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 instructions 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;

computer-readable instructions for optimizing, via the at least one processor, an effectiveness of the collections officer by measuring personality traits of the collections officer and selecting the collections officer from a plurality of collections officers based on the personality traits of the selected collections officer and the optimal resolution of the collections cycle to be communicated by the collections officer;

computer-readable instructions for generating, via the at least one processor, a script for use by the selected collections officer in communicating with the borrower, the generated script being generated based on statistical analysis of the persuasiveness of the script.

20. The method of claim 19, wherein the generated script is further generated based on borrower behavioral information.