RECOMMENDER ENGINE FOR COLLECTIONS TREATMENT SELECTION

• US 20130151383A1
• Filed: 12/13/2011
• Published: 06/13/2013
• Est. Priority Date: 12/13/2011
• Status: Active Grant

• Current Assignee: Electrifai LLC
• Original Assignee: Opera Solutions, LLC.
• Inventors: Gancarz, Gregory, Zhang, Jenny, Crowe, Kathleen Marie
• Granted Patent: US 9,792,653 B2
• Time in Patent Office: Days
• US Class Current: 705/30
• CPC Class Codes: G06F 16/00 Information retrieval; Database structures therefor; File system structures therefor
• CPC Class Codes: G06F 17/40 Data acquisition and logging for input to computer G06F3/00
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 30/00 Commerce
• 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 40/08 Insurance
• CPC Class Codes: G06Q 40/12 Accounting

Abstract

A system for automatically recommending treatments for delinquent accounts in collections is provided. The system includes one or more sub-models for analyzing and scoring delinquent accounts and comparing them to historical accounts to identify similar historical accounts. The system can select and recommend one or more treatments for the delinquent account based on treatments for the historical accounts that may have previously provided a favorable outcome for a similar account. The system may test the efficacy of new treatments and incorporate the results into the memory, thereby learning and adapting to new treatments and account behavior.

Claims

1. A system for recommending treatments for one or more delinquent accounts at a particular point in time comprising:

a processor operable to execute programs;

a memory associated with the processor;

a database associated with said processor and said memory;

the memory comprising reference data for one or more accounts;

the memory comprising one or more sub-models;

a program stored in the memory and executable by the processors, the program being operable for;

accessing the reference data for the one or more accounts;

accessing the one or more sub-models;

expressing the reference data as historical vectors in an n-dimensional feature space;

receiving at least one recommendation request data record for each of one or more delinquent accounts;

converting the at least one recommendation request data record to request vectors using scores from the one or more sub-models;

selecting one or more neighbor vectors from the historical vectors for each of the request vectors, based on an algorithm analysis of data in the n-dimensional feature space;

determining neighbor fitness values for each of the selected one or more neighbor vectors by looking forward in historical time for each of the one or more accounts in the one or more neighbor vectors;

generating a list of allowed treatments for the request vectors from all treatments available for each of the selected one or more neighbor vectors by applying one or more sets of rules to said all treatments available;

computing a treatment fitness score for each of the treatments in the allowed treatments list, using the neighbor fitness values from the neighbor vectors that experienced a particular treatment being analyzed;

selecting at least one of the allowed treatments available for each of the at least one recommendation request based on the treatment fitness score; and

presenting the selected at least one treatment to a user.

2. The system of claim 1, wherein the delinquent accounts are selected from the group consisting of delinquent accounts, accounts in collection, historical accounts, test accounts, new accounts or combinations thereof.

3. The system of claim 1, wherein overall categories of the allowed treatments may include specific treatment to use, variant of the treatment, sequence of treatments, phone number to use, time of day to call, prioritization of accounts to receive treatment, specific collector to work an account.

4. The system of claim 1, wherein the one or more rules are selected from a group consisting of the policy and regulation expert rules, policies of the user, any applicable governmental regulations, financial industry regulations or combinations thereof.

5. The system of claim 1, further comprising the step of determining treatment fitness from events outside the one or more neighbor vectors.

6. The system of claim 1, further comprising the step of providing a rationale for recommending the selected at least one treatment.

7. The system of claim 1, wherein the sub-models are built using one or more models selected from a group consisting of neural networks, regression analysis, integrated rules systems, decision tree models, other statistical models or combinations thereof.

8. The system of claim 1, wherein the reference data comprises account demographics, treatments, model scores, account properties, account behaviors, days past due, the final collections result on the account or combinations thereof.

9. The system of claim 1, wherein the reference data comprises results from test accounts created to test the efficacy of new treatments or new sets of rules.

10. The system of claim 9, wherein the results are incorporated into the memory, thereby providing a mechanism for learning and adapting to new treatments and debtor behavior.

11. The system of claim 1, wherein the reference data comprises multiple entries for historical account data at different points in time.

12. The system of claim 1, wherein receiving the one or more recommendation request data, further comprises receiving a request for recommendation or notification record from a client device in a predefined API or XML format.

13. The system of claim 1, wherein the algorithm includes a calculation of the similarity between two or more n-feature vectors in an n-dimensional feature space.

14. The system of claim 13, wherein the similarity is a calculation of the distance between two or more n-feature vectors in an n-dimensional feature space.

15. The system of claim 13, wherein the algorithm calculation is selected from a group consisting of Mahalanobis distance calculation, Manhattan distance formulas, Euclidean distance formulas, Pearson correlation coefficient, Spearman rank correlation, Kendall'"'"'s τ

correlation, mean squared differences, entropy, adjusted cosine similarity or combinations thereof.

16. The system of claim 1, wherein the treatments are selected from a group which may include a letter sent by mail to the debtor'"'"'s home, a letter sent by mail to the debtor'"'"'s work, an email message sent to the debtor'"'"'s email address, an SMS message sent to the debtors telephone, a phone call made by a collection specialist to the debtors home, a phone call made by a collection specialist to the debtors office, no action, restructuring the delinquent account, forgiving a portion of the delinquent account, utilizing an automatic/automated agent, resending the bill, debt counseling, forbearance program, payment matching program, settlement agreement, running a skip trace search, multiple smaller payments, legal action or combinations thereof.

17. The system of claim 16, wherein the treatments are defined by a time duration, numeric value, frequency or combinations thereof.

18. The system of claim 17, wherein the time duration is selected from a group consisting essentially of days, weeks, months, quarters, years or combinations thereof.

19. The system of claim 17, wherein the numeric value is a settlement percentage.

20. The system of claim 17, wherein the frequency is the number of times the treatment is repeated within a fixed time period.

21. The system of claim 1, wherein the cost comprises an operational monetary cost and a relationship cost.

22. The system of claim 21, wherein the operational cost is assigned by the user or the system based on an analysis of historical data.

23. The system of claim 21, wherein the relationship cost is assigned by the user or the system based on an analysis of historical data.

24. The system of claim 1, wherein the fitness score comprises performance measures fed into a fitness function which summarizes the performance measures into a single score.

25. The system of claim 24, wherein the performance measures are selected from a group consisting of the average cost incurred on the delinquent accounts through the time the delinquent account exited collections, the recovery amount of the delinquent account, time to recovery on the delinquent account, number of payments, the final outcome on an account, recovery success percentage for the delinquent account, partial payments, completion of payment programs or combinations thereof.

26. The system of claim 24, wherein the fitness function comprises the overall average for the entire historical group and the average for the neighborhood group, weighted by the number of historical accounts in the neighborhood group.

27. A computer-implemented method for recommending treatments for one or more delinquent accounts at a particular point in time the method comprising the computer-implemented steps of:

accessing one or more servers, the one or more servers comprising a database with one or more sub-models, and a memory comprising reference data for one or more accounts;

expressing the reference data as historical vectors in an n-dimensional feature space;

receiving at least one recommendation request data record for each of one or more delinquent accounts;

converting the at least one recommendation request data record to request vectors using scores from the one or more sub-models;

selecting one or more neighbor vectors from the historical vectors for each of the request vectors, based on an algorithm analysis of data in the n-dimensional feature space;

determining neighbor fitness values for each of the selected one or more neighbor vectors by looking forward in historical time for each of the one or more accounts in the one or more neighbor vectors;

generating a list of allowed treatments for the request vectors from all treatments available for each of the selected one or more neighbor vectors by applying one or more sets of rules to said all treatments available;

computing a treatment fitness score for each of the treatments in the allowed treatments list, using the neighbor fitness values from the neighbor vectors that experienced a particular treatment being analyzed;

selecting at least one of the allowed treatments available for each of the at least one recommendation request based on the treatment fitness score; and

presenting the selected at least one treatment to a user.

28. The computer-implemented method of claim 27, wherein the delinquent accounts are selected from the group consisting of delinquent accounts, accounts in collection, historical accounts, test accounts, new accounts or combinations thereof.

29. The computer-implemented method of claim 27, wherein overall categories of the allowed treatments may include specific treatment to use, variant of the treatment, sequence of treatments, phone number to use, time of day to call, prioritization of accounts to receive treatment, specific collector to work an account.

30. The computer-implemented method of claim 27, wherein the one or more rules are selected from a group consisting of the policy and regulation expert rules, policies of the user, any applicable governmental regulations, financial industry regulations or combinations thereof.

31. The computer-implemented method of claim 27, further comprising the step of determining treatment fitness from events outside the one or more neighbor vectors.

32. The computer-implemented method of claim 27, further comprising the step of providing a rationale for recommending the selected at least one treatment.

33. The computer-implemented method of claim 27, wherein the sub-models are built using one or more models selected from a group consisting of neural networks, regression analysis, integrated rules systems, decision tree models, other statistical models or combinations thereof.

34. The computer-implemented method of claim 27, wherein the reference data comprises account demographics, treatments, model scores, account properties, account behaviors, days past due, the final collections result on the account or combinations thereof.

35. The computer-implemented method of claim 27, wherein the reference data comprises results from test accounts created to test the efficacy of new treatments or new sets of rules.

36. The computer-implemented method of claim 35, wherein the results are incorporated into the memory, thereby providing a mechanism for learning and adapting to new treatments and debtor behavior.

37. The computer-implemented method of claim 27, wherein the reference data comprises multiple entries for the historical account data at different points in time.

38. The computer-implemented method of claim 27, wherein receiving the one or more recommendation request data, further comprises receiving a request for recommendation or notification record from a client device in a predefined API or XML format.

39. The computer-implemented method of claim 27, wherein the algorithm includes a calculation of the similarity between two or more n-feature vectors in an n-dimensional feature space.

40. The computer-implemented method of claim 39, wherein the similarity is a calculation of the distance between two or more n-feature vectors in an n-dimensional feature space.

41. The computer-implemented method of claim 39, wherein the algorithm calculation is selected from a group consisting of Mahalanobis distance calculation, Manhattan distance formulas, Euclidean distance formulas, Pearson correlation coefficient, Spearman rank correlation, Kendall'"'"'s τ

correlation, mean squared differences, entropy, adjusted cosine similarity or combinations thereof.

42. The computer-implemented method of claim 27, wherein the treatments are selected from a group which may include a letter sent by mail to the debtor'"'"'s home, a letter sent by mail to the debtor'"'"'s work, an email message sent to the debtor'"'"'s email address, an SMS message sent to the debtors telephone, a phone call made by a collection specialist to the debtors home, a phone call made by a collection specialist to the debtors office, no action, restructuring the delinquent account, forgiving a portion of the delinquent account, utilizing an automatic/automated agent, resending the bill, debt counseling, forbearance program, payment matching program, settlement agreement, running a skip trace search, legal action, multiple smaller payments, or combinations thereof.

43. The computer-implemented method of claim 42, wherein the treatments are defined by a time duration, numeric value, frequency or combinations thereof.

44. The computer-implemented method of claim 43, wherein the time duration is selected from a group consisting essentially of days, weeks, months, quarters, years or combinations thereof.

45. The computer-implemented method of claim 43, wherein the numeric value is a settlement percentage.

46. The computer-implemented method of claim 43, wherein the frequency is the number of times the treatment is repeated within a fixed time period.

47. The computer-implemented method of claim 27, wherein the cost comprises an operational monetary cost and a relationship cost.

48. The computer-implemented method of claim 47, wherein the operational cost is assigned by the user or the system based on an analysis of historical data.

49. The computer-implemented method of claim 47, wherein the relationship cost is assigned by the user or the system based on an analysis of historical data.

50. The computer-implemented method of claim 27, wherein the fitness score comprises performance measures fed into a fitness function which summarizes the performance measures into a single score.

51. The computer-implemented method of claim 50, wherein the performance measures are selected from a group consisting of the average cost incurred on the delinquent accounts through the time the delinquent account exited collections, the recovery amount of the delinquent account, time to recovery on the delinquent account, number of payments, the final outcome on an account, recovery success percentage for the delinquent account, partial payments, completion of payment programs or combinations thereof.

52. The computer-implemented method of claim 50, wherein the fitness function comprises the overall average for the entire historical group and the average for the neighborhood group, weighted by the number of historical accounts in the neighborhood group.

53. A user interface for a computer program to gain-insight to the operations or fine-tune rules for a recommender engine for recommending one or more treatments for delinquent accounts, the user interface comprising:

a processor operable to execute programs;

a memory coupled to the processor;

the memory comprising reference data for accounts;

a database associated with said processor and said memory;

a program comprising one or more sub-models;

an analysis program stored in the memory, executable by the processor, the analysis program being operable to accessing the reference data for the one or more accounts;

accessing the one or more sub-models;

expressing the reference data for historical accounts in the one or more sub-models as n-feature vectors in an n-dimensional feature space;

reducing the n-dimensional feature space;

selecting at least one of the n-feature vectors;

generating a report about treatments and their efficacy for the selected at least one of the n-feature vectors; and

displaying one or more fields for fine tuning rules for the treatments and the selected at least one of the n-feature vectors.

54. The user interface of claim 53, wherein the n-dimensional feature space is a 3-dimensional feature space.

55. The user interface of claim 53, wherein reducing the n-dimensional feature space comprises selecting the most critical dimensions.

56. The user interface of claim 53, wherein reducing the n-dimensional feature space comprises using or more dimensionality reduction analytic techniques.

57. The user interface of claim 53, wherein the one or more rules are selected from a group consisting of the policies of the user, any applicable governmental regulations, financial industry regulations or combinations thereof.

58. The user interface of claim 53, wherein the reference data comprises account demographics, treatments, model scores, account properties, account behaviors, days past due, the final collections result on the account or combinations thereof.

59. The user interface of claim 53, wherein the reference data comprises multiple entries for the historical accounts data at different points in time.

60. The user interface of claim 53, wherein the sub-models models are built using one or more models selected from a group consisting of neural networks, regression analysis, integrated rules systems, decision tree models, other statistical models or combinations thereof.

61. The user interface of claim 53, wherein the treatments are selected from a group consisting of a letter sent by mail to the debtor'"'"'s home, a letter sent by mail to the debtor'"'"'s work, an email message sent to the debtor'"'"'s email address, an SMS message sent to the debtors telephone, a phone call made by a collection specialist to the debtors home, a phone call made by a collection specialist to the debtors office, no action, restructuring the delinquent account, utilizing an automatic/automated agent, resending the bill, debt counseling, forbearance program, payment matching program, settlement agreement, forgive a portion of the delinquent account, running a skip trace search, legal action, multiple smaller payments or combinations thereof.