Method and Apparatus for Assessing Debtor Payment Behavior

1 . A computer readable medium containing a program which, when executed by a processor, performs a method for modeling debtor behavior, comprising:

obtaining historic customer placement data for each of at least one debtor in a debt portfolio;

obtaining historic credit data for each of the at least one debtor in the debt portfolio;

determining a probability of payment model by processing the historic customer placement data and historic credit data according to either of (1) a generalized linear modeling technique with a link function based upon a Generalized Beta of the Second Kind (GB2) family of distributions or (2) a generalized linear modeling technique using a link function based upon a member of the G-and-H family of distributions; and

storing, in a memory, values corresponding to the probability of payment model.

2 . The method of claim 1 , further comprising:

determining an expected conditional sum of payments model by processing the historic customer placement data and historic credit data according to either of (1) a generalized linear modeling technique using a member of the natural exponential family or (2) a maximum likelihood estimation fit to a member of the GB2;

storing, in a memory, values corresponding to the expected conditional sum of payments model.

3 . The method of claim 2 , further comprising:

determining an expected monetary amount using the probability of payment and the expected conditional sum.

4 . The method of claim 1 , further comprising:

determining an expected monetary amount either directly using the Tobit model or using the Tobit model with the probability of payment model as an input to the Tobit model.

5 . The method of claim 1 , wherein the probability of payment model is determined using one or both of logistic regression and probit regression.

6 . The method of claim 1 , wherein the modeling techniques comprise one or more of a neural network processing technique, a linear regression technique, a discriminant analysis technique and a random forests technique.

7 . The method of claim 1 , wherein the generalized linear modeling uses one or more of Normal, Poisson, Gamma, Inverse Gaussian, Negative Binomial, Logarithmic and Compound Poisson/Gamma distributions.

8 . The method of claim 5 , wherein the generalized linear modeling technique is further adapted according to a complexity penalty criterion.

9 . The method of claim 8 , wherein the complexity penalty criterion comprises a Schwartz Bayes Criterion.

10 . The method of claim 1 , wherein the historic credit data comprise credit bureau data.

11 . The method of claim 1 , wherein the debt portfolio is associated with a type of debt.

12 . The method of claim 11 , wherein the type of debt comprises one or more of vehicle loan debt, education loan debt, medical debt, credit card debt, trade credit, health club debt, mortgage debt and tax debt.

13 . The method of claim 1 , further comprising:

using the probability of payment model to process current customer placement data and current credit data associated with a second debt portfolio to determine thereby a probability of payment for each of at least one account in the second debt portfolio.

14 . The method of claim 13 , wherein:

each account of the second debt portfolio is associated with a collection score calculated as its respective probability of payment multiplied by 100 ; and

a Collection Score for the second debt portfolio is calculated as the sum of all the collection scores.

15 . The method of claim 13 , further comprising:

establishing a collection rating as a function of the probability of payment for the accounts in the second debt portfolio.

16 . The method of claim 15 , wherein the probability ratings of the accounts in the second debt portfolio are divided into a plurality of ranges.

17 . The method of claim 16 , wherein the ranges are determined using a bell curve, division into deciles, Fibonacci sequences of the score interval endpoints or a Jenks optimization method.

18 . The method of claim 3 , further comprising:

using the expected monetary amount model to process current customer placement data and current credit data associated with a second debt portfolio to determine thereby an expected conditional sum of payments for each of at least one account in the second debt portfolio.

19 . The method of claim 18 , wherein:

each account of the second debt portfolio is associated with a Monetary Score calculated as its respective expected payment amount; and

a Monetary Score for the second debt portfolio is calculated as the sum of all the Monetary Score.

20 . The method of claim 18 , further comprising:

establishing a monetary rating as a function of the expected payment amounts for the accounts in the second debt portfolio.

21 . The method of claim 20 , wherein the monetary ratings of the accounts in the second debt portfolio are divided into a plurality of ranges.

22 . The method of claim 21 , wherein the ranges are determined using a bell curve, division into deciles, Fibonacci sequences of the score interval endpoints or a Jenks optimization method.

23 . The method of claim 2 , further comprising:

using the probability of payment model to process current customer placement data and current credit data associated with a second debt portfolio to determine thereby a probability of payment for each of at least one account in the second debt portfolio; and

using the expected conditional sum of payments model to process current customer placement data and current credit data associated with a second debt portfolio to determine thereby an expected conditional sum of payments for each of at least one account in the second debt portfolio.

24 . The method of claim 23 , further comprising:

ranking by an expected monetary amount determined using the probability of payment and the expected conditional sum associated with the second debt portfolio to determine a prioritization of collection activity.

25 . A computer readable medium containing a program which, when executed by a processor, performs a method for using at least one of the debtor behavior models of claim 2 , comprising:

receiving customer placement data associated with a second debt portfolio;

obtaining credit data for at least a portion of the debtors within the second debt portfolio;

processing the received customer placement data and obtained credit data according to a debtor behavior model; and

storing, in a memory, values corresponding to an expected monetary amount for the portion of the debtors within the second debt portfolio.

26 . The method of claim 25 , further comprising:

determining a total value of the second debt portfolio.

27 . The method of claim 25 , further comprising:

prioritizing the accounts in the second debt portfolio using one or both of probability of payment and expected payment amount to improve a recovery strategy.

28 . The method of claim 25 , further comprising:

grouping the accounts in the second debt portfolio using one or both of probability of payment and expected payment amount to improve a recovery strategy.

29 . The method of claim 1 , wherein:

the processing the historic customer placement data and historic credit data includes a bivariate analysis utilizing a plurality of analysis variables including the data elements within the historical customer placement and credit data and additional analysis variables created using numerical or categorical data elements.

30 . The method of claim 29 , wherein:

the bivariate analysis further utilizes demographic data.

31 . The method of claim 1 , wherein the placement data comprises an amount and age for each receivable.

32 . A computer readable medium containing a program which, when executed by a processor, performs a method for determining a debtor behavior model, comprising:

obtaining one or both of historic customer placement data for each of at least one debtor in a debt portfolio and historic credit data for each of the at least one debtor in the debt portfolio;

processing the one or both of historic customer placement data and historic credit data according to a generalized linear modeling technique with a Generalized Beta of the Second Kind distribution to determine thereby a model describing a maximum likelihood estimation of at least one of a probability of payment and an estimated payment amount for each account in the debtor population; and

storing, in a memory, values corresponding to properties of the determined model.

33 . A computer readable medium containing a program which, when executed by a processor, performs a method for modeling debtor behavior, comprising:

obtaining historic customer placement data for each of at least one debtor in a debt portfolio;

determining a probability of payment model by processing the historic customer placement data according to either of (1) a generalized linear modeling technique with a link function based upon a Generalized Beta of the Second Kind (GB2) family of distributions or (2) a generalized linear modeling technique using a link function based upon a member of the G-and-H family of distributions; and

storing, in a memory, values corresponding to the probability of payment model.

34 . The method of claim 33 , further comprising:

determining an expected conditional sum of payments model by processing the historic customer placement data according to either of (1) a generalized linear modeling technique using a member of the natural exponential family or (2) a maximum likelihood estimation fit to a member of the GB2;

storing, in a memory, values corresponding to the expected conditional sum of payments model.

35 . The method of claim 34 , further comprising:

determining an expected monetary amount using the probability of payment and the expected conditional sum.

36 . A computer readable medium containing a program which, when executed by a processor, performs a method for modeling debtor behavior, comprising:

obtaining historic credit data for each of at least one debtor in a debt portfolio;

determining a probability of payment model by processing the historic credit data according to either of (1) a generalized linear modeling technique with a link function based upon a Generalized Beta of the Second Kind (GB2) family of distributions or (2) a generalized linear modeling technique using a link function based upon a member of the G-and-H family of distributions; and

storing, in a memory, values corresponding to the probability of payment model.

37 . The method of claim 36 , further comprising:

determining an expected conditional sum of payments model by processing the historic credit data according to either of (1) a generalized linear modeling technique using a member of the natural exponential family or (2) a maximum likelihood estimation fit to a member of the GB2;

storing, in a memory, values corresponding to the expected conditional sum of payments model.

38 . The method of claim 37 , further comprising:

determining an expected monetary amount using the probability of payment and the expected conditional sum.