Method and apparatus for a model assessing debtor behavior

1. A non-transitory computer readable medium containing a program which, when executed by a processor, performs a method comprising:

aggregating at least one of account receivable data and payment data of a debtor (i) from each of a plurality of creditors (j);

using the aggregated debtor data and debtor data from the plurality of the creditors (j), determining a probability of a credit or collection event (P(CCE ij )) of the debtor (i) with respect to at least one creditor of the plurality of the creditors (j), wherein the P(CCE ij ) is determined as a function of (AR ij , BCDI i1 , BCDI i2 , . . . BCDI iJ ); where (AR ij ) comprises accounts receivable data for debtor (i) with creditors (j); and (BCDI ij ) comprises Business Credit Data Interchange data elements for debtor (i) across j creditors in the BCDI;

storing, in a memory, values corresponding to the determined (P(CCE ij )).

2. The non-transitory computer readable medium of claim 1 , wherein P(CCE ij ) is also a function of (APP ij ); where (APP ij ) comprises internal data for debtor (i) with creditor (j).

3. The non-transitory computer readable medium of claim 1 , wherein P(CCE ij ) is also a function of at least one of third party data on debtor (i) selected from at least one of a credit bureau, a demographic data source or a private data source.

4. The non-transitory computer readable medium of claim 1

making a credit decision utilizing the stored values corresponding to the (P(CCE ij )).

5. The non-transitory computer readable medium of claim 1 , further comprising:

receiving a creditor inquiry regarding a particular debtor; and

evaluating expected behavior of the particular debtor with respect to the inquiring creditor according to the determined P(CCE) values stored in the memory.

6. The non-transitory computer readable medium of claim 5 , wherein the method further comprises:

providing to the inquiring creditor a report regarding the evaluation of the debtor.

7. The non-transitory computer readable medium of claim 1 , wherein the account receivable data further comprises total outstanding balance across creditors, amount past due across creditors, amount of write-off (loss) across creditors, amount placed for collection across creditors, and average days past due across creditors.

8. The non-transitory computer readable medium of claim 1 , wherein the method further comprises:

calculating an expected utility loss (EUL) of a debtor with respect to at least one creditor according to the following equation:

EUL ij =P (CCE ij )*UL ij ; where

utility loss (UL) is a function of one or more of balance outstanding and collection effort costs.

9. A non-transitory computer readable medium containing a program which, when executed by a processor, performs a method comprising:

adapting a debtor score at a first creditor model in response to account receivable data and payment data of the debtor received from a second creditor model, each of the creditor models being generated according to respective Business Credit Data Interchange (BCDI) groups, wherein the debtor score comprises a probability of a credit or collection event (P(CCE ij )) of the debtor (i) with respect to at least one creditor of a plurality of the creditors (j), wherein P(CCE ij ) is a function of (AR ij , BCDI i1 , BCDI i2 , . . . BCDI iJ ); where (AR ij ) comprises accounts receivable data for debtor (i) with creditor (j); and (BCDI ij ) comprises Business Credit Data Interchange data elements for debtor (i) across all J creditors in the BCDI;

storing, in a memory, values corresponding to the debtor score.

10. The non-transitory computer readable medium of claim 9 , wherein P(CCE ij ) is also a function of at least one of (APP ij ), third party data on debtor (i) selected from at least one of a credit bureau, a demographic data source or a private data source, where (APP ij ) comprises internal data for debtor (i) with creditor (j).

11. The non-transitory computer readable medium of claim 9 , wherein the method further comprises:

providing the values corresponding to the debtor score to a requester for making a credit decision utilizing the stored values corresponding to the (P(CCE ij )).

12. The non-transitory computer readable medium of claim 9 , wherein the method further comprises:

generating the account receivable data by performing a bivariate statistical analysis upon the accounts receivable data elements within the BCDI group.

13. The non-transitory computer readable medium of claim 9 , further comprising:

applying one or more transformations to the account receivable data to create predictive variables, the one or more transformations comprising any of logs, truncations, censoring, variances, averages, measures of volatilities, compound variables, missing variable assignments and the creation of dichotomous variables.

14. The non-transitory computer readable medium of claim 13 , wherein:

said first creditor model is generated by processing candidate predictive variables in association with the dependent variables according to a selection technique.

15. The non-transitory computer readable medium of claim 9 , wherein the respective BCDI groups further comprise data relating to total outstanding balance across creditors, amount past due across creditors, amount of write-off (loss) across creditors, amount placed for collection across creditors, and average days past due across creditors.

16. A method for determining a probability of a credit or collection event comprising:

accessing at least one of account receivable data and payment data of a debtor (i) from each of a plurality of creditors (j);

using the accessed debtor data and debtor data from the plurality of the creditors (j), determining a probability of a credit or collection event (P(CCE ij )) of the debtor (i) with respect to at least one creditor of the plurality of the creditors (j), wherein the P(CCE ij ) is determined as a function of (AR ij , BCDI i1 , BCDI i2 , . . . BCDI iJ ); where (AR ij ) comprises accounts receivable data for debtor (i) with creditors (j); and (BCDI ij ) comprises Business Credit Data Interchange data elements for debtor (i) across all J creditors in the BCDI; and

storing, in a memory, values corresponding to the determined (P(CCE ij )).

17. The method of claim 16 , wherein P(CCE ij ) is also a function of at least one of (APP ij ), third party data on debtor (i) selected from at least one of a credit bureau, a demographic data source or a private data source, where (APP ij ) comprises internal data for debtor (i) with creditor (j).

18. The method of claim 16 further comprising:

making a credit decision utilizing the stored values corresponding to the (P(CCE ij )).

19. The method of claim 16 further comprising:

receiving an inquiry regarding a particular debtor from a requester; and

providing the requester with the values corresponding to the determined (P(CCE ij )) with respect to the particular debtor.

20. The method of claim 16 further comprising:

evaluating an expected behavior of a particular debtor according to the values corresponding to the determined (P(CCE ij )).

21. The method of claim 16 , wherein the account receivable data further comprises total outstanding balance across creditors, amount past due across creditors, amount of write-off (loss) across creditors, amount placed for collection across creditors, and average days past due across creditors.

22. The method of claim 16 , further comprising:

calculating an expected utility loss (EUL) of a debtor with respect to at least one creditor according to the following equation:

EUL ij =P (CCE ij )*UL ij ; where

utility loss (UL) is a function of one or more of balance outstanding and collection effort costs.