Selection of the sample 23

The information about “bad” and “good” credit borrowers was gathered for the period from the year 2001 until the beginning of 2005. To build the initial credit scoring model this information was collected from the Area Managers of FcA. Credits applications of good and bad customers were selected from the FcA archive. In total, I have selected the initial sample of 1037 companies from 13 countries. After the careful consideration this sample was decreased to 862 companies13, due to the fact that some applications were incomplete or for their assessment previous version14 of the FcA credit scoring model was used.

4.1.1 Definition of “good” credit

The next criteria were used for the selection of “good” customers from the FcA database:

(1)the last payment was done not later than 2003-01-01, (2)a customer did already at least 8 credit payments to FcA,

(3)customer’s indebtness to FcA is not more than 300 Euro according to the +30-list15 ,

(4)total weighted number of days late is not more then 3 days16.

The list of “good” customers was obtained by the observation of all payments for all customers.

13 _{See Appendix III}

14 _{Some credit applications were analyzed by the old version of FcA credit scoring model. In}

order to be consistent, I’m using for the building of a new credit scoring model credit applications of the companies , which were assessed by the last version of the FcA credit scoring model, which was created in the end of 2001

15 _{The +30-list is the list of customers who have indebtness by the current credit payments}

more than 30 days. The sum 300 Euro was selected in order don’t allow bank’s service costs affect companies which were filtered out as good ones

The list of “good” customers was filtered out in the next sequence of operations with the help of database management tools:

(1)all companies with the last date of payment not less then 2003-01-0117, (2)all customers who hadn’t made at least 8 payments in total in all open

contracts with FcA were filtered out from the list created on the stage 1, (3)all customers who had more than 300 Euro indebtness to FcA on the

+30-list were filtered out from the list created on the stage 2,

(4)all customers with the total weighted number of days less than 3 days were filtered out from the list created on the stage 3.

4.1.1.1 Problem of the calculation of the total weighted number of days late in payments

The approach to calculate the total weighted number of days late in payments was suggested by FcA’s manager in order to make the selection criteria for “good” group more rigorous.

When we had started to follow this algorithm of “good” credit application finding, we faced with the problem what to do if a customer made some of its payments earlier than their due dates and some credit payments were made with delays. How we can treat this customer? It was suggested by FcA’s managers to consider also a money value of the each particular credit payment transaction, not only the time of payment. Below you can see the following example of their reasoning:

N Days late for the credit payments Paid amount (€) Share of payment in total sum of payments Weighted number of days late 1 5 1200 13,00% 0,65 2 12 578 6% 0,72 3 3 7426 80% 2,4 4 27 133 1% 0.27

Total sum of payments 9337 100%

Total weighted number of days late18

4,04

Table 1: Total weighted number of days late in payments - Example 1

The sum of the each payment transaction and the time of payment were taken into account. In the provided example you can see that the weighted number of days late is more if we received 7426€ with the delay of 3 days than when we got the payment of 133€ with the delay of 27 days.

Sometimes FcA’s customers are making credit payments in advance (Table 2: Example 2). In this case, we have a negative number of days late for the credit payments, because customer made the first payment 10 days earlier than the due date. And the second payment was made with delay of 10 days. Total weighted number of days late is 0 in this case, and this customer is considered by our algorithm of selection as “good”.

18 _{Weighted number of days late = Days late for the credit payments * Share of payment in}

N Days late for the credit payments Paid amount (€) Share of payment in total sum of payments Weighted number of days late 1 -10 1000 50% -5 2 10 1000 50% 5

Total sum of payments 2000 100%

Total weighted number of days late

0

Table 2: Total weighted number of days late in payments - Example 2

This algorithm has one important drawback that a customer, who is paying in advance, is considered as “good”. In reality, there should be other reasons for an early payment, for instance, customer’s desire to spend an excess of cash on hand or some seasonal fluctuations of payments.

When we have calculated the total weighted number of days late, we did not separate the payments by contract, only by customer. All payments value dates made by a customer where compared to their respective due dates irrespectively of which contract it was made for.

4.1.2 Definition of “bad” credit

The credit supposed to be “bad”, if the history of relationships with the particular customer, who has bought commercial vehicles on credit, contains the one of the next historical facts:

(1)vehicles which were purchased on credit are repossessed, (2)there were delays in credit payments more than 180 days, (3)FcA brought a suit against this particular customer.

After having created both “bad” and “good” debt lists, FcA’s Area Managers were asked to filter out customers that appeared on the lists mistakenly due to the technical errors19 of the FcA’s information system.