Question

Nast stores has derived the following consumer credit-scoring model after years of data collect Y=(0.20 x Employment) + (0.4 x Homeowner) + (0.3 x Cards)

Employment = 1 if employed part-time, and 0 if unemployed

Cards= 1 if presently has 1-5 credit cards, 0 otherwise

Nast determines that a score of at least 0.70 indicates a very good credit risk, and it extends credit to these individuals. (each letter below is a separate question, answer a-d)

PLEASE SHOW ALL WORK

A. If Janice is employed part-time, is a homeowner, and has six credit cards at present, does the model indicate she should receive credit?

B. Janice just got a full-time job and closed two of her credit card accounts. Should she receive credit? Has her credit worthiness increased or decreased, according to model?

C. Your boss mentions that he just returned from a trade-association conference, at which one of the speakers recommended that length of time at present residence (regardless of homeownership status) be include in credit-scoring models. If the weight turns out to be 0.25, how do you think the variable would be coded (i.e., 0 stands for what, 1 stands for what, etc)?

D. Suggest other variables that associated might have left out of the model, and tell how you would code them (i.e., 0,1,2 are assigned to what conditions or variables?).

Answer 1

A.

Employment = Part - time i.e. 1

Homeowner = Yes i.e. 1

Credit Cards = 6 i.e. 0

Y = 0.2 * Employment + 0.4 * Homeowner + 0.3 * Cards

Y = 0.2 * 1 + 0.4 * 1 + 0.3 * 0 = 0.6

Since, it is less than threshhold of 0.7, it indicates that it is not so very good credit risk and Jannice should not be given loan as per this credit scoring model.

B.

Employment = Full - time i.e. 1 (Since for Full Time employment, information is missing, we are assuming that full time is better than part time so, it should also be given 1 for Employment)

Homeowner = Yes i.e. 1

Credit Cards = 4 i.e. 1

Y = 0.2 * Employment + 0.4 * Homeowner + 0.3 * Cards

Y = 0.2 * 1 + 0.4 * 1 + 0.3 * 1 = 0.9

Since, it is greater than threshhold of 0.7, it indicates that it is very good credit risk and Jannice should be given loan as per this credit scoring model. Her credit worthiness has improved as a result of closing down of 2 credit cards, now she hold only 4 credit cards which is treated better than having 6 cards. For full time it is even better than part time but we have treated that equally. So, Jannice creditworthiness has improved and now we can provide loan to her.

C.

Since having stayed on a particular residence for more than particular threshold is good for a lender as he can trace that customer easily in case the borrower defaults, we should code

1 for having stayed on a particular residence for a specific period of time

0 for not having stayed on a particular residence for a specific period of time.

This specific period of time can be analysed with the help of data collected already.

D.

1. First of all, Full time job is missed out of employment status. It also needs to be coded and given higher weight than part time because it give more confidence to lender. Thus, it can be coded as:

2 for Full Time Job

1 for Part Time Job

0 for No Job

2. Other Variables that can be included are:

Present Monthly Income (0 for less than $20,000 and 1 otherwise as higher monthly income is better for lenders.)

Checking Balance (0 for less than $2000 and 1 otherwise as higher checking balance is better for lenders.)

Security Provided for Loan (0 for No Security and 1 for Security Provided) etc....