IN-HOUSE CREDIT SCORING TECHNIQUES
The need for credit scoring
The Need for Credit Scoring:
The credit score is a retail credit technique that helps the lender to evaluate credibility and worthiness of an applicant to determine if he or she is credit worthy. The score also helps the credit officer to decide the amount of credit that could be granted to the customer. The duration of the credit and the interest rate are also dependent of the score.
Simply put, the higher the credit score, the larger the amount of credit that can be granted, the longer the duration offered, and the lower the interest rate.
On the other hand, the lower the credit score, the lower the amount of credit that can be granted, or it can even result in a rejection of the credit request. A lower credit score will likely translate into a shorter loan duration and a higher interest rate.
The credit score can also be used to determine the need for collateral. Where customers have a high score, lenders may decide that they do not require collateral. However, if a customer has a low score, the lender may decide to issue a loan only with collateral. The level of the score will also determine the amount of the monthly installment.
| High Credit Score | Low Credit Score | |
| Amount of credit | High | Low |
| Duration of credit | Long | Short |
| Interest rate | Low | High |
| Collateral | NA | Needed |
| Terms and Conditions | Soft | Hard |
| Instalment | Low | high |
Let’s take an example. Assuming the lender decides that a credit score of 85% or greater is the highest credit rating, the policy of the lender allows that a personal loan would be given up to eight times the borrower’s monthly income. The duration is up to five years and the rate of interest rate is 5 percent.
If a customer has a score between 65% and 84%, the lender allows a personal loan only up to six times the customer’s monthly income, and only up to three years at a 4% rate of interest.
Forming Scoring Tables & Matrices
Score cards and credit scoring systems collect data about the applicant of the credit from two resources. The first is the account application form and the second is the credit application form. The required data from these forms is fed into the system to reach a final score for the credit applicant. The scoring system includes valuable data about the applicant such as the level of source of income, the debt burden, the utility bills, the employment, the employer, the length of service, and education.
In some countries, the use of data on gender, age and marital status is permitted, while this in some countries such as the USA, the collection or use of such information is not permitted.
Each piece of information is awarded certain points which represent the value of each element based on analysis and statistics of previous years.
This helps predict who is most likely to repay a debt. The total number of points (which is the credit score) helps to predict the creditworthiness of the applicant, and how likely he or she will be to repay a loan and make the payments when they’re due.
The scores are weighted by numbers based on the statistics collected from the IT department of the bank/financial institution.
For example, the credit manager would request the IT department to print a report for the delinquency cases for the last year or last three years sorted by ages. The manager may observe that the age group of 21-24 years represented the highest delinquency rate in the previous year. Therefore, the range of age 21-24 may be given a very low score for this element. The manager’s observes that the age group of 27-29 year is better than the younger age, and keeps improving until 40 years old and then starts reversing towards high risk up to 60 years old. The credit manager would fix these weighted numbers based on this information will assign the highest score to the age between 30 and 40 years after which risk begins to increase again according the graph below:
Let’s look at anotherexample. The credit manager requests the IT department to print a report for delinquency cases over the last year or last three years average, sorted by source of income. He or she may observe that customers with a monthly salary of $3,000 were the highest risk and have contributed to the highest number of default/delinquency cases.
The manager may have observed also that with the growth of salary towards US$15,000, there were improvement of risk factor, reaching the best scenario between US$15,000 and US$25,000 then after that started going up again showing more delinquency until the level of $100,000 and greater. Having observed these factors, the credit manager would assign weighted numbers that represent the level of risk at each category.
The credit manager fixes a weighted number to the element itself such as the income, age, employment, or employer.
The following graph shows that the higher an employee goes up in his or her hierarchy, the fewer chances of available jobs in the market. Therefore, at a very high level in the corporate hierarchy, the risk to an employee of losing their job and not finding a suitable replacement job is higher. These facts would be considered while fixing the weighted levels and numbers.
