Credit risk modelling is a financial concept where models are created to calculate the chances of a borrower defaulting on his credit repayment. An example is an individual who has taken a credit card in his name; the risk model will speculate if and how he will default on the monthly card payments. And if he does, the total amount that he owes and the total loss to the lender is also calculated.
The use of this type of models that are created using historical data to gauge the probability of a credit default is known as credit risk modelling. It is an important resource for banks and financial institutions to check the credit holding capacity of individuals and businesses. The goal is to prevent losses.
How Does Credit Risk Modelling Define Borrowers?
A risk model essentially divides customers (borrowers) into two types:
Bad borrower
- Has defaulted their payments several times over a short period of time
- Has filed for bankruptcy
- Last payment was more than 90 days ago
- Associated accounts being inactive
Good borrower
Anyone who does not fall under the ‘bad borrower’ category is placed here
It should be noted that this classification is general in nature. Different organizations have different credit risk models. In some cases, the calculation mechanism of the models can also differ.
How Does It Work?
Credit risk modelling uses two methods to estimate the probability of a defaulting. Here they are:
In Judgmental Method, several factors defining the borrower are assessed. These also help in creating credit scores.
- Credit history of the borrower
- The difference of assets and liabilities of the borrower
- Presence and value of the collateral such as property or gold
- External factors such as recession
- Borrower’s sources of income
Credit professionals look at these parameters to get an idea about the borrower. If none of the parameters yields a positive rating, the credit is usually denied.
On the other hand, in the Statistical Method, as the name suggests, statistics and historical data are used to conclude the credit capacity of a borrower. The advantage of this method is that it does not include the factor of bias in its calculation. Borrowers can be sure that they got an unbiased assessment.
Sources of Data for Credit Risk Modelling
Since it is not possible to collect data on a case-by-case basis, organizations depend on a variety of sources for this task. There are three main sources of data in this type of modelling:
- Demographic – Personal details that are easy to collect as the borrower will furnish them during application (loan or credit card)
- Individual History – The historical data about that specific borrower will be collected through partner agencies. If the borrower already has another account in the bank, this data and associated behaviour are also used
- Credit Bureau – Credit score and other details are sourced from a central database
Credit risk modelling helps banks and financial institutions keep their money lending processes in check. It is one of the most reliable methods to prevent fraud.
Also Read: What is Credit Risk Management
