Credit Risk Modelling - An Overview Risk is an indispensable part of the lending business. It is a complex job to pinpoint the exact likelihood a person is to default on their loan. As such, financial institutions and lenders are constantly trying to perfect the algorithm of gauging the risk posed by each customer. Credit risk modelling is a way in which lenders can understand the likelihood of repayment of a particular loan. In other words, it’s a tool to calculate the ‘credit risk’ of a borrower. What is Credit Risk? Credit risk is the probability that a borrower will default on a debt by failing to make the required payment. The lender may not receive their due interest or the principal on time, resulting in an interruption of cash flows and an increase in the cost of collection. In the worst case, some part of the loan or even the entire loan may have to be written off resulting in a loss to the lender. There are various parameters considered while calculating the credit risk such as the amount at the time of default, the expected loan worth at the time of default and the overall loss in case of a default. What Comprises Credit Risk? ● An individual borrower failing to make a payment due on a credit card, a mortgage loan, credit line, or any other personal loan ● A business or individual failing to pay a trade invoice on the due date ● A company, unable to repay fixed or floating debt ● An insolvent insurance company, unable to pay a claimed amount ● A company failing to pay an employee’s salary on the due date ● A now-bankrupt bank failing to return money that has been deposited How Big Data and Machine Learning Have Revolutionised Credit Risk Modelling Algorithms?
The traditional credit risk models are based on behavioural patterns which are reflected from the customer’s payment history, their current level of indebtedness and the average length of credit history. All these factors are collectively manifested in the consumer credit report referred by lenders to determine whether a consumer is a good candidate for a loan and to fix an apt interest rate. Older generations were taught to build a credit trail by making big-ticket purchases such as homes, furniture
and cars, then make monthly payments on time. This score determined a consumer’s creditworthiness which would then translate to the ability to qualify for a loan. As Millennials are taking over, things are not the same today. With people shifting cities & countries for jobs, investing in a house or buying a car is now no longer a priority. People rent out things and use the money on other things such as education, entertainment and global experience. Such individuals who are seldom in need of a credit card or a loan, either have a zero score, less score or not a long enough credit history to establish legit creditworthiness. In absence of a credit score, lenders have traditionally turned down requests for credit from these people and have declined to offer loan to these ‘credit invisible’ groups. On the other hand, many of these individuals have banking and non-credit product history that can be used to create a better credit risk assessment. This ‘alternate’ data can lead to greater access to credit markets for these consumers and an opportunity for lenders to offer financial products at reasonable risk as well as assess that ongoing risk during the life of the loan. Big Data and credit risk analytics have helped majorly refine the process of credit risk management in banks. Data enables you to build complex information and eventually find multiple parameters to predict whether someone will default on their loan. This eliminates the historical need of having a mandatory credit history. CRIF High Mark- One of the four credit information companies in India, provides a full portfolio of modelling tools and expertise, empowering business analysts, from beginners to advanced modellers, to develop, build, test, deploy and manage predictive models. CRIF’s expertise in predictive analytics in banking has helped develop various scoring projects in over 18 countries.