Introduction to Credit Risk Management
People who have home loans, personal loans, car loans, and own credit cards are facing unprecedented times. The borrowers are confined to their homes for the past couple of months in the world’s strictest lockdown. The engine that runs the world’s economy is idling with just essential spending. People and businesses across are conserving cash and shelving discretionary spending. The borrowers are anxious if their jobs are safe from being laid off. Banks who have lent these borrowers are getting anxious if the money they have lent will ever come back to them. The bank’s credit risk department will be in the spotlight as they analyze reams and reams of customer data and macroeconomic indicators for measuring the credit risk across individuals and portfolios(car loans, agriculture loans, personal loans, car loans, credit cards, home loans).
Credit risk is the risk of default on a debt that arises from a borrower failing to make required payments. In a downturn like the one we are facing now, every contributor to the economy be it the individual or the business are under severe stress due to their inability to service the debts. Ascertaining the right provision to be made for bad loans or NPAs (Non-Performing Assets) to ensure that the banks get prepared to weather the storm is a job that falls in the charter of the credit risk department.
There are various avenues to manage credit risk. We all would have at some point in time, seen messages pop up by our mobile operator and banks alike, stating the credit limit has been increased by a certain amount as a preferred customer or to reward loyalty. You might think that there is an employee from the company who is looking at each and every customer payment history and augmenting the credit limit, but the fact is that it is being done with the help of Artificial Intelligence (AI) and Machine Learning (ML). It is clever enough to understand from the database on customer behavior and deduce the proportion of customers that can be trusted for timely payment and those customers who can be flagged off as possible defaulters.
The second way to manage credit risk is to seek collateral from the loan seekers. Collateral acts as a security to the lender in case the debtor defaults on servicing the loans. The traditional collateral is property or jewelry but what if a big enterprise is raising debt and giving its market investments as collateral? The market investments can be complex derivative or illiquid instruments, how does the lender ascertain the value? Here enter the AI / ML experts who train machines to value these complex market instruments.
The third is the credit scoring mechanisms. Institutions like CRISIL, CIBIL have independent scores on borrowers which the lenders can leverage before advancing the loan. Institutions like CRISIL have automated models that track the credit history of millions of borrowers across different classes of loans. If homeowners default on a monthly installment, the automated models automatically flag the borrowers to be watched. If the borrower defaults on his payments for three straight months(90 days), he is classified as a defaulter. The scoring mechanism will then auto-update the credit score to a lower figure. This updated credit scoring is immediately available to be seen to all the current lenders or prospective lenders. These days one might have seen advertisements from banks, credit cards about instant approval or loan within 24 hours. Such aggressive lending is possible only by technologies like AI / ML and data analytics.
On the other side of lending to the commercial borrowers, the in house credit scoring teams analyze qualitative and quantitative data. They devise mechanisms to calculate ratios as interest coverage ratio(number of times the interest payments can be serviced by the earnings of the company in the referenced period). Healthy interest coverage of 6 times can mean the company can service debt payment for six months from its current earnings. This ratio has to be updated by the lender periodically as the borrower declares quarterly earnings. Any adverse scenarios causing the earnings to be depleted in a particular period could mean the coverage ratio may fall below acceptable limits. A large lender could set up an auto-trigger mechanism to flag all such borrowers. On the qualitative side, the credit scoring mechanism might flag events such as key management personnel(KMP) departure which could adversely impact the ability of the enterprise to service current debts or raise money at competitive rates in the future.
Lenders also closely monitor the loan book at the portfolio level – Agriculture, Personal, Car, Home, Credit Card, Commercial. The macro indicators of the economy can have a bearing on the repayability of the borrowers in each of these industries. A bad monsoon can push farmers to distress and can cause defaults in the Agri loan books. A pandemic like Covid-19 can have an impact across the spectrum. So the credit risk team monitors a set of indicators that can foretell the lender which investments can turn risky. This will help the lender create provisions before the loans turn bad. Today, AI can analyze and interpret macro-economic indicators and predict with increasing accuracy on the sectors that can turn risky. Even before the COVID-19 outbreak, our economy was hit by a perfect storm in the banking sector with many borrowers defaulting on loans. As result lenders have been extremely cautious of lending to risky borrowers. Banks that have leveraged technology to the fullest and which have credit risk professionals with strong domain knowledge have had much success in their ability to forecast and successfully change their lending strategy to suit the needs of the time.
How to become Credit Risk Professional?
I)An analytical mindset with an academic background in Engineering, Finance, or Accounting.
II)Knowledge of the Banking industry.
III)Ability to leverage technology to solve problems.
IV)Qualification/experience in data analytics.
V)Understanding of macroeconomics.
