In the real world, unlikely things happen, and likely things fail to happen, be it in investing, lending, or life in general.
Lending, or investing, involves monitoring the future. However, the future is unknown, implying various degrees of risk possibility. Taking up a particular risk should generate returns that match the level of the risk. Now, how do you calculate that risk when you cannot know the future?
The future is filled with probability distributions. The number of variables is overwhelming. Based on the data available, experts can project a range of possibilities. As the range and reliability of the data increases, so does the accuracy of estimations. Econometrics paves way for scientific tools that can estimate the future behavior of assets, investments, and borrowers with a certain level of accuracy. The tools keep evolving for more efficiency and newer sources of individual and institutional data related to finance.
Risk-adjusted loan pricing (RLP) is a global practice, by which more creditworthy customers get cheaper loans. Borrowers with greater chances of defaulting pay higher interest rates and processing fees. The calculations consider capital cost of the loan amount, operational costs, expected returns, and projected chances of default, and losses, thereof (risk costs).
Creditworthiness of borrowers is calculated using data related to their financial behavior, borrowing patterns, default history etc. Apart from repayment history, the records of paying one’s utility bills also affect creditworthiness. Using such components is of advantage during risk calculations. Risk-adjusted selling gets more scientifically implemented once the calculations are understood.
The three advantages of credit risk modeling
- Credit risk modeling works in favor of penny-wise borrowers. On the other hand, with a flat rate, financial institutions divide the total lending cost equally among all the borrowers. It results in thrifty people, who are careful to avoid defaults, bearing the burden of extra charges incurred due to risk costs. They arise due to reckless borrowers who might fail to repay on time. Not charging them a proportionally high interest rate creates non-performing assets (NPA) for the financial institution.
- People with low credit scores can at least borrow, even if the rates are higher for them. Without credit risk modeling, the applicants simply get classified as ineligible. Efficient big-data analysis, statistical modeling, and risk-adjusted pricing enable firms to offer loans at aptly increased prices after considering the risk cost.
- The third business advantage is that of risk-avoidance, by which your institution can take calculated risks to earn higher returns without being reckless.
Now, how do financial institutions use credit risk modeling to their benefit?
Let us work it out in a reverse manner. To offer risk-adjusted prices, you need to know the risks. To know the risk, you need to analyze data, and to analyze the data with some degree of correctness, you need sufficient data.
Data may be captured through your processes and systems. Plus, you can buy data or reports from credit rating agencies like TransUnion.
For effective credit-risk calculations, financial institutions need to:
- Build and maintain a strong database of client transactions. It should update periodically, monitor, and analyze all relevant data for insights.
- Develop a credit-scoring system that relies on the captured data. The system should consider sectorial, behavioral, financial, and macroeconomic factors for the scoring system.
The database, analytics, and scoring system should be customizable for a wide range of operations and scenarios. Today, even individual lenders, small and medium enterprises, and large-scale multinational corporations use customizable software for that very reason – flexibility.
A ship in a harbor is safe, but that is not what ships are built for. Avoiding risky clients is safe, but that is not what financial institutions are made for. Being able to perform risk-adjusted selling multiplies your agility and business potential manifold. Given the hectic nature of modern-day banking, businesses have two options: One, reach out with the support of a robust system for credit scoring and risk-adjusted selling. Two, slide back into the traditional risk-avoidance model and face extinction. Make your choice!
Preethi vagadia is currently a Senior Business architect with the Service operations practice at a well-known IT Industry in Bangalore. She has worked in several process improvement projects involving multi-national teams for global customers. She has over 8 years of experience in Mortgage Technology and has successfully executed several projects in Logistics management, Logistics Integration, Reverse logistics, Warranty software and Programmatic Solutions.