Why Loan Interest Alone Cannot Identify Expected Credit Loss
Summary
The note considers whether a lender can infer expected losses from the interest charged on retail loans and compare that estimate with official risk figures. Its central point is that the loan rate includes several components besides expected credit loss, such as funding, capital, and servicing costs. Some components can vary across loan vintages, making a direct reverse calculation unreliable without additional assumptions.
Where payment histories are available, the response recommends estimating risk from those records instead. One rough approach is to count recent missed-payment indicators; with risk scores, the lender can compare average interest rates across score groups to examine how pricing relates to risk. Alternatively, if other rate components can be estimated, they may be removed to approximate the loss component. These suggestions are practical starting points rather than a specified statistical loss model: the note gives no calibration method, portfolio results, or validation evidence, and a missed-payment count is only a rough risk proxy.
Key ideas
- Loan interest compensates lenders for funding, capital, servicing, and credit risk.
- Differences in costs across loan vintages can confound comparisons between interest rates and losses.
- Payment histories provide a more direct basis for estimating loss risk than interest charges alone.
- Recent missed-payment indicators can serve as a rough risk measure.
- Estimating the loss component from interest requires assumptions about the other rate components.
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# Calculate the implied loss rate on a loan, given the interest charged # Calculate the implied loss rate on a loan, given the interest charged My bank has a retail credit portfolio of 100 million in loans. I know the payment history,balance history of all these loans since inception. Are there any tools to calculate an expected loss, a loss distribution either at the loan level or at the portfolio level from the interest charged? My thinking is that a higher interest charged relates to a higher expected loss qualitatively. What I want to do is reverse engineer the expected loss based on the interest and see if the number matches with the official numbers from the Group Risk folks. ## Answer by Magic is in the chain (score 1, accepted) https://quant.stackexchange.com/a/45253 If you have access to the payment history then would not it be better to estimate the loss rate using historical payment data? The thing with the interest rate is it will include compensation for a lot of other factors as well, such as funding cost, capital charge, servicing cost etc. And some of these would vary by vintage, for example the funding cost would differ depending on when the loan was booked. And if you want to prove the relationship between risk and price, then why don’t you score the accounts using payment history, and then plot the average interest rate against the score. If you don’t have access to risk scores, then you can use some rule of thumb. For example, get the number of missed payment indicators over the past 12 months, and add the indicator values over the previous 6 months or 1 year, and this will give a reasonable rough indicator of risk. Alternatively if you can make an assumption around the other add-ons that I mentioned before, then you can strip out the loss component of the interest rate charges.
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