I didn’t start my career in risk modelling. I’m an accountant by training. My early years were spent in accounting and finance roles, working on things like OPEX and CAPEX planning, budgets, and variance tracking. Numbers, yes. But not credit models. Statistically, my toolkit was basic. Mean, median, standard deviation. Some idea of correlation, regression, and probability from textbooks. Nothing fancy. No machine learning. No advanced econometrics. So when I say this, I mean it honestly: If I could understand ECL and work with it comfortably, almost anyone in finance can. ECL looks complex mainly because of how it’s presented, not because of what it’s trying to do. At its core, ECL is built around one very practical question: If this borrower fails to pay in the future, how much will I realistically lose? Everything else exists only to answer this question in a structured, regulator-acceptable way. Before we talk about ECL, let’s talk about provisioning When I was in core fi...
At a high level, loan pricing looks like this: We have fair idea about all the components except for credit cost. Let’s assume everything except credit cost is already known. This article is only about how to think about, estimate, and sanity-check credit cost — in a way that actually works in real lending. What credit cost really represents Credit cost answers one very boring but very important question: “Out of all the money I lend, how much will I not get back?” It is not : GNPA, write-offs, stress loss, worst-case loss. It is the average, expected loss baked into pricing. If you don’t price this correctly, everything else in loan pricing becomes meaningless. The irreducible formula (don’t fight it) Credit cost has only two moving parts: Credit Cost = PD × LGD Where: PD (Probability of Default) Out of 100 similar loans, how many will default at least once? LGD (Loss Given Default) If a loan defaults, what % of the outstanding amount will I ultimate...
Somewhere between the tenth and twentieth portfolio I reviewed, I realised I was no longer surprised by failures. The products kept changing. Home loans, MSME, EV, solar, supply chain. Secured, unsecured. But the reasons things went wrong started repeating themselves. Sometimes slowly, sometimes all at once.
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