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In the previous three guides, we explained PD , LGD , and EAD separately. Now we stop explaining. We calculate . This article walks through a complete Expected Credit Loss (ECL) calculation using: One small portfolio One 12-month timeline One clear set of assumptions If you follow the tables carefully, you should be able to rebuild this in Excel yourself. Step 0: Define the portfolio (nothing fancy) Assume today is 1 April 2024 . We have 3 live loans , all Stage 1. Table 1: Portfolio snapshot (today) Total portfolio outstanding = ₹3,00,000 Step 1: Assumptions (explicit and simple) We freeze assumptions upfront. PD assumptions (12-month) Portfolio 12-month PD = 12% We will not break it into marginal PD here. We will use a simplified allocation later. LGD assumption From historical recovery analysis: Discounted LGD = 60% Meaning: On defaulted exposure, we expect to lose 60% Discount rate Monthly discounting ignored for simplicity (We’ll add...

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...