What Expected Credit Loss (ECL) Really Means — Through My Journey in Credit
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 f...