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If you have ever tried to understand Probability of Default (PD) , chances are you’ve been told things like: “PD comes from complex statistical models” “You need logistic regression” “You need advanced mathematics” “This is only for quants” That framing scares people away. The truth is simpler. In the real lending world, PD starts with a very basic question : Out of all loans that are performing today, how many of them will become 90+ days past due within the next 12 months? That’s it. Everything else comes later. Let’s walk through this step by step, using plain data and plain logic. First, let’s fix the definition in your head In most retail lending setups: Default = loan reaching 90+ DPD 12-month PD = probability that a loan which is currently below 90 DPD will migrate to 90+ DPD within the next 12 months PD is not : Recovery Loss Write-off Provision PD is only about migration to default status . Once this clicks, everything else becomes ...

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...