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In the previous guides, we did two important things: We showed that PD is about loans migrating into 90+ DPD We showed that LGD is about cash recovery after default, adjusted for time There is one missing piece that quietly holds both together. Before a loan defaults. Before recovery even begins. Before loss can be measured. We must answer one simple but slippery question: How much money will actually be outstanding when default happens? That number is Exposure at Default (EAD) . This guide explains EAD the way it is built and used in real ECL models , not the way it is defined in textbooks. First, kill the most common misunderstanding EAD is not : Sanction amount Original disbursed amount Current outstanding A single fixed number In real models: EAD is a month-wise path of outstanding balances, not a point estimate. Why? Because default does not happen “today” for all loans. It happens somewhere in the future , and outstanding changes every mo...

In the previous article, “ How Probability of Default Is Actually Calculated in the Real World ” we did one thing clearly: We showed that PD is nothing but loans migrating into the 90+ DPD bucket over a defined time window. PD answers: Which loans enter default? Once that happens, PD’s job is over. From that exact moment, a new question takes over: Out of the money that was outstanding at default, how much will we finally lose — and when? That question is LGD . This guide explains LGD the same way the PD guide explained PD: Using tables Using behaviour Using time Using cash No theory first. Reality first. Step 0: Fix the universe (this is non-negotiable) LGD is never computed on: The full portfolio Performing loans Stage 1 accounts LGD universe is only loans that have already defaulted . Meaning: Loans that crossed 90+ DPD Loans that entered Stage 3 So your LGD input table must look like this: Table 1: Default entry universe (output of P...

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 ...

 This is not a textbook explanation. This is how I actually think about portfolios when someone asks: “What will my book look like after 6 months?” “Why are my NPAs rising even though disbursements are stable?” “What happens if my collection efficiency drops slightly?” Let’s start from first principles and build our way up. Step 1: The easy part. Total AUM forecasting If all you care about is overall AUM , life is simple. You take: Opening AUM Add fresh disbursements Subtract normal amortisation Subtract prepayments And you get closing AUM. For example: Particulars Amount (₹ Cr) Opening AUM 100 Disbursements +20 Normal amortisation −8 Prepayments −2 Closing AUM 110 This works fine if you don’t care where the AUM is sitting . But lending is never that simple. Step 2: Why bucket-wise AUM actually matters Most real decisions depend on where the AUM sits, not just how much. Bucket-wise AUM is required for: ECL provisioning Understanding delinquency buil...