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Before you start reading, download this Excel model . Keep it open beside you. You don't need to understand every formula in it right now. Just look at two things: the amortisation schedule the simple P&L summary built on top of it Amortisation Schedule 3 year P&L view This article explains what those numbers mean. Excel helps you see them. Read both together, and lending will start making sense in a way it usually doesn't. Most discussions on lending start in the wrong place. They start with portfolios, ROE, growth, or regulations. They talk about yields, spreads, and leverage. But lending does not begin with a balance sheet. It begins with one loan . If you truly understand how one loan behaves over time, everything else in lending becomes easier to reason about. If you don't, no amount of ratios will save you. So let's start there  — with a single loan, and with the amortisation schedule you see in the Excel. The p...

The other day, we were brainstorming a new loan product. I had built a financial model from scratch based on the assumptions we had agreed on. While reviewing the P&L, my CEO paused and asked a simple question: "Why is ROE falling in the later years?" "Because we're introducing debt," I said. She replied, "But debt magnifies ROE, right?" That sentence sounds obvious. But it's incomplete. Debt can magnify ROE. But it can just as easily destroy it. And this distinction matters more than most people realize. Let me explain the framework I use to think about this. It applies to lending businesses, but also to any business that uses leverage. First, remove debt from the picture Before talking about leverage, ask one basic question: What does the business earn on its own capital? Strip the business of debt and look at operating economics alone. A simple way to express this is: Pre-debt return = Operating profit ÷ Capital employed Ope...

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...

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...

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...