Credit Analysis — Scenario-Based Knowledge Check

Practical scenarios testing applied understanding of credit analysis concepts covered across this section — useful for self-assessment, not drawn from any formal examination.

Q1. A business reports ₹80 lakh in operating cash flow and has annual debt obligations of ₹60 lakh (principal + interest). However, the business operates aging manufacturing equipment requiring ₹15 lakh in necessary annual maintenance capex. Calculate the properly adjusted DSCR, and explain why the unadjusted figure would be misleading.

Adjusted cash available for debt service = ₹80 lakh − ₹15 lakh (maintenance capex) = ₹65 lakh. Adjusted DSCR = 65 / 60 = 1.08. The unadjusted DSCR (80/60 = 1.33) would overstate the business's genuine debt-service capacity by ignoring capex necessary just to sustain existing operations — the properly adjusted 1.08 DSCR is much closer to the 1.0 threshold and likely below most lenders' minimum acceptable level (typically 1.2-1.5x), a materially different risk picture than the unadjusted figure suggests.

Q2. Two businesses in the same industry both report ₹30 lakh in operating cash flow. Business A's cash flow comes primarily from normal customer collections on standard payment terms. Business B's cash flow includes a significant component from delaying payments to its own suppliers well beyond normal terms. Which business represents better credit quality, and why, despite identical headline cash flow?

Business A represents meaningfully better credit quality despite the identical headline number. Business B's cash flow, partly achieved through stretching payables, isn't sustainable — supplier relationships are strained by delayed payments, favorable terms may be lost, and this cash flow "boost" is a one-time, non-repeatable effect rather than genuine, ongoing operational strength. A credit analyst examining only the headline total would miss this critical difference; examining the actual composition and drivers behind the cash flow figure reveals it.

Q3. A borrower's DSCR has declined from 1.6 to 1.4 to 1.2 over three consecutive quarters, alongside a noticeably slower pace of financial reporting compared to prior periods. No payment has yet been missed. How should a credit analyst respond, and why shouldn't they wait for an actual missed payment?

This pattern — a consistent, multi-quarter downward DSCR trend combined with slower reporting — represents a genuine early warning signal worth proactive investigation, not a single explainable data point. Waiting for an actual missed payment (or the 90-day NPA threshold) means missing the entire window where proactive engagement — a direct conversation with the borrower, tightened monitoring, or early restructuring discussion — could meaningfully improve the outcome, since by the time an actual default occurs, the borrower's situation and the lender's available options have both typically deteriorated further.

Q4. A borrower has strong financial ratios across the board but the credit analyst discovers significant undisclosed transactions with a related entity controlled by the borrower's ownership, priced well outside normal market terms. How should this affect the credit decision, even though the reported financials look strong?

This is a genuine governance red flag that ratio-based analysis alone would miss entirely — undisclosed related-party transactions priced outside arm's-length terms can mean the reported financial strength doesn't reflect the borrower's true, standalone economic position, since value may be shifted to or from the related entity in ways that distort the reported numbers. This should weigh heavily in the credit decision regardless of how strong the reported ratios otherwise appear, illustrating why the "Character" C (including governance and management integrity) deserves genuine analytical attention, not treatment as a quick formality before the "real" quantitative analysis.

Q5. A lender's loan portfolio consists of 200 individually well-underwritten loans, each passing rigorous individual borrower analysis, but 70% of the total portfolio value is concentrated in a single regional manufacturing sector. Is this portfolio genuinely low-risk? Explain your reasoning.

Not necessarily, despite the strong individual underwriting — this portfolio carries significant concentration risk at the aggregate level, since a sector-wide shock (a regional economic downturn, an industry-specific disruption) could simultaneously affect a large majority of the portfolio's borrowers, even though each was individually sound at the time of underwriting. Individual borrower analysis and portfolio-level concentration/correlation analysis are distinct disciplines — a portfolio can be excellent by individual-loan standards while still carrying substantial aggregate risk from concentration that only portfolio-level analysis, and potentially stress testing against a sector-specific downturn scenario, would reveal.