Credit Risk Models Explained: Altman Z-Score and Merton Model
Structured Finance & Securitisation Explained showed how securitisation involves pooling financial assets and issuing securities backed by those pools to investors, allowing originators to transfer credit risk and free up capital for further lending. Credit risk models answer the next interview question: how do analysts estimate default risk before lending, investing, rating, or pricing credit exposure? The key framing is to compare Altman Z-Score, which uses accounting ratios, with the Merton Model, which uses market-based asset values.
- Altman Z-Score predicts corporate bankruptcy using five accounting ratios: X1, X2, X3, X4, and X5.
- Z-Score = 1.2ĆX1 + 1.4ĆX2 + 3.3ĆX3 + 0.6ĆX4 + 1.0ĆX5.
- Zones: Z > 2.99 = Safe Zone (unlikely distress), 1.81 < Z < 2.99 = Grey Zone, and Z < 1.81 = Distress Zone (high default risk).
- Indian application: Analysts used Z-Score to flag stress in IL&FS subsidiaries and Jet Airways before their defaults.
- Merton Model is a Structural Credit Model that views equity as a call option on the firm's assets.
- In the Merton Model, default occurs when asset value falls below debt face value at maturity.
- Probability of default = N(-dā), where dā is derived from Black-Scholes.
Big Picture: Two Ways to Estimate Default Risk
Credit risk models can be framed as two complementary ways to estimate default risk: Altman Z-Score uses accounting ratios to predict corporate bankruptcy, while the Merton Model uses market-based asset values. The first is useful as a ratio-based distress signal; the second is used by sophisticated credit desks for pricing CDS and estimating default probabilities from equity market data.
Z-Score = 1.2ĆX1 + 1.4ĆX2 + 3.3ĆX3 + 0.6ĆX4 + 1.0ĆX5
Altman Z-Score - Predicting Corporate Bankruptcy
Altman Z-Score predicts corporate bankruptcy by combining working capital, retained earnings, EBIT, market value of equity, total liabilities, revenue, and total assets into one score. The output is then interpreted through zones that separate unlikely distress, grey zone, and high default risk.
Z-Score Zones
The Z-Score is not useful only as a formula. In interviews, the interpretation matters because the same score must be mapped to a default-risk zone.
Indian application: Analysts used Z-Score to flag stress in IL&FS subsidiaries and Jet Airways before their defaults. The strategic so what is that a Z-Score below the distress threshold can act as an early warning signal before a visible default event.
Merton Model (Structural Credit Model)
Merton Model is a Structural Credit Model. It views equity as a call option on the firm's assets.
Default occurs when asset value falls below debt face value at maturity. Probability of default = N(-dā), where dā is derived from Black-Scholes.
Merton Model (Structural Credit Model): Views equity as a call option on the firm's assets. Default occurs when asset value falls below debt face value at maturity.
Altman Z-Score vs Merton Model
The clean interview comparison is accounting-ratio based default risk versus market-based default probability. Both estimate default risk, but they approach it from different inputs and use cases.
Structuring a Credit Risk Models Explained Interview Answer
"How would you compare Altman Z-Score and the Merton Model for estimating corporate default risk?"
The best answers do not stop at writing the Z-Score formula. They interpret the zones, use IL&FS and Jet Airways as Indian applications, and then contrast Altman Z-Score with the Merton Model's market-based view of default risk.
The most frequent error is memorising the Z-Score formula but not interpreting the thresholds. If you do not state that Z < 1.81 is the Distress Zone with high default risk, or if you miss that the Merton Model uses equity market data, the answer stays mechanical instead of credit-risk focused.
Conclusion
Credit risk models are best understood as complementary default-risk tools: Altman Z-Score uses accounting ratios and zone thresholds to flag distress, while the Merton Model uses market-based asset values to estimate default probability. In interviews, the final takeaway is to explain both the formula and the interpretation.