What is the Altman Z-score?

Developed by Edward Altman in 1968, the original Z-score is a statistical model designed to distinguish financially distressed companies from healthier businesses. It combines information from the balance sheet and profit and loss statement.

The original model was developed for publicly traded manufacturing companies. Later variants were designed for privately held manufacturers and non-manufacturing businesses. Choosing the appropriate variant matters: the formulas, definitions and interpretation thresholds are not interchangeable.

The five financial ratios used in the Z-score

The classic and private-manufacturer variants combine five ratios, labelled A to E. The non-manufacturing variant uses the first four.

Each ratio adds a different perspective. A company can be profitable but short of cash, or have a strong balance sheet while generating insufficient operating earnings. This is why the combined score can be more informative than any one ratio.

Altman Z-score formulas by company type

1. Publicly traded manufacturing companies — original Z-score

Z = 1.2A + 1.4B + 3.3C + 0.6D + 1.0E

In this original formula, D is the market value of equity divided by total liabilities.

2. Privately held manufacturing companies — Z′-score

Z′ = 0.717A + 0.847B + 3.107C + 0.420D + 0.998E

Here, D uses the book value of equity, making the variant suitable for companies without a quoted share price.

3. Non-manufacturing companies — Z″-score

Z″ = 6.56A + 3.26B + 6.72C + 1.05D

This model omits sales-to-assets (E) to reduce differences associated with asset turnover across industries. It is commonly used for service and other non-manufacturing businesses, subject to the model's applicability and the quality of available accounts.

Calculate your Altman Z-score online

How to interpret an Altman Z-score

Higher scores generally indicate lower financial distress risk. However, the cut-offs depend on the model. The following commonly cited ranges are indicative, not guarantees.

These categories are useful for screening and prioritising reviews. They do not establish a company's legal insolvency status, nor do they quantify the precise likelihood or timing of default.

Illustrative calculation

Consider a privately held manufacturer with A = 0.15, B = 0.10, C = 0.08, D = 0.50 and E = 1.20.

Z′ = (0.717 × 0.15) + (0.847 × 0.10) + (3.107 × 0.08) + (0.420 × 0.50) + (0.998 × 1.20) = 1.85 (approximately).

This falls within the private-manufacturer grey zone. It should prompt closer investigation rather than an automatic rejection of credit.

Limitations of the Z-score in credit risk assessment

Financial statements are backward-looking

Annual accounts may be several months old. They may not capture a recent liquidity shortage, a major contract loss, a refinancing difficulty or a rapid deterioration in customer payments.

The quality of accounting data matters

Unrecognised bad debts, overstated inventory, unusual accounting policies or inconsistent consolidation scopes can distort the ratios and therefore the score. The analyst should question the reliability of the underlying figures.

Industry and business models differ

Asset-light service businesses, manufacturers and companies operating in different countries can have very different balance sheet structures. A score should be interpreted in its sector, economic and accounting context.

A score cannot capture the entire customer relationship

The model does not directly assess payment behaviour, the customer's market position, management quality, concentration risk, commercial disputes, available guarantees or the strategic importance of a transaction.

How to use the Z-score in a B2B credit decision

1. Select the appropriate model for the company's listing status and business activity.
2. Verify the financial data and identify unusual or potentially misleading balance sheet items.
3. Calculate and compare scores over time rather than relying solely on one year's result.
4. Investigate the drivers of a weak or deteriorating score, especially liquidity and operating profitability.
5. Combine the result with qualitative information, including payment behaviour, sector outlook and the customer's commercial situation.
6. Make a proportionate credit decision on payment terms, credit limits, guarantees and monitoring frequency.

For a fuller assessment, see our guides to customer credit analysis, balance sheet ratios and customer credit scoring.

Conclusion: a useful indicator, not a substitute for judgement

The Altman Z-score provides a structured way to identify signs of financial distress and compare companies or periods. It is particularly useful as an early screening tool. But effective credit management requires a broader assessment of the customer, its business environment and the seller's exposure.

The credit analyst's role is to translate these findings into practical decisions that support sales while keeping credit risk under control.

Next tutorial: Who should make the credit risk decision?