Business Crisis: The Hybrid Diagnosis between Qualitative Factors (Balanced Scorecard) and Prospective Quantitative Indicators

The Code of Corporate Crisis and Insolvency (CCII) enshrined a fundamental principle: crisis is not an unpredictable event, but a gradual and monitorable degenerative process. The renewed Article 2086 of the Civil Code imposes on the entrepreneur the obligation to equip himself with adequate organisational, administrative and accounting structures to intercept signs of crisis in a timely manner and safeguard business continuity.

However, financial analysis based on historical financial statements has a crucial limitation: it reflects the effects of the crisis (backward looking approach), not its early signs. The financial crisis is often only a symptom of the degeneration of underlying economic or operational factors. For a truly early diagnosis (early warning), it is essential to supplement numerical analysis with the evaluation of qualitative factors.

Qualitative Prevention: The Scorecard Model

In this scenario, methodologies adopting a quantitative approach (or hybrid) prove essential for prevention. Prominent among these is the use of management control frameworks based on the Balanced Scorecard (BSC), a strategic management model devised by Kaplan and Norton.

The BSC was redeveloped for Small and Medium Enterprises (SMEs) with the objective of measuring actions and not just numbers. It breaks down the company into four interconnected perspectives (Financial, Customer, Internal Processes, Learning and Development) and requires the definition of objectives and indicators (KPIs) for each of them.

Qualitative** and non-financial **Key Performance Indicators (KPIs) are considered the real clues to a crisis and must be monitored in real time. A deterioration in these areas typically precedes budgetary imbalances. Examples of crucial qualitative KPIs include:

• Company climate and harmony among members.
• The training propensity and innovation activity (products or processes).
• The monitoring of customer satisfaction and the number of complaints.

The adoption of a qualitative-quantitative system such as the Control Dashboard, based on the Balanced Scorecard, is not only a management choice, but a fundamental piece of evidence to demonstrate that the company has the appropriate organisational structures required by law.

The Quantitative Imperative: DSCR and Predictive Analysis

The integration of these qualitative indicators with quantitative analysis is imposed by the need to assess the predictive sustainability of debt. The crisis, according to the CCII, manifests itself as the “inadequacy of prospective cash flows to meet planned obligations on a regular basis”.

The priority indicator (alert level 2) under the regulations, based on forward-looking data (forward looking), is the Debt Service Coverage Ratio (DSCR). The DSCR measures the company’s ability to generate Free Cash Flows (FCFF) sufficient to cover the debt (principal and interest) expected over the next six months. A DSCR below 1 indicates high risk.

If the DSCR is not available or deemed reliable, the joint use of five quantitative balance sheet ratios is used, with thresholds differentiated by sector:

1. Financial charges sustainability index (financial charges/turnover).
2. Capital adequacy index (equity/total debt).
3. Asset liquidity index (cash flow/assets).
4. Liquidity ratio (short-term assets/short-term liabilities).
5. Social security and tax debt ratio.

The Frontier of Forecasting: Machine Learning

In addition to traditional statistical methods, such as Altman’s Z-Score from 1968, which is retrospective and based on linear relationships, today’s most advanced diagnostic systems use Machine Learning (ML) algorithms.

Algorithms such as XGBoost or Random Forest offer significantly superior performance in predicting the default. Unlike linear models that rely on rigid assumptions, ML algorithms can model complex, non-linear relationships between thousands of variables, even non-accounting variables, and dynamically adapt to the data. Empirical studies show that the XGBoost, for example, can achieve an accuracy (AUC) of up to 0.97, significantly outperforming classical models such as the Z-Score (AUC around 0.75).

The adoption of a monitoring system that combines qualitative early warning (as suggested by the Scorecard model) with perspective quantitative requirements (DSCR) and predictive accuracy (Machine Learning) is the key to building a resilient business management that complies with the modern regulatory framework.