Identifying customer risk requires a structured analysis of financial and behavioral data. Discover concrete methods to assess a company's financial health.
Solvency analysis relies on several complementary data categories. Each provides specific insight into a company's ability to honor its commitments.
In Practice
Cross-referencing these sources helps detect inconsistencies (e.g., positive results but tight cash flow) and refine risk assessment. No single data point is sufficient alone.
Solvency analysis can rely on traditional methods or automated AI approaches. Each has advantages depending on the context.
Human Expertise
Analysts interpret ratios and context
Adaptability
Accounts for exceptional situations
Limitations
Time-consuming, subjective, difficult to scale across thousands of files
Speed and Volume
Analyzes entire portfolios in minutes
Consistency
Criteria applied uniformly, without human bias
Transparency
Explainable models reveal the variables that influenced the decision
Recommended Hybrid Approach
In practice, explainable AI handles the bulk of the volume and alerts on sensitive cases. Analysts intervene on complex or atypical cases, relying on model insights. This combination maximizes efficiency without sacrificing quality.
Here's a typical process for analyzing the solvency of a new B2B customer. Each company will adapt these steps to their context.
Retrieve company ID, consult public databases, obtain financial statements. If possible, access payment data via CRM or partner.
Calculate key indicators (debt ratio, liquidity, profitability). Compare with industry averages to detect anomalies.
Example: an 80% debt ratio may be normal in some sectors (real estate), but critical in others (services).
Search for legal alerts, check economic press, analyze management or capital changes. These qualitative elements complement financial analysis.
Assign a score or risk class (A, B, C...). Document points of attention and recommendations (credit limit, guarantee, credit insurance).
Important: The rating must be explainable. Each decision must be justifiable to internal clients or auditors.
Analysis isn't static: regular updates (annual, quarterly) or triggered by events (payment delay, press alert). A healthy portfolio requires continuous surveillance.
To gain efficiency and consistency, it's essential to formalize the solvency analysis process. This involves defining roles, tools, and clear rules.
Who performs the analysis? Who validates? Who decides on credit approval? Clarifying these responsibilities avoids bottlenecks.
Write an internal procedure: sources to consult, ratios to calculate, alert thresholds, validation circuit.
Use scoring software or CRM with risk module. Data and decisions must be traceable.
Standardization Benefits
RocketFin helps you automate customer risk assessment with explainable AI. Gain speed and reliability.