How Explainable Artificial Intelligence transforms B2B credit scoring by ensuring transparency, decision justification, and regulatory compliance. Discover why every score must be explained.
Explainable AI, or XAI, refers to a set of techniques and principles that allow understanding, interpreting, and justifying decisions made by an artificial intelligence system. Unlike traditional "black box" models (deep neural networks, complex ensembles) that produce results without explanation, XAI makes criteria, weightings, and decision logic transparent.
Technical Definition
XAI relies on techniques like feature importance, SHAP values (contribution of each criterion to the final score), or interpretable decision rules. The goal: enable a human to understand why the AI made such a decision, even without being a data scientist.
In credit scoring, decisions have direct financial impacts and can affect a company's reputation. Unlike other AI use cases (product recommendations, targeted advertising), financial scoring is subject to strict regulations and high transparency expectations. Here's why XAI has become indispensable:
Let's take the example of a company rated 65/100 (moderate risk). Here's how an explainable system breaks down this score:
Base score (neutral)
70/100
Final score = 70 - 5 - 8 + 4 + 2 - 3 = 65/100
With this breakdown, you immediately understand that the recent payment incident (-8) is the most penalizing factor, followed by high debt (-5). You can thus adapt your decision: request guarantees, reduce payment terms, or accept under monitoring.
Don't let AI's black box endanger your regulatory compliance and credibility. Discover how RocketFin transforms every score into a justified and transparent decision.