Beyond the AI Act: The 5 Pillars of Ethical Governance in B2B Credit Scoring

Introduction: From Compliance to Trust

The European Union has clearly established the rule: credit scoring is a "high-risk" Artificial Intelligence system under the AI Act. For financial institutions, this means that simple model performance is no longer sufficient. Strict regulatory compliance is required, and, critically, ethical governance that goes beyond minimum requirements.

An ethical B2B scoring system is not just one that avoids fines; it is a system that builds market trust, fosters financial inclusion, and enables banks and fintechs to make fairer, better-informed decisions.

The goal is not to stifle innovation but to frame it responsibly. Drawing on RocketFin’s experience in developing explainable and compliant scoring tools, we have defined the five fundamental pillars upon which every AI governance strategy in the B2B credit sector must rest.

Part 1: Pillar 1 – Total Transparency and Proactive Explainability (XAI)

Transparency is the foundation of trust. In credit scoring, this translates to AI Explainability (XAI).

1.1. The Demand for Explainability (XAI)

The AI Act imposes the duty to inform the user of the factors that led to a decision. It is not enough to explain; one must explain proactively.

• Proactive Reason Codes: Every score (whether positive or negative) must be accompanied by clear, concise Reason Codes, generated in real time. These codes should not be mere technical labels but understandable operational factors (e.g., "Positive cash flow but high short-term debt").

• The Right to Understand: The scoring system must offer a simple, accessible mechanism for the client to delve deeper into the explanation, for example, via a dashboard where the company can see the impact of different variables on its score.

• Transparency of Data Used: Businesses must know which categories of data (banking, accounting, legal, alternative) were mobilized for their score calculation.

1.2. Public Model Documentation

Companies should be informed about the general functioning of the system. This includes publishing a non-technical summary of the purpose, accuracy level, and limits of the scoring model. This transparency increases adherence to the process.

Part 2: Pillar 2 – Bias Mitigation and Fairness

One of the most serious risks of AI is the perpetuation, or even amplification, of historical biases. B2B scoring must aim for Fairness.

2.1. Identifying B2B-Specific Biases

Unlike consumer credit (where biases are often based on age or ethnicity), B2B biases often manifest around indirect variables:

• Geographical/Local Bias: A model could unintentionally disadvantage businesses located in under-documented regions or those with high historical default rates, even if the current business is healthy.

• Size/Sector Bias: Classic models often favor large, well-established firms. Ethical scoring must be inclusive of VSEs, startups, and emerging sectors, by valuing alternative data (Open Banking, e-commerce) rather than just the age of the balance sheet.

2.2. Fairness Testing Methods

Ethical governance requires regular, automated testing (Monitoring) for bias.

• Disparate Impact Testing (DIT): Measuring whether groups of companies (grouped by sector, size, etc.) suffer a disproportionate refusal rate compared to their actual risk.

• Debiasing Techniques (Pre-processing/In-processing): Using statistical and algorithmic techniques to neutralize the excessive influence of non-relevant variables on the final result, ensuring the score reflects only objective financial risk.

Part 3: Pillar 3 – Data Quality and Provenance (Data Governance)

An AI model is only as good (and as ethical) as the data that feeds it. Ethical governance begins with impeccable data management.

3.1. Verifiability and Data Freshness

• Open Banking Data: Ethical use of banking data requires clear validation of the company's consent (PSD2 compliance) and perfect traceability. The model must only use data whose freshness is guaranteed (real-time feeds, not six-month-old extracts).

• Management of Alternative Data: The use of alternative data (e.g., customer reviews, web activity) must be limited to publicly accessible information and must exclude any personal or sensitive data irrelevant to financial risk.

3.2. The Principle of "Quality for Use"

The data used must be rigorously cleaned, complete, and relevant for the purpose (credit granting). Missing or low-quality data silently introduces bias. The governance system must include automated data quality control before any injection into the model.

Part 4: Pillar 4 – Human Oversight and Control (Human-in-the-Loop)

In a high-risk system like credit scoring, total automation is contrary to the spirit of the AI Act. The role of the human must be clearly defined.

4.1. Defining "Uncertainty Zones"

The human role should not be overwhelmed with files. It should intervene only in cases where the AI model presents high uncertainty.

• Confidence Thresholds: The system must automatically flag files falling into "Gray Zones" (scores close to refusal/approval thresholds, or when there is significant disagreement between different data sources).

• Role of the Credit Manager: The human role is not to re-verify calculations, but to bring qualitative expertise and client context that the AI cannot grasp (e.g., a historical relationship, an imminent merger).

4.2. Override and Feedback Mechanisms

• Override Capability: Credit Managers must have the documented ability to override the AI's decision, but this action must be tracked and justified.

• Human Feedback: Human decisions that contradict the model serve as training data (Human Feedback) to improve the ethics and accuracy of the next model version.

Part 5: Pillar 5 – Robust Auditability and Continuous Documentation

Ethical governance is a living process that must be maintained and proven. This pillar is the core of the AI Act’s technical requirements.

5.1. The Compliance Log (Audit Log)

Every decision made by the AI model ( or any human intervention) must be recorded in an unalterable audit log.

• Complete Traceability: The Audit Log must record: the time, the model version used, all exact input data, the final score, the generated Reason Codes, and the result of bias checks.

• Proof of Compliance: This log is the ultimate proof that the institution has adhered to its own ethical standards and regulatory requirements.

5.2. "Living" Technical Documentation

The AI Act requires comprehensive technical documentation. This documentation must not be a static document but a continuously updated repository.

• Model Maintenance: Describe the strategy for monitoring model performance, including the frequency of fairness and accuracy tests.

• Change Management: Formalize the procedure for updating the model (the Model Governance process) to ensure that any major modification (algorithm change, added data source) is validated by risk and ethics experts before deployment.

Conclusion: Ethical Governance as a Driver of Trust

In the hyper-automated world of B2B credit, ethics and governance are not constraints but fundamental differentiators. The 5 pillars—Transparency, Fairness, Data Quality, Human Oversight, and Auditability—form a framework that allows financial institutions to continue innovating with AI while ensuring responsibility and justice.

Adopting strict ethical governance transforms regulatory risk into trust capital. For RocketFin, this is the only viable path for the future of B2B finance.

Contact our team by clicking here to assess the compliance of your current scoring system and strengthen your AI governance in the face of the AI Act.