When a bank deploys a credit scoring model based on generative AI, the first question is no longer technical; it is regulatory. Since the adoption of the AI Act in 2024, credit scoring and risk assessment are among the use cases classified as high-risk in Europe. For teams working in technical finance, this framework changes the way models are designed, documented, and audited even before they go into production.
AI Act and Financial Scoring: What Technical Teams Need to Deliver
The classification as high-risk entails concrete obligations. This includes governance of training data, traceability of model decisions, documented bias management, and transparency towards the regulator.
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In practice, this means that a data scientist building a credit model can no longer simply focus on optimizing a performance metric. They must produce structured technical documentation, comparable to a compliance file, detailing the data used, architectural choices, and results of bias tests.
In France and Germany, national authorities have begun publishing specific positions on the use of generative AI in the distribution of financial products and KYC/AML compliance. The trend is clear: human safeguards and model audits are becoming the norm. To keep up with these developments, one can consult Finance Technique news, which regularly covers regulatory implications for industry professionals.
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Model Risk Management: A Standalone Profession in Finance
In recent years, an increasing number of banks and large insurers have structured dedicated teams for Model Risk Management (MRM). This is no longer a niche topic reserved for quants. The function is organized around hybrid profiles, halfway between data science, compliance, and internal audit.
What an MRM Team Actually Does
The MRM team is involved throughout the entire lifecycle of a model. They validate assumptions before development, test outputs in real conditions, and monitor model drift after deployment.
- Initial validation: checking the quality of training data, robustness testing under stress scenarios, documenting known limitations of the model.
- Production monitoring: tracking performance indicators, detecting biases that emerge over time, triggering recalibrations when results deviate from defined thresholds.
- Periodic audit: comprehensive review of the model at regular intervals, with updates to regulatory documentation and reporting to the risk committee.
MRM transforms the relationship between model developers and governance. An algorithm is no longer delivered as a finished product. A model is delivered alongside a living risk file, continuously updated.
Chief Model Risk Officer: A Position That Is Becoming Common
Several financial institutions have created the position of Chief AI Officer or Chief Model Risk Officer, directly reporting to the risk management department. This hierarchical attachment is significant. It ensures that model governance is not confined to IT teams but rises to the decision-making level.
Feedback varies on the actual maturity of these functions across institutions, but the direction is clear. European regulators are pushing for this structuring, and banks hiring for these profiles are looking for candidates who can engage with both a machine learning engineer and an ACPR inspector.
Tokenization of Real Assets and Blockchain Infrastructure in Technical Finance
The tokenization of assets (real estate, private debt, fund shares) is progressing within an increasingly regulated framework. The topic is no longer experimental. Several regulated platforms in Europe now allow the issuance and exchange of financial securities in the form of tokens on the blockchain.
For technical teams, the challenge is twofold. First, they must ensure that the chosen blockchain infrastructure complies with local regulatory requirements (European regulation on crypto-asset markets, pilot regimes). Then, they need to connect this infrastructure to existing management systems: record-keeping, tax reporting, accounting reconciliation.
This last point is often underestimated. Tokenization does not replace back-office processes; it complicates them as long as the bridges between blockchain and legacy systems are not reliable. It is observed that the projects making the most progress are those where the technical team collaborates from the start with compliance and operations, not in silos.
Alternative Data and Big Data: What Is Changing for Financial Risk Analysis
The use of alternative data (satellite data, anonymized transaction flows, signals from social networks) in risk analysis is not new. What is changing is the level of demand for traceability and quality of this data.
Regulators now expect precise documentation on the provenance of alternative data. A fund using satellite images to estimate port activity must be able to justify the reliability of its source, the frequency of updates, and known limitations of interpretation.
- Provenance: licensing agreement, collection chain, compliance with GDPR if the data involves individuals.
- Quality: missing data rates, geographic or sectoral coverage biases, historical data available for backtesting.
- Integration: ability to cross-reference this data with traditional financial sources without creating duplicates or contradictions in the models.
Big data in finance is no longer just about accumulating volumes. The value lies in the ability to prove the reliability of each source used in an investment decision-making or risk management process.
The coming months are likely to strengthen this convergence between technical requirements and regulatory demands. Teams that have structured their data and model governance in advance will be the ones that deploy the fastest, without delays during the audit process.