Europe Pushes AI into Credit Scoring Processes

Europe Pushes AI into Credit Scoring Processes

Artificial intelligence is moving from the experimental fringe to the decision-making core of Europe’s lending markets, with France helping to set the regulatory tempo.

Banks, fintechs and consultancies are switching from rigid credit scoring to machine learning models such as TabNet, LightGBM and CatBoost, fed by open banking data and even device behaviour. The aim is precision lending that can include customers long overlooked by traditional measures.

“The transition to behaviour-based modelling is revolutionising credit assessment,” says Alexandre Durand, head of risk and compliance at Yaroko Partners, a Parisian fintech consultancy specialising in tailored strategies for payment platforms and financial technology providers. “Fintechs and banks are now able to serve consumers who would once have been excluded, without sacrificing accuracy or compliance.”

France’s Prudential Supervision and Resolution Authority has issued rules on AI governance that emphasise data quality, transparency and stability, echoing guidance from the EU’s banking and central bank authorities. The UK’s Financial Conduct Authority and the Bank of England have also weighed in, underscoring the cross-border nature of the shift.

The EU’s AI Act, adopted last year, treats credit scoring as a “high-risk” application, demanding human oversight and detailed documentation. For lenders operating across borders, this regulatory convergence offers clearer guardrails as they deploy AI tools.

Data access is the other catalyst. ECB research shows that analysing real-time banking transactions can raise acceptance rates among low-score borrowers by up to half. Device signals, from browser types to application timings, now form part of risk profiles, adding behavioural nuance to traditional credit histories.

“Our clients want to combine open banking and device data, but must do so within GDPR boundaries,” says Durand. “Those who master both compliance and technology will win market share.”

With challenger banks and fintechs leading the charge, Europe is moving towards a model where AI-driven credit decisions are not just faster but potentially fairer. The question now is whether regulators and industry can maintain the pace without losing public trust.Europe Pushes AI into Credit Scoring Processes

Artificial intelligence is moving from the experimental fringe to the decision-making core of Europe’s lending markets, with France helping to set the regulatory tempo.

Banks, fintechs and consultancies are switching from rigid credit scoring to machine learning models such as TabNet, LightGBM and CatBoost, fed by open banking data and even device behaviour. The aim is precision lending that can include customers long overlooked by traditional measures.

“The transition to behaviour-based modelling is revolutionising credit assessment,” says Alexandre Durand, head of risk and compliance at Yaroko Partners, a Parisian fintech consultancy specialising in tailored strategies for payment platforms and financial technology providers. “Fintechs and banks are now able to serve consumers who would once have been excluded, without sacrificing accuracy or compliance.”

France’s Prudential Supervision and Resolution Authority has issued rules on AI governance that emphasise data quality, transparency and stability, echoing guidance from the EU’s banking and central bank authorities. The UK’s Financial Conduct Authority and the Bank of England have also weighed in, underscoring the cross-border nature of the shift.

The EU’s AI Act, adopted last year, treats credit scoring as a “high-risk” application, demanding human oversight and detailed documentation. For lenders operating across borders, this regulatory convergence offers clearer guardrails as they deploy AI tools.

Data access is the other catalyst. ECB research shows that analysing real-time banking transactions can raise acceptance rates among low-score borrowers by up to half. Device signals, from browser types to application timings, now form part of risk profiles, adding behavioural nuance to traditional credit histories.

“Our clients want to combine open banking and device data, but must do so within GDPR boundaries,” says Durand. “Those who master both compliance and technology will win market share.”

With challenger banks and fintechs leading the charge, Europe is moving towards a model where AI-driven credit decisions are not just faster but potentially fairer. The question now is whether regulators and industry can maintain the pace without losing public trust.