The EU AI Act and Its Implications for Credit Risk Models in Banking
The EU AI Act intends to establish a landmark framework for the safe and ethical deployment of AI systems. This whitepaper examines how the AI Act intersects with credit risk modeling in banking, offering insights into compliance pathways and operational challenges. Drawing on the European Commission's February 2025 guidelines on AI system definition and other authoritative sources, we provide financial institutions with key takeaways for navigating this regulatory landscape.
Definition of AI Systems Under the Act
The AI Act defines AI systems through seven key elements:
- Machine-based system: Operating through hardware or software
- Autonomy: Functioning with varying degrees of independence from human control
- Adaptability: Potential to modify its operations based on experience or new data
- Objective-driven: Designed to achieve specific goals, whether explicit or implicit
- Inference capability: Able to derive conclusions from input data
- Output generation: Producing content, predictions, recommendations, or decisions
- Environmental influence: Capable of affecting physical or virtual environments
Credit Risk Model Types and Coverage Under the Act
Examining common credit risk modeling approaches through the lens of the AI Act reveals important distinctions. Whether a model falls under the AI Act depends on how the model is constructed. Some common types of models could be assessed as follows.
Traditional Regression Models
Pure logistic regression models with manually selected variables and fixed coefficients likely fall outside the AI system definition, as such models lack autonomy and adaptability. However, models that automatically select variables or periodically recalibrate coefficients may cross into AI territory.
Scorecard Models
Classic scorecard models with predefined attributes and weights likely wouldn’t qualify as AI systems. However, scorecards derived from machine learning algorithms or those that update automatically based on new data would likely be considered AI systems.
Machine Learning Models
Decision trees, random forests, gradient boosting machines, and neural networks likely would qualify as AI systems due to their adaptability, inference capabilities, and often complex pattern recognition.
Hybrid Approaches
Models combining traditional statistical techniques with machine learning components present classification challenges. The European Banking Authority (EBA) has noted that such hybrid approaches require case-by-case assessment, focusing on the system’s core functionality rather than peripheral components.
High-Risk Designation for Credit Models
The AI Act adopts a risk-based approach, categorizing AI systems into four risk levels: unacceptable risk (prohibited), high risk, limited risk, and minimal risk. Each category comes with obligations proportional to the potential harm the system might cause. All credit risk models that (1) qualify as AI systems under the Act’s definition and (2) are intended to be used for evaluating creditworthiness or credit scoring of natural persons are classified as “high-risk”. This designation reflects the significant impact these systems can have on individuals’ access to financial resources and, by extension, their economic opportunities and quality of life.
Provider vs. Deployer Obligations
The AI Act distinguishes between obligations for different operators in the AI value chain. The two fundamental types of operators are providers (that develop or significantly modify AI systems) and deployers (that deploy and use AI systems). Most financial institutions will likely serve as both providers and deployers of AI credit risk models, as they typically develop these systems in-house and then deploy them in their operations. The Swedish Data Protection Authority has confirmed that it is possible for an institution to be both the provider and deployer for the same AI system.
This dual role means banks must comply with both sets of obligations outlined below. The distinction is particularly important for institutions that purchase third-party AI solutions or develop models that are later used by other entities. Understanding where a bank falls in the AI value chain for each model is crucial for determining applicable compliance requirements.
Provider Obligations
When acting as “providers” (developing or significantly modifying AI systems), financial institutions must fulfill the following obligations under the AI Act.
Risk Management System: Establish systems to identify, assess, and mitigate operational, misuse, and foreseeable risks.
Data and Data Governance: Implement rigorous practices to evaluate data sources for bias and risks to fundamental rights, ensuring training data is appropriately reviewed. For credit models, this means rigorous assessment of training data for demographic biases.
Technical Documentation: Maintain comprehensive documentation covering system architecture, data requirements, validation procedures, human oversight, and lifecycle changes.
Transparency and Provision of Information: Clearly disclose system characteristics, limitations, oversight mechanisms, expected lifetime, and performance metrics.
Human Oversight: Systems must allow designated persons to understand capabilities and limitations, recognize automation bias, interpret outputs correctly, override decisions when necessary, and interrupt system operation.
Accuracy, Robustness, and Cybersecurity: Ensure systems permit human intervention (to override or interrupt operations) and meet high standards for accuracy, robustness, and protection against unauthorized alterations.
Deployer Obligations
As deployers, financial institutions face additional obligations.
Operational Compliance: Follow deployment guidelines, ensuring that operations align with intended purposes and that personnel receive proper training.
Input Data Relevance: Regularly assess and confirm that input data remains current and representative.
Monitoring and Record-Keeping: Implement enhanced monitoring to detect anomalies and maintain detailed logs beyond standard model oversight.
Discrimination Risk Management – A Critical Focus: Address bias by providing objective justification for using protected characteristics (e.g. age), conducting fairness tests, and applying mitigation strategies. Research shows credit models can perpetuate discrimination through proxy variables even when protected characteristics are excluded.
Fundamental Rights Impact Assessment (FRIA): Expand existing impact assessments to cover broader societal implications, including privacy, accessibility, and potential discrimination. Financial institutions can leverage existing GDPR Data Protection Impact Assessments (DPIA) but must expand their scope to address all fundamental rights considerations.
Leveraging Regulatory Synergies
Financial institutions can utilize existing regulatory frameworks that overlap with AI Act requirements.
Basel Framework Alignment: BCBS 239 principles for risk data already align with the Act’s data quality requirements. Similarly, Basel model validation requirements satisfy many technical documentation needs, though AI-specific risks must be explicitly addressed.
GDPR Complementarity: GDPR establishes principles that complement the AI Act, including Article 22 on automated decision-making and profiling of natural persons, as well as data minimization principles and transparency obligations.
EBA Guidelines on Loan Origination and Monitoring: These guidelines already require consideration of automated models in credit decisions, establishing expectations for governance, data quality, and validation that can be expanded to address AI Act requirements.
Implementation Strategies for Financial Institutions
1.AI Inventory and Classification: The first step is to conduct a comprehensive inventory of systems that may qualify as AI under the Act.
2.Gap Analysis Against Requirements: For systems classified as high-risk AI, a detailed gap analysis should assess current practices against AI Act requirements. This analysis should produce a prioritized list of compliance gaps requiring remediation.
3. Enhanced Model Development Lifecycle: Model development lifecycle should be updated to incorporate AI Act considerations from inception through retirement.
Concluding Thoughts and Remarks
Based on the European Commission’s February 2025 guidelines, several key implications emerge for credit risk models in banking:
- Traditional credit models with limited autonomy and adaptability may fall outside the Act’s AI definition, potentially reducing compliance burden for established systems
- As credit risk models increasingly incorporate machine learning elements, institutions should anticipate transitioning into the high-risk AI category and prepare compliance frameworks accordingly
- Even for borderline cases, maintaining robust documentation of model development, validation, and governance will be essential for demonstrating compliance
- Banks that excel in addressing potential discrimination risks may find opportunities to differentiate their services through enhanced trust and ethical reputation
The EU AI Act presents both challenges and opportunities for banks utilizing AI in credit risk assessment. While compliance demands investment and process adjustments, it also encourages the development of more transparent, fair, and robust AI systems. Financial institutions that proactively adapt their risk modeling practices will not only achieve regulatory compliance but may also strengthen customer trust, enhance risk management capabilities, and potentially gain competitive differentiation in an increasingly AI-regulated landscape.
Learn more about the EU AI Act and our advisory on Risk and Finance.
