Case studies
AI-Driven Risk Assessment for Banking
Transforming credit risk modeling with advanced machine learning for more accurate and fair lending decisions
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Summary
This project reimagined credit risk assessment by replacing legacy scorecard models with an explainable AI system. The platform ingests traditional financial data alongside alternative data sources to create a more holistic view of borrower risk. Built with fairness constraints embedded in the training process, the system delivers more accurate predictions while actively reducing demographic bias in lending outcomes.
Duration
9 months
Team Size
10
01
Challenges
Key challenges included meeting stringent regulatory requirements for model explainability (ECB and EBA guidelines), ensuring algorithmic fairness across protected demographic groups, integrating with core banking systems while maintaining real-time response times, and managing the organizational change from traditional to AI-driven risk assessment processes.
02
Innovation
We developed a constrained optimization framework that jointly maximizes prediction accuracy and minimizes demographic parity gaps. The system uses gradient-boosted trees with SHAP-based explanations for every decision, meeting regulatory explainability requirements. A novel monitoring dashboard tracks model performance, fairness metrics, and drift in real-time, enabling proactive model governance.
03
Impact
The new AI system improved Gini coefficients by 30% over legacy models, translating to better risk discrimination and reduced default losses. Approval rates for qualified underserved populations increased by 18% while maintaining portfolio quality. The bank estimated annual savings of EUR 12M from reduced credit losses and improved operational efficiency.
Impact metrics
Our impact in the vertical
Quantifying our success to showcase how we bring transformative AI solutions to healthcare
30%
Risk Prediction Improvement
+18%
Underserved Population Approvals
EUR 12M
Annual Savings
case studies
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How Our Swift Path Methodology Drives Real-World Impact
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