The project
Banque CPH, a local banking institution for over 85 years, wanted to modernize its fraud‑detection tools in order to keep pace with evolving regulations and the emergence of new fraud patterns.
The objective was to strengthen the efficiency of existing controls while maintaining a high‑quality customer experience and meeting strict requirements related to data confidentiality and technological sovereignty.
The challenge
- Reduce reaction time to emerging fraud, by identifying unusual behaviors faster and adapting to constantly evolving scenarios.
- Ensure strict compliance with current regulations (GDPR and the upcoming AI Act) through a fully local, cloud‑free solution, guaranteeing complete control over data and processing.
- Limit false positives, which create friction for customers, unnecessarily mobilize internal teams, and complicate operational workflows.
Our solution
We designed a machine‑learning model integrated into the existing scoring system, capable of evolving through a continuous labeling mechanism.
Approach
- In‑depth business immersion to understand the existing rules.
- Development of an explainable and compliant AI model.
- Rapid iterations to improve accuracy and reduce false positives.
- Strong governance and team training to ensure successful adoption.
Benefits for users
- Fewer transactions mistakenly blocked.
- Clear explanations for every AI recommendation.
- Time savings and reduced manual workload.
Team Feedback
The analysts confirmed the relevance of the AI recommendations and how easily they could be integrated into their workflows.
What We Delivered
- AI Model: Fraud detection with full explainability.
- Data Pipeline: Preparation and governance of sensitive data.
- Interface: A clear compliance‑ready dashboard.
- Training: AI Act workshops and hands‑on adoption sessions.
Results in Numbers
- Accuracy above 95% achieved by the final model.
- Half of transactions processed faster.
- Significant reduction in overall processing times.
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