The Challenge

A multinational bank sought to strengthen its fraud detection capabilities using AI, but its existing processes created more challenges than solutions. Their legacy detection models generated excessive false positives, frustrating legitimate customers and slowing transaction approvals. With operations across multiple regions, the bank also faced conflicting compliance requirements and struggled to maintain a consistent fraud prevention strategy.

Departmental silos compounded the issue. Fraud monitoring was fragmented, with teams in different geographies working in isolation, leading to gaps in oversight. The leadership team wanted an AI-powered system that could adapt in real time, scale globally, and pass compliance scrutiny in every region of operation.

Key Obstacles:

• High False Positives: Too many legitimate customers flagged incorrectly.

• Complex Regulations: Different compliance standards across markets.

• Siloed Operations: Lack of a unified global fraud detection strategy.

• Scaling Challenges: Legacy systems couldn’t handle enterprise-wide AI adoption.

The Solution & Impact

CirrusLabs deployed a global AI fraud management framework to unify and modernize operations.

• Compliance Audit: Ensured alignment with international financial regulations.

• Adaptive Fraud Models: Used machine learning to reduce false positives in real time.

• Unified Monitoring: Standardized fraud detection across all markets.

• Audit-Ready Reporting: Built transparency into all processes for regulators.

Impact Delivered:

• 85% reduction in false positives.

• Real-time fraud detection scaled across global operations.

• 100% compliance with international regulations.