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Fraud Detection Using Man–Machine Intelligence
Success Story #
27
Banking
Insurance
Risk
Business Problem
A financial services organizationaimed to reduce false negatives and improve fraud detection accuracy for onlinecard-not-present (CNP) transactions, particularly within airline merchantgroups.
Actions Taken
· Designed a scalable man–machine frauddetection ecosystem combining automated ML models with human review.
· Applied classification models to identifysuspicious transaction patterns.
· Integrated model outputs into operationalfraud review workflows.
Outcomes Achieved
· Improved accuracy of fraud detection.
· Reduced false negatives in fraudidentification.
· Achieved 20% reduction in manual review effort.
· Enhanced efficiency of merchant servicesfraud operations.
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