Designing a Financial Crime Detection Analytics System for a Nigerian Payment Platform

Payment platforms in Nigeria face escalating financial crime threats including transaction laundering, identity fraud, and account takeover, and professionally designed analytics systems can significantly improve real-time detection capacity. This study designed a financial crime detection analytics system for a Nigerian payment service provider operating under CBN Payment Service Bank licence. A professional design methodology was applied with structured consultations with 12 payment platform security officers, 6 NFIU compliance specialists, and 8 financial crime data science practitioners, combined with review of CBN Payments System Fraud Risk Management Framework and FATF transaction monitoring guidelines. The system designed specifies a four-layer architecture: transaction feature engineering with velocity calculations and network centrality scores, a supervised ensemble classifier for known fraud patterns, an unsupervised anomaly detector for novel schemes, and a case management dashboard for compliance investigators. STIX format threat intelligence integration with CBN's national fraud exchange is specified. Model drift monitoring and quarterly retraining protocols are detailed to address the evolving nature of fraud patterns. Expert review by nine payment security and financial crime analytics specialists confirmed the system's technical soundness and regulatory compliance. The study recommends phased deployment starting with the highest-risk transaction corridors identified in historical fraud data, CBN reporting integration as a first implementation milestone, and establishing a model performance review committee with compliance and engineering co-representation.

Keywords: financial crime detection, payment platform, transaction monitoring, Nigeria, anomaly detection