AML Monitoring Modernization

A global bank processed 50,000+ AML alerts monthly across 40+ countries, with a 95% false positive rate that overwhelmed investigation teams. AML analysts spent most of their time reviewing false alarms, while genuine risks faced delays. The bank partnered with Vitosha to build an ML-powered alert triage and investigation platform that reduced false positives by 70%, tripled alert processing capacity, and cut SAR preparation time by 60%.

The bank’s legacy AML system used rules-based monitoring generating 50,000+ monthly alerts with a 95% false positive rate. A team of 200+ AML analysts reviewed alerts manually, spending 80% of their time on false positives. Average time-to-investigate was 7 days, and SAR preparation required 20+ hours per case. With regulatory scrutiny intensifying across all 40+ jurisdictions, the bank needed to improve detection accuracy, reduce investigation cycle times, and demonstrate the effectiveness of its AML program to regulators.

Phase 1: Alert Triage Optimization — Applied Azure ML models (XGBoost and deep learning) to score and tier alerts based on probability of suspicious activity, using features including transaction patterns, customer risk profile, network analysis, and typology matching, reducing the number of alerts requiring manual review by 70%. Phase 2: Investigation Workbench — Built a unified investigation platform on Microsoft Fabric and Dataverse, integrating alert data, customer profiles, transaction history, and external screening results into a single case management interface with automated SAR template population. Phase 3: Regulatory Reporting — Developed Power BI dashboards for AML management and regulators, providing real-time visibility into alert volumes, investigation status, SAR filings, and key risk indicators, with drill-through to case-level detail and full audit trail.

False positives dropped by 70%, reducing monthly alerts requiring manual review from 50,000 to 15,000. Alert processing capacity tripled without adding headcount, as analysts focused on high-probability cases. SAR preparation time decreased by 60% through automated data collection and template population. The bank achieved a 95% on-time SAR filing rate (up from 75%) and passed four regulatory AML examinations with no material findings in the first year post-implementation.

The AML modernization program transformed the bank’s financial crime operations, dramatically reducing false positives while improving detection accuracy. ML-powered triage and a unified investigation workbench enabled analysts to focus on genuine risks and meet regulatory expectations.