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Machine Learning Development – AML Transaction Monitoring
Using machine learning to reduce false positives and improve suspicious activity detection for compliance teams.
Machine Learning Development – AML Transaction Monitoring
Client/Department
Traditional AML systems overwhelmed the client with 95% false alerts, burying real threats in operational chaos. Compliance teams struggled under Singapore's strict regulations, risking both genuine money laundering detection and regulatory penalties. They needed intelligent automation that delivered precision without compromising compliance.
Problem
Legacy AML systems overwhelmed operations with inaccurate alerts
- 95%+ false positive rate buried real threats in noise
- Manual review bottlenecks drained resources and delayed responses
- Escalating operational costs and regulatory compliance exposure
Solution
Intelligent ML detection and smart prioritization platform
- Advanced unsupervised learning exposed hidden transaction anomalies
- Precision-trained supervised models dramatically improved threat accuracy
- Sophisticated network analysis uncovered complex coordinated schemes
- AI-powered triage engine automatically prioritized high-risk alerts for immediate action
Technology Stack
We combined traditional ML libraries with distributed platforms to handle high-volume, real-time transaction data.
Python & scikit-learn
NetworkX
Apache Spark
Elasticsearch & Kibana
AWS SageMaker
PostgreSQL
Custom Python Pipelines
Achievements
A measurable impact on compliance efficiency and cost savings.