Challenges in using machine learning for financial monitoring
summary
Shifting a rules-based AML system to machine learning cuts the false positive rate down ~50% and raises escalation quality, while supervised models train on SARs as supervised labels that are often noisy or defensive. Workflows combine supervised and unsupervised ML with entity resolution and network generation to surface new risks and outliers, but face data drift, rigid regulatory model validation, high model lifecycle maturity needs, and scaling pressures moving monitoring from monthly toward near real time.
AI-written summary · about
chapters (6)
- 0:00Introduction
- 2:33Supervised machine learning
- 8:18Model validation
- 13:05Regulatory challenges
- 15:15Other challenges
- 18:43What can we do
related talks
18mAnti-Money Laundering ML Modelling Approach
46mML for Anti-Money Laundering
44mAdvancing Cybersecurity with AI and Machine Learning: Opportunities and Challenges
49mEnhanced AML fraud detection solutions with Azure Machine Learning
13mHarnessing Machine Learning to Combat E-commerce Fraud
42mTesting Machine Learning Modules in Production
37mThe Role of Machine Learning in Today’s Payment Ecosystems
25mAutomating Operations with ML