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Neural Network-Based Anti-Money Laundering (AML) Systems: Detection Rate Improvement & Investigation Efficiency - Technological Advancements

Former SVP, Audit Director - Global Financial Crimes
Bank of America
Diversified Financial Services
USA
North America

$ 795

June 18, 2025

Topics covered in this transcript

  • How do graphneural networks map complex transaction networks?
  • What supervisedlearning techniques improve detection accuracy?
  • How are entityresolution algorithms combating synthetic identities?
  • What regulatoryvalidation processes ensure model compliance?
  • How do systemsadapt to emerging money laundering typologies?
  • What ROI metricsjustify neural network investments?
  • How arecross-border transaction patterns analyzed?
  • Whatprivacy-preserving techniques protect customer data?
  • How doinvestigation workflows integrate with case management?
  • What feedbackloops enable continuous model retraining?
  • How are falsepositive rates benchmarked against legacy systems?
  • Whatvisualization tools aid investigator decision-making?

Keywords

  • AML Neural Networks
  • Transaction Network Analysis
  • Alert Fatigue
  • Supervised Learning
  • Entity Resolution
  • Regulatory Validation
  • Cross-Border Patterns
  • Model Retraining
  • Investigation Workflow
  • False Positive
  • Data Privacy
  • ROI Analysis

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info@nextyn.com
Singapore
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049422, Singapore
Jakarta

Revenue Tower, Scbd, Jakarta 12190, Indonesia
Mumbai
4th Floor, Pinnacle Business Park, Andheri East, Mumbai, 400093
Bangalore

Cinnabar Hills, Embassy Golf Links Business Park, Bengaluru, Karnataka 560071
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