Se ha denunciado esta presentación.
Utilizamos tu perfil de LinkedIn y tus datos de actividad para personalizar los anuncios y mostrarte publicidad más relevante. Puedes cambiar tus preferencias de publicidad en cualquier momento.

How to conduct an anti-money laundering (AML) system assessment

1.178 visualizaciones

Publicado el

This presentation was given on October 4, 2016 at the Toronto Marriott Downtown Eaton Centre Hotel at the 14th installment of Canada’s premier event in the field of money laundering compliance and control. The theme of Money Laundering in Canada 2016 is Financial Crime, Compliance, and Regulation: Keeping Pace with the Times.

Publicado en: Datos y análisis
  • Sé el primero en comentar

How to conduct an anti-money laundering (AML) system assessment

  1. 1. MONEY LAUNDERING IN CANADA 2016 FINANCIAL CRIME, COMPLIANCE & REGULATION HOW TO CONDUCT AN AML SYSTEM ASSESSMENT presented by
  2. 2. PRESENTATION KEITH FURST
  3. 3. ANTI- MONEY LAUNDERING (AML) SYSTEM
  4. 4. OVERVIEW Implementation assessment Ongoing data quality Model performance
  5. 5. TECHNICAL DOCUMENTATION Architecture Functional Specification Extract, Transform and Load (ETL) process Operations manual Inadequate documentation could be a risk for AML system operations because of staff attrition.
  6. 6. AUDIT AND CONTROLS If, the above procedures are not implemented then it could increase the risk for failures in the IT department which increases the risk to Compliance (owner of the AML system). Error handling Database backups Source code control
  7. 7. DATA RECONCILIATION Validating the counts of source data and comparing counts in the AML system is crucial for accountability and producing an audit trail.
  8. 8. DATA VOLUME TRENDS Volatility in the daily record count of crucial transaction files could be a symptom of issues in the ETL process. 0 10000 20000 30000 40000 50000 60000 70000 80000 90000 100000 September 2016 Record Count
  9. 9. ONGOING DATA VALIDATION NULL check Referential Integrity Range of values Data can change over time so it is essential to build ongoing validation processes to ensure accuracy and make configuration changes as needed.
  10. 10. CLIENT ONBOARDING STANDARDIZATION If, new client data is captured without strict standards then the quality of results in the AML and/ or fraud system will degrade. i.e. Address validation and account takeover incidents. Names Addresses Phone numbers Tax identification
  11. 11. MODEL PERFORMANCE Distinct account / alert ratio STR ratio High risk entity attributes Group segmentation Thresholds and scoring Creating metrics to evaluate a model’s performance over time is a way to identify trends and improvements for optimization.
  12. 12. IN SUMMARY Is employee attrition a risk because of lack of documentation ? Are you monitoring for data quality red flags on a daily basis? Is your AML dashboard helping to identify trends at the enterprise level?
  13. 13. QUESTIONS?

×