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Presentatie Mirjan ramrattan


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Presentatie van het Datacongres ''data science voor maatschappelijke uitdagingen'' op 22 november 2018

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Presentatie Mirjan ramrattan

  1. 1. /9 Anti Money Laundering Automation with predictive modeling Mirjan Ramrattan ABN AMRO BANK December 2018
  2. 2. /9/9 Anti Money Laundering • What are Anti Money Laundering activities? • Who defines the policies and regulations? • Who implements the monitoring and reporting? • What are the technical challenges? 2
  3. 3. /9/9 Transaction Monitoring Sender account Receiver account Source bank … 3 • Millions of transactions per day. • A rule-based monitoring system gets filtering rules from business lines and processes the transactions and raises alerts. • The raised alerts are put in to the backlog to be manually processed. • A large part of the raised alerts are false cases that are dismissed as not needing further investigation. • Predictive modelling comes into play to help make a second layer automated filtering to throw away the false alarms and mitigate the problem of workload.
  4. 4. /9/9 Data sources 1. Information regarding the transaction such as amount, date and time, origin and transfer path, target account. 2. Information regarding the alert coming from the rule-based system. 3. History of the transactions from and to the target account and other accounts of the client. 4. History of the past alerts and analysis reports by the Customer Due Dilligence experts, made on the target account or other accounts of the client. 4
  5. 5. /9/9 Model (feature engineering) • We create features from the – Generated alert, – Target transaction – Interbank transfer route – History of the account – History of the client – History of the past raised alerts on the account and the client – Client’s profile, etc. • We take binary targets based on the analysis records: – Non-suspicious dismissed alerts (negative class) – Alerts that were filed for further investigation (positive class) • An ensemble (e.g. random forest, gradient boosting) model is trained on all the alert level features and targets. 5
  6. 6. /9/9 Model (tuning approach) • Set classification threshold in a way to avoid any false negative: most conservative approach to make sure no actual suspicious activity passes the filter. • On average we are able to cut down the false positive alerts of the rule- based system. • The model is re-trained periodically with new analysis records • A small set is always analysed to make sure the model performance stays robust. 6
  7. 7. /9/9 Impact • On workload: less manual time and energy spent on dismissing false alerts, more time to filter and investigate cases • On the economy: Anti Money Laundering (AML) regulations in general contribute to transparent financial flow in the Netherlands, and EU. Predictive modelling helps banks achieve this with less cost and more efficiency. • On society: transparent financial flow helps closing loopholes and prevent financial corruptions. By reducing the possibility for financial criminal activities, it can help make national and EU financial policies more effective, improve the mutual trust individual and the society have with the banks, and strengthen the economy. 7
  8. 8. /9/9 Questions? 8
  9. 9. /9/9 Appendix • Models – AML cash: deposits and withdrawals – AML LRT (Large Reportable Transactions): wire transfers – AML HRG (High Risk Group): cross-border transactions from/to outside the Netherlands • FCCM – a tool that rule based selects suspicious transactions • CDD – Client Due Diligence, they receive alerts in the backlog and manually process the alerts for further investigation. 9