Know your data: Improving an anti-money laundering programme with dedicated data management

Drew Galow and Sara Wright
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Drew Galow: Director, AML Model Management and Machine Learning, Risk Capital and Model Development, US AML Office, Bank of Montreal, USA
Sara Wright: Director, AML Data, AML Risk, Scotiabank, Canada

Journal of Financial Compliance, 2021, vol. 5, issue 1, 45-54

Abstract: Data is gathered, stored and analysed across the globe in every size business and industry faster than it ever has been at any point in human history; the velocity of data at a financial institution is no different. A financial institution’s anti-money laundering (AML) programme needs to equip itself to use this data to their advantage. A dedicated team must be formed to gather critical data for the programme. This team should focus on storing, mapping, evaluating and remediating the programme’s required data. This team can strengthen an AML programme’s base of data and information and, in turn, help the programme effectively and efficiently manage risk. With this base of quality data, the programme can tackle more advanced analytical tasks and use data to drive decision-making, ultimately leading to an efficient, innovative and compliant programme that helps it accomplish its primary job, identifying potential bad actors in the financial system.

Keywords: AML; data management; data quality; data issues; analytics; data advocate; data model (search for similar items in EconPapers)
JEL-codes: E5 G2 K2 (search for similar items in EconPapers)
Date: 2021
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