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Predictive analytics in fraud and AML

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  • Mills, Clinton

Abstract

The common problem we are all facing in fraud risk and compliance these days is how to address the challenge of reducing false positive rates to optimise detection for fraud and, very importantly when monitoring transactions for anti-money laundering/ counter-terrorist financing (AML/CTF), how to avoid being overwhelmed by alerts. This paper aims to introduce to the reader the importance of considering the use of predictive analytics in the financial crimes prevention strategy and programme of any organisation. This paper starts by offering a detailed background of the scope of the fraud and AML/CTF problem in general, focusing on the high costs that many organisations face when trying to prevent and detect financial crimes and also protect their genuine customers from becoming victims of financial crimes. The paper goes on to describe the most common challenges that organisations face, in particular resource challenges when devising and implementing their strategies. It also encourages a collaborative approach to fraud prevention. The reader can expect to gain insightful information about how predictive analytics can be used in the prevention of financial crimes and what type of benefits it can deliver.

Suggested Citation

  • Mills, Clinton, 2017. "Predictive analytics in fraud and AML," Journal of Financial Compliance, Henry Stewart Publications, vol. 1(1), pages 17-26, June.
    • Handle: RePEc:aza:jfc000:y:2017:v:1:i:1:p:17-26
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    More about this item

    Keywords

    predictive; model; detection; false positive; prevention; victim;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit
    • K2 - Law and Economics - - Regulation and Business Law

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