Home White Paper SAS-How-AI-and-Machine-Learning Are-Redefining-Anti-Money-Laundering

SAS-How-AI-and-Machine-Learning Are-Redefining-Anti-Money-Laundering

by thetechmarketer

The bad actors are making strong plays. Take every advantage to fight back.

The evolving landscape of money laundering

Financial criminals are shrewd in disguising the origins of their illegal profits and getting that money into the financial system, either for personal gain or to support criminal enterprise. While most laundered money stems from drug trafficking and organised crime, the events of 9/11 also put the spotlight on covert funding for terrorist activities,
which has traditionally been even more difficult to detect.

It’s a daunting challenge. Consider the immense volume of data that financial institutions are expected to comb through to meet regulatory requirements to detect and report suspicious activity. The data is usually diverse and subpar. It’s common for systems to use only a subset of available data when generating alerts. Traditional transaction monitoring systems are unwieldy to maintain and rely on rules and thresholds that are easy for criminals to test and circumvent. Investigation processes tend to be highly manual, from gathering the supporting data for a case to submitting
a complete SAR (suspicious activity report).

Meanwhile, the money launderers are working night and day to remain hidden, constantly engineering new ways to conceal the flow of funds,

Traditional anti-money laundering (AML) and combating the financing of terrorism (CFT) tools and tactics take longer and cost more than they should. To fortify the defense, financial institutions need ways to:

  • Automate tasks that formerly required human intervention, such as disposition of
  • Detect more risk and effectively prioritize it with sophisticated analytics techniques.
  • Provide richer context for investigations with access to more comprehensive

Here’s where AI comes in

The concept of artificial intelligence (AI) conjures up visions of robots who learn too much, grant themselves too much power and vanquish their creators. The reality of AI is far less dramatic. Broadly speaking, it’s about allowing a machine to make a decision a human could have made.

When Amazon and Netflix recommend things you might like, AI is behind the scenes. When Siri and Alexa intelligent personal assistants help you organise your life and make recommendations, or when facial recognition authenticates your online or mobile payments, artificial intelligence is at work. From driverless cars to personalised product offers to detecting credit card fraud, AI and machine learning technologies are benefiting a host of industries and creating new ones not dreamed of.

A subset of AI, machine learning enables a computer program to learn from data rather than through explicit programming. These programs work by taking example data, finding patterns in it that might be too complex for a human to intuitively see, then applying the findings to new data. When this learning capability is coupled with modern computing power, you have a recipe for a system that can make complex decisions in an automated way.

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