Machine Learning Use Cases in Financial Crimes

Ten practical and achievable ways to put machine learning to work

Are Your Fraud Systems Keeping Pace?

Fraudsters are crafty, and they have to be. As financial institutions discover and block their usual tactics, they have to change course. They launch more sophisticated and complex schemes, involve more entities to cover their tracks, cruise just under the radar, and move in new directions whenever another path is blocked.

In short, the art and science of fraud are constantly evolving. Are your fraud and financial crimes systems evolving to keep pace?

Most organizations still use rules-based systems as their primary defense. Rules are great for uncovering known patterns, but rules alone are not very good for uncovering unknown schemes, adapting to new fraud patterns, or handling increasingly sophisticated forms of financial crimes.

That’s where machine learning comes in. Unlike rules-based systems, which are fairly easy for criminals to test and circumvent, machine learning adapts to changing behaviors in a population.

Machine learning systems automatically create analytic models that adapt to what they find in the data. Over time, the algorithm “learns” how to deliver more accurate results, whether the goal is to make smarter credit decisions, retail offers, medical diagnoses or to detect fraud. It’s easy to see the value of adaptive modeling to keep pace with emerging fraud tactics.


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