Solving Financial Fraud Detection with Machine Learning Methods

For decades, financial organizations used rule-based monitoring systems for fraud detection.

These legacy solutions were deployed in SQL or C/C++. They were attempts of the engineers to transfer the knowledge of domain experts into sequel queries, which would typically end up being long, convoluted, and extremely brittle.

And whenever they tried to change parts of these fraud detection systems later, to update a threshold or something, it led to the breaking of the entire codebase.

This prevented banks from fighting fraud effectively – the criminals would just come up with new ways around alert triggers in their weak, rule-based platforms.

So now many financial firms have abandoned their legacy tools to try and solve fraud detection with new-age machine learning solutions, and more still are planning to follow suit in the future.

ML algorithms can process millions of data objects quickly and link instances from seemingly unrelated datasets to detect suspicious patterns. They’re one of the only tools left that can help banks and FinTechs keep up with new defrauding schemes, which are growing increasingly sophisticated.

But it might be unclear for someone who’s not a data scientist which algorithm to opt for to help their company identify illicit transactions. In this post, we’ll describe a few popular choices.

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