IDG Contributor Network: Small, community banks using machine learning to reduce fraud

It will come as no surprise to hear that fraud is an increasing problem across all financial institutions, but it is not only plaguing larger banks but also smaller financial institutions. Statistics show that charges of debit card fraud have grown over 400% in only three years.

A case in point is Orrstown Bank, a community bank located in Pennsylvania and Maryland. Orrstown wanted a way of tackling fraud in an ongoing way, but within the context of their budget and technology constraints. Fraudulent credit card scammers have developed more abilities to work around the majority of safeguards that banks have in place.

For Orrstown, analyzing the patterns of activity from transactions where a card is present used to be much simpler. Historically, the bank could either search for charges made outside of their region or rely on customers to flag fraudulent activity on their statements. However, identifying fraud today has become much more complex. For example, there has been an increasing number of cases where criminals are selling cards back into the local area from which they were stolen—thus making tracking by locality more difficult. As a result, Orrstown explored more advanced forms of data analysis that could do a better job of identifying these types of transactions.

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