Big Data Analytics to Detect, Prevent Financial Crime

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In today’s technologically disruptive world, even cybercriminals use a variety of advanced exploits to compromise the computer systems of financial organizations worldwide.

In fact, fighting all types of financial crime—including fraud—is one of the biggest challenges these financial institutions face, according to Mike Urban, Director of Product Management, Financial Crime Risk Management, Fiserv.

To protect themselves and their clients, banks and other financial firms must deploy top-notch strategies to prevent and detect such crimes. A key part of any such strategy is Big Data analytics, Urban notes.

Although banks have used predictive analytics and other techniques to fight financial crime, including behavior monitoring, network analysis, pattern recognition, and profiling for years—Big Data is “changing the game,” he says.

“Financial fraudsters are becoming increasingly sophisticated and daring, leading industry experts to speculate future attacks that could have a systemic impact on the financial systems,” according to Urban.

So the onus is on the financial organizations to do everything in their power to fight these threats. If they don’t, they expose their customers to cybercrime, not to mention damage their own reputation and incur monumental losses, according to Urban.

That’s where Big Data analytics comes in.

Big Data analytics enables banks and other financial institutions to deploy real-time analytics on a “massive scale” to fight the ever-increasing risk of financial fraud. They must implement a “financial crime risk management strategy with a multi-faceted analytic approach to detect and mitigate financial crime” that is built around behavioral profiling, Urban says.

To detect unusual activity in a customer’s account, a bank has to profile and track the behavior of that customer account from the time it is first opened, in addition to monitoring the customers’ transactions and putting into place customer engagement tools, he says.

“A combination of behavioral profiling, real-time detection scenarios, and predictive analytics provides the most accurate results,” Urban notes. “Big Data analytics enables financial institutions to provide these services on a scale that simply was not possible five years.”

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