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Advanced banking analytics: Giving banks the advantage to stop financial fraud

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Business and Technology Writer

Kited checks. Misappropriated funds. Untruthful loan applications. Identity theft. Synthetic ID. Account takeovers. The nightmarish list of banking fraud grows as institutions offer more mobile banking and payment services to satisfy consumers' appetites.

Thanks to advanced banking analytics, however, financial institutions have their own technological tools to stop fraud in its tracks. Big data and fraud analytics have become the lens and the leverage for financial institutions to prevent bad actors from causing harm.

Snapshot of a problem: Fraud in the banking world

Criminals are working steadily to break through the banking industry's defenses. Historically, substantial resources were required to protect financial data. According to Johnny Lee, managing director of forensic, investigative and dispute services at Grant Thornton, defending a bank from cyberfraud is a demanding task. The first step is to protect accounts from intrusion; if a criminal is unable to access the data, the opportunities to commit fraud are limited.

"We have clients in the financial services industry who log a million hits per hour that are some form of attack," Lee said in a recent interview with me. "Differentiating signal from noise in a log that fills up a million rows per hour — that's really a full-time job, and not just for one person."

Several recent statistics show just how taxing fraud can be on a financial institution:

  • Forty-four percent of banks exposed to corporate or commercial credit card fraud suffered financial losses as a result of the crime in 2013, according to a J.P. Morgan report.
  • One in every 2,200 phone calls placed to a bank or retailer in 2013 was a fraud attempt, according to Pindrop Security research reported by American Banker.
  • 37 percent of U.S. consumers who had their identity stolen were compromised via their bank accounts, according to recent Bureau of Justice Statistics research.

The scale and scope of bank fraud is far-reaching. However, advanced banking analytics can provide industry leaders with an immediate response.

https://kapost-files-prod.s3.amazonaws.com/uploads/direct/1437748180-19-6376/Real-timeBankingAnalytics_Blog.jpgInstitutions already possess many of the keys to stopping fraud, with myriad advanced banking analytics, detailed historical data regarding previous fraud attempts, and a vast sea of unstructured data that offers insight into malicious actors and their data patterns. Here's how banking leaders can use those assets:

  • Search for patterns in transaction data, credit card history, signatures and loan applications. Whereas this was once a manual process of searching and comparing records, data analytics allows investigators to dedicate algorithms to transaction history. This frees up internal resources while ensuring an even deeper pattern assessment.
  • Apply analytics to the stream of incoming data. Banks must draw upon inbound data before it is stored to get ahead of criminals. Stream processing and analytics now allow fraud-prevention experts to see data in flight. "Patterns happen within 15–30 second windows, during which thousands of dollars could be lost," according to InfoQ. Banks need to be responsive in the same time frame and ready for bursts, not long arcs, of attack. Advanced banking analytics provide a new lens into the stream.
  • Expand the field of observation. Preventing fraud requires a wider scope of data that stretches beyond internal sources. Banks can compile a risk scorecard, which includes watch-list information and known fraudsters, to help developers of internal fraud detection algorithms proactively seek details about already visible external risks. When a connection appears, it allows security leadership to quickly remedy the situation.

Banks are working with the latest technology to help stop banking fraud, and they are increasingly able to assess, in granular detail, the actions of would-be perpetrators.

"A lot of the time there are telltale signals, even in the way [criminals] click on the website and type in the application," said Michael Li, founder of The Data Incubator, in a recent interview with me. "That's just one example of the trillions of signals banks are getting from their websites that, ... in the past, they haven't been able to leverage."

As banks look for criminal patterns with data analytics, they're preventing the next fraudster in real time. The key to stopping the associated losses is information. Advanced banking analytics provide the tools that give banks a data-driven advantage.