How to Manage Data to Prevent Digital Fraud

Banks are spending lots of money fighting fraud, but security experts say they could do better when it comes to mitigating fraud and detecting money laundering.

"There's a lot being spent by bankers to keep these risks at bay," says Michael Versace, a research director at IDC Financial Insights. Versace says global spending on bank risk will be about $70 billion this year, with security spending reaching about $28 billion. He says that's growing at about 15 percent per year, so the anti-crime tech market looks bullish.

This level of investment suggests there's plenty of awareness of crime such as fraud and money laundering. But experts say what needs to change to meet the new threats is the strategic approach. Banks will need to organize the way they fight fraud and other crime around the entire company, breaking down departmental silos and bringing together isolated fraud fighting efforts such as anti-money laundering, insider fraud and payments fraud.

"On the organizational level, banks will have to look at their crime prevention capabilities across areas to draw together their risk and compliance functions toward the common goal of fighting financial crime," Versace says.

Versace and Bob Beckett, senior director of worldwide data and insight for Dun & Bradstreet (D&B), spoke with BTN this week about emerging fraud and crime threats, and offered some tips as to how banks can change their approach to improve effectiveness of prevention.

D&B recently published a set of tips to help combat fraud, most of which (somewhat self-servingly) rely on better data analysis and improved aggregation and sharing of information across departments. D&B says three of the keys to fighting fraud are acting quickly to spot potential fraud at the point of sale, verify fraud on the spot via reliable business data, and continuously track fraud to improve prevention.

The good news when it comes to data is most banks are already improving data management for other purposes. That includes new analytics tied to structured, internal transactional data; and unstructured external sources such as social media.

The data centralization projects that are underway at banks across the globe to centralize disparate sources of internal and external data are being used mostly for credit risk and marketing. This integration, which creates more breadth and depth of user and transaction information, can make the behavioral and probabilistic modeling used by new anti-money laundering tech to locate potential threats more powerful, because more relationships are taken into account.

Beckett says bank data is still distributed across departments at many institutions, and the centralizing of data that's helping marketing can also help ensure the legitimacy of consumers, internal users and third parties, as well as track transactions and communication to spot trouble.

"What we find with customers [bank clients of D&B] is they have disparate databases across the organization, in the sales, retail, investment or leasing channels. Often what we see is [the bank] has acquired or been acquired and there are different back end systems with different data sets."

Beckett says there are subtle differences in the way customers engage different channels and departments at a financial institution — forms may be slightly different, or procedures to register or vet potential customers may vary. These discrepancies can contribute to the siloing that makes aggregating data difficult. Versace says the manner in which different departments and different types of crime are investigated and reported can be standardized, along with corrective actions. "Banks can look at a common workflow to support incidents that are flagged for follow-up investigations, as well as look at common factors [to spot fraud] across the enterprise," Versace says.