Applying Data to Rules


Leverage data infrastructure for risk management, compliance

By Ellis Booker

Much of the recent fevered interest in data analytics in financial services has been around the use of advanced analytics for gleaning timely insights to gain new customers and make current ones more profitable. Although these marketing-related applications for generating greater profitability can be useful, they are by no means the only ones.

Equally important, and particularly relevant for community banks without gigantic marketing budgets or immense IT staffs, is a variety of operational uses of analytics, banking technology consultants say. The operational uses for data analytics can potentially range from fraud detection and risk management to regulatory compliance.

Of course, the ongoing costs and time burdens of regulatory compliance are major obsessions for community banks of all sizes. Within the prudential regulation and consumer compliance space, these range from asset-liability rules and liquidity requirements to consumer-protection and fair lending laws. Technology companies are earnestly researching how to develop systems to meaningfully apply data analytics to reduce costs and risks and increase timely operational sights for banks in the resource-consuming area of regulation and compliance.

Whatever a community bank’s size and scale, manual compliance-checking systems will inevitably give way to automated ones to manage the increasing volume of ever-changing regulatory mandates and expectations, says Pam Perdue, executive vice president and chief regulatory officer at Continuity in New Haven, Conn., an ICBA Preferred Service Provider for regulatory compliance software. Perdue points out that the financial code contains more than 14,000 parts and that, on average, about 300 of those items in the code change in some way every year. Machines, rather than humans, are best at tracking such changes, she says.

Systems available now
Some industry technologists point out that although some compliance-focused data analytics systems exist for the largest banks, few have yet made their way downstream to the community bank sector. The best automated systems for community banks commonly track compliance tasks rather than apply data to achieve more efficient and insightful regulatory processes and outcomes.

Vincent Hui, senior director at Cornerstone Advisors Inc., a consulting firm in Scottsdale, Ariz., says cutting-edge regulatory reporting and analysis systems aren’t likely to be a priority for most community banks in the grand scheme of things. Rather, Hui believes, the greatest benefit of leveraging data analytic computing wouldn’t be for regulation and compliance per se, but rather for more broadly improving overall risk management and operational business practices, including those for safety and soundness and compliance.

As Hui explains, generally understanding risk and loss exposure, for example, means identifying internal flaws with processes that could lead to errors and losses that potentially lead to safety and soundness problems and regulatory troubles. Take the area increasingly regulated by the Consumer Financial Protection Bureau of handling customer complaints, he points out. By capturing and reviewing patterns in complaint information—and, crucially, how complaints are resolved—a community bank could not only appease nettlesome regulatory requirements but also potentially identify any systemic service problems that should be addressed by adjusting its training, products or policies.

Or as Hui adds, broadly analyzing such customer interactions could let a bank step back and ask, “Is there an underlying theme here, either a breach of regulation or a breach of bank policy?”

“Is there an underlying theme here, either a breach of regulation or a breach of bank policy?”
—Vincent hui,
tech expert

Prepare for tomorrow
The consensus from technology consultants is that community banks need to develop comprehensive IT strategies, not pinpoint solutions, that begin to consider how they might apply data analytics across regulation and compliance operations to achieve strategic and tactical goals. As Hui puts it: “Our view is that if you design your overall analytics strategy, your overall data warehouse strategy, you should be able to get some pretty significant benefits, as opposed to going out and trying to find [a point solution or separate set of software tools].”

For Hui, prime activities for community banks to first harness the opportunities of more data through analytics for regulation and compliance will center on fair lending and credit-loss management. After addressing customer profitability, transaction monitoring for traditional transaction fraud and Bank Secrecy Act anti-money laundering—though involving more cost reduction and financial policing than true safety and soundness or consumer-protection compliance—are other regulatory-related activities where data analytics are most likely to be initially applied, he says.

But data analytical systems ideally can be applied not only toward past events, such as losses over time from X or Y product or from a particular customer segment, but also to progressions that reveal future events. For regulatory purposes as well as operational purposes, these systems are adept at “looking backward in terms of historical losses and looking forward in terms of future volume,” Hui says. With this backward and forward visibility from various data that community banks have, he says, more informed decisions can be made about the risks banks bear, ultimately with the goal of answering the question, “Am I OK with that exposure?”

Paul Schaus, president and CEO of CCG Catalyst Consulting Group in Phoenix, Ariz., agrees, adding that applying analytics for its potential predictive power will become increasingly important for every bank operation, including regulation and compliance. Reporting on what has happened or is happening—with customers or systems—is “looking in the rearview mirror,” he says.

But Schaus and Hui acknowledge that today’s data analytical systems aren’t inexpensive to buy or build in-house. Moreover, applying data analytics to risk and compliance isn’t a top priority for core system providers or other software developers, Hui says. Indeed, larger community banks with larger operations and vaster volumes of data flows to tap will see the benefits of systematized reporting and compliance-analysis tools first, as they often do with other technologies, he says.

Hui’s advice is for community banks to take a step back and consider opportunities for regulatory efficiency by using data analytics, even if their greatest priority for IT projects might be to drive customer profitability and new revenues. Perdue says community banks should start looking at data analytics by reviewing the regulatory end results they want to achieve, including the reports they produce for examiners. She also recommends using automation to develop standardization wherever possible to achieve repeatable outcomes to measure regulatory performance over time.

Ellis Booker is a technology writer in Illinois.