Predictive Analytics and Complex Event Processing Technology Move to Cutting-Edge of Financial Services Industry
Regardless of the industry vertical in which they operate, financial services institutions stand to make -- or lose -- a lot of money based on their ability to analyze past occurrences to predict future events. Traditionally, firms attempted this through planning exercises. They'd collect data, study trends and make one-time presentations to company executives.
But as data volumes continue to rise, firms are adopting increasingly complex predictive analytics models to analyze the large data streams and anticipate future behavior and events. And, instead of using these models on a year-to-year basis, companies are employing them day to day or, in the case of Wall Street, in real time. The increased prominence of these sophisticated tools and systems across the financial services industry suggests that predictive analytics have become "the next big thing" -- a hot technology with the potential to transform the business.
By leveraging predictive analytics and pattern analysis technologies, financial services firms are able to understand their customers, their operations and their markets in greater detail. Perhaps more important, they are able to identify and react to trends as they emerge, staying ahead of the curve -- and the competition.
A Customer Focus
In insurance and banking, predictive analytics often are used to segment valuable customers and anticipate the types of products and services that will attract their new business or increase their loyalty. "I see predictive analytics becoming more pervasive around the operations of financial services companies, beginning with customer focus -- as we go from a product-based industry to a customer-focused industry and from a product profitability standpoint to a customer lifetime value ambition," says Marty Ellingsworth, president of innovative analytics at ISO (Jersey City, N.J.).
At Branchville, N.J.-based Selective Insurance ($4.8 billion in total assets), predictive analytics are leveraged to improve pricing and identify and retain the most valuable customers, according to Daniel Bravo, SVP of Selective's strategic operations group. "The models aid the decision-making process in the field to get to the right pricing granularity for the right risks," he says. "That translates into both new business and renewal."
Selective began working on predictive models in early 2005 and was leveraging predictive analytics in business decisions by 2006, Bravo continues. The carrier worked closely with Deloitte Consultingon the initiative, with much of the development occurring in-house, he adds, declining to name Selective's other technology partners.
Bravo explains that the insurer has underwriters positioned in the field -- inside the offices of independent agent partners -- who make decisions around the business the company will and will not take on. "Through the use of predictive analytics, we are helping them make decisions with even better information," he says, adding that Selective employs predictive models to analyze data collected via policy applications to determine the quality of a given risk and how it likely will perform for the life of that customer's account.
A key part of the success of Selective's predictive analytics effort, according to Bravo, was the complete integration of predictive analytics with personal and commercial lines operations, rather than launching predictive analytics as an isolated, bolt-on solution. "One of the advantages that Selective has is our ability to bring [predictive analytics] into the regular insurance operations of the company and not disrupt those operations while still taking full advantage of the guidance and granularity the models provide," he says.
Bravo adds that ensuring that employees embraced the technology was the most important part of the transformation. Use predictive analytics to "offer guidance," he advises. "Present it as a tool and [implement] it in a way that people can take advantage of it without being Ph.D.s in anything."
Immersing the company in predictive analytics, rather than stapling the concept to the side of the enterprise, also was a key to success at Wachovia ($720 billion in assets). In part to facilitate the use of predictive analytics in building new customer relationships, the Charlotte, N.C.-based bank realigned its marketing group to include several other divisions, such as insight and innovation, e-commerce, and customer loyalty and satisfaction.
"We are aligning under more predictive analytics with more thought and resources dedicated toward it," relates Dan Thorpe, SVP of statistics and modeling at Wachovia. "We've put together all the groups that circle around that into one division."
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