Seeking an edge: Exploiting alternative big data sources and customer data for financial gain

Risk calculations

Alternative data sources—and the holistic understanding of one’s customer they fortify—is a key component of minimizing risk for financial institutions. Similar to the way in which Seacoast is calculating customer lifetime value, organizations can also determine the potential risk posed by customers. The challenge in doing so, however, involves viewing a specific customer across departments and business lines—which has historically proved exacting in finance. “Personal banking, mortgage, credit cards, small business—those are all separate entities within various financial organizations,” Norvell said. Linking various databases across these business lines involves standardizing aspects of metadata management, business terminology, and data models.

Ideally, the more databases and sources (both alternative and otherwise) utilized, the more information organizations have available to “start doing risk calculations,” Aasman posited. Again, statistical AI models are influential in the predictive and prescriptive analytics necessary to determine signals for risk. If one looks at the world as a set of events, Aasman said, it is possible to ask: What are the signals that indicate if A and B happen, that C is likely to happen? In finance, those signals are applicable to fraud, money laundering, or credit card payment failure. They’re also applicable to predicting churn, which is valuable to organizations across industries.

Moreover, those signals are responsible for identifying opportunities for cross-selling and upselling, especially when utilized at scale with alternative big data sources for a complete understanding of one’s customers.

In retrospect

The legal and regulatory demands of finance were the impetus for banks attempting to “have a comprehensive overview of everything that ever happened to their customer, independent of what business lines are serving him,” Aasman ventured. However, they also serve as the means of profiting from such intimate understanding of customers via the extraction of alpha, the determination of customer lifetime value, and the identification of horizontal business opportunities across departmental units. With parallels between these opportunities (and opportunities for risk mitigation) in financial services and other industries, these examples will surely be scrutinized and emulated in other verticals too.