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

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Cross-departmental opportunities

Implicit to knowing one’s customer, calculating customer lifetime value, and availing oneself of alpha is the ability to rapidly integrate, aggregate, and exchange relevant data across business units for the end user. Although this task is difficult for most firms, “Finance, like any other enterprise, has the trouble that their customers are in silos, especially in banks, where they’re in different business lines,” Jans Aasman, CEO of Franz observed. Hardly any bank can, with one query, see a comprehensive overview of everything that a customer knows, he noted. There are numerous approaches for overcoming these silos, some of which involve cloud data stores, comprehensive data fabrics, data lakes, or knowledge graphs. Organizations deploying these mechanisms or others for horizontal department or business line access will almost surely identify opportunities for financial enrichment.

Loughlin added that it is not uncommon for large financial institutions to have different customer databases for 401(k) plans and brokerage purposes. “Think about it; if you’re a 401(k) customer that’s heading into retirement and you’re going to start withdrawing money, [your 401(k) provider] would like to know that on the brokerage side, so that the money goes into your brokerage account, and not to someone else.”

The data management nuances of this use case are critical. Capitalizing on this opportunity not only requires linking together information between internal databases, but also doing so for timely action across business units.

For example, said Lee, certain results from Seacoast’s customer lifetime value model are fed into its marketing department for automated offers.

The ability to link data together in this example and in others noted above enables more proactive interactions, said Craig Norvell, VP of global sales and marketing at Franz. Norvell said this might include providing new services to the customer or reaching out to that person about a line of credit. “Normally, you’d be passive [about] that and need someone to come looking for it; instead, you can actually go out to them and suggest things.”

Identifying alternative data sources

A key facet of obtaining alpha and exploiting it with contextualized understanding of internal and external sources is actually identifying the types of alternative data sources that add value for specific use cases. The legal rigors for KYC have propelled numerous financial institutions to subscribe to services such as those provided by an extremely prominent media entity that’s “creating knowledge graphs around companies and the knowledge they can find around a company, whether that’s financial data, or in-the-news data, or whatever,” Aasman stated. The main customers for that are banks and financial institutions because they need to know, he added. The intricacy of KYC becomes magnified for business or corporate customers, which often have several subsidiaries and various identifiers for different divisions. This reality is pushing the movement for Legal Entity Identifiers, which would provide universal identifiers for businesses to simplify this concern.

In other instances, the types of alternative data sources impacting an organization are dictated by a particular use case. Loughlin described a potential use case in which a nationwide, specialized sporting agency seeking to raise funds for a major international competition could identify company directors who played the sport in college, or who have children currently playing in college: “You’re starting to look at these connections, so you may reach out to Thomson Reuters about people who are on boards, you might do web scraping looking at people’s interests, or you might look at LinkedIn for where they went to college. There’s a lot of different information out there that you could bring together to build a profile of people who might be more likely to make a significant donation.”

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