Seeking an edge: Exploiting alternative big data sources and customer data for financial gain
The three vectors driving the need for big data methodologies in financial services are lucrative, hazardous, and mirrored (to a lesser extent) across verticals. In finance, the lure of prescriptive analytics, data science, and cognitive computing is fueled by the following:
Formal legislation: Myriad national laws compel operators in the financial space to combat financial crimes. Spurred by the tenet of “know your customer,” or KYC, financial organizations are forced to learn as much as they can about those they serve to prevent money laundering, fraud, and other financial misdeeds.
Regulatory adherence: Several regulatory accords dictate how financial organizations can use the data gathered for issuing product and services; some of these mandates apply to the use of data gathered to counteract financial crimes. Paradoxically, organizations are almost impelled by law to use big data techniques, yet exponentially incur risk for doing so.
Competitive advantage: The need to raise profit margins, reduce costs, and do so more effectively than competitors can is a horizontal concern that’s particularly acute in finance.
Although most organizations realize that failure to sate these demands results in penalties and decreased market share, very few know that fulfilling them can produce a profound profitability. “There’s also a business opportunity that they’re missing by not truly understanding who their customers are, their relationships, and things that are outside the pure banking relationship itself that may impact a business opportunity with that customer,” noted Marty Loughlin, VP of financial services at Cambridge Semantics.
Truly knowing one’s customer—and managing alternative big data sources across business units for this purpose—catalyzes significant revenue generation across a range of industries, not just financial services.
Alternative data sources
The rapid incorporation of alternative data sources alongside traditional ones is particularly cogent in finance because most organizations “pretty much have the same information today,” Loughlin noted. “They all have the same market trends, sales, and company performance data. They’re all running models off the same data; no one really has an edge.” Alternative data sources, however, provide that proverbial edge by equipping organizations with information others lack. According to Loughlin, traditional financial data sources include information organizations choose to make public, such as production numbers and company reports, as well as market trends.
Alternative data sources, however, are based on big data and aren’t as easily attained or analyzed. Examples include satellite images of retailers’ parking lots, which might indicate micro- or macro-level trends throughout the industry, and weather data that can presage regional issues with agriculture or factory production. Social media and other means of sentiment analysis are also alternative sources contributing to what Loughlin termed “alpha”—“knowledge you have about potential performance the market doesn’t have.” Loughlin mentioned an airline use case in which on-time arrival data was combined with social media information and stock prices so analysts could take certain dimensions, do feature engineering, feed that to machine learning, and try to predict whether the stock was going to move based on their on-time arrivals.
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