For those outside the industry, data analytics seems to go hand-in-hand with the financial services vertical. These outsiders aren’t wrong, quite the opposite. They often are unaware of just how many applications advanced data analytics can serve within finance.
In a Money 20/20 session titled, “Enabling Credit for Underserved Borrowers with Advanced Data Analytics,” a panel of experts from around the credit world gathered to discuss the advantage of data analytics in shedding light on those who had previously fallen through the cracks of traditional data, specifically those with little or no credit history.
Although nearly everyone will have some sort of interaction with financial credit over their lifetime, most people are unable (or unwilling) to fully grasp its often-opaque inner workings. Through the use of machine learning, these data points are processed into a more user-friendly format, allowing for transparency that previously was locked behind confusing algorithms.
“The benefit of machine learning is its interdependency, and the way you’re able to see attributes affect one another,” said Steve Allocca, VP & GM global credit at payment giant PayPal. “When explaining these concepts to those that may not be familiar with it, it’s important that the data forms a narrative, and that it tells a story. The beauty of this machine learning is explaining credit risks through the stories we’re able to pull from the data.”
The raw data in financial lending is, undoubtedly, a valuable resource in and of itself, but its full potential can’t be unlocked without combining this data with a use case and the expertise of a dedicated data scientist.
“If you put those two things together, you have a meaningful solution,” said Bill Parsons, GM & SVP of product & analytics at Envestnet | Yodlee. “To add even a third factor in, it helps even further to employ the use of someone who is close to the regulatory environment and the winds of change therein.”
The world of loans is one that many would say is intentionally confusing. The obfuscation present in the lending environment is slowly being lifted, however, by analytics tools that allow both customers and lenders to better understand the risks—and yes, upsides—of these transactions.