Understanding the Role of Big Data in the Lending Industry
For lenders and financial institutions, accuracy and predictability are key drivers of success. Being able to predict when a borrower will repay a loan or when it’s likely to be sent to a collector can mean the difference between running a profitable business and losing the shirt off your back. And at the heart of insights like these is data – big data, to be precise.
Exploring the Relationship Between Big Data and Lending
In a traditional scenario, a person goes to a lender and asks for a loan – say, for a new car. The lender presents an application, asks the borrower to fill out some information, sign some pages, and wait for an approval.
Over the course of a couple of days (depending on how busy the lender is), they review the application. But more important than the application, they pull up the borrower’s credit score. And in most cases, the credit score – combined with a little information on income and other existing debt – will determine whether the individual is approved for the loan.
In all honesty, the traditional loan approval process is shortsighted. This is why many lenders are now reaching beyond this restricted method and relying on more data to point them in the right direction.
“We don’t have a ‘minimum Fico score,’” says Brent Sergot, World Omni Financial Corp.’s group vice president. “We review the loan application holistically because we realized Fico score is just a small part of the application process.”
Sergot isn’t the only one. Big data is playing an increasingly important role in the auto finance industry, as well as in other areas – including home mortgages, business loans, and personal loans.
Take RISE Credit as an example. This company addresses a common pain point among consumers – long and cumbersome approvals – and speeds up the loan application process to just a few hours. They do so by using proprietary algorithms and advanced scoring methods that look past credit scores and acknowledge a borrower’s true risk and opportunities.
Big Data Scoring, a cloud-based credit decision engine, has developed one of the leading technologies and claims it’s working with some of the world’s biggest banks and insurance companies.
“We develop and deploy custom scoring models that combine a lender’s internal data with thousands of pieces of external data such as location based information, web search results, behavioural tracking, device technical details, mobile app data and much more,” Big Data Scoring explains. “This enables lenders to accurately predict borrower payment behaviour, helping then make informed and more profitable credit decisions in real time.”
Then there’s artificial intelligence (AI), which is always closely intertwined with big data. Many of these same lenders are using AI engines to learn from past lending decisions and gain a more accurate picture of who applicants are and how they may behave.
Some companies are even using AI to detect fraud by comparing the behavior of applicants with baseline data for normal customers. This helps them single out the outliers and avoid the headache of working with these scammers.
Lenders Enjoy Better Insights
We’re still far from a perfect system or model that’s 100 percent accurate – and we’ll likely never get there – but big data is getting us as close as we could ever hope for.
At the end of the day, it’s all about gaining better insights in an effort to make more accurate decisions that remove risk and bolster the bottom line. Big data is helping lenders do all of the above, which gives data scientists plenty of hope to believe that analytics, AI, and the technologies that accompany them will stick around for the long haul.