More mortgage lenders are finding a competitive advantage hiding in their own data, according to a new white paper from Redefining Business Intelligence (RBI), a data science company that uses AI and experienced business analysts to pull actionable insights from very large data sets.
The new white paper, “The Critical Need for Data Science in the Modern Business Enterprise,” defines data science for the mortgage industry and offers options for lenders seeking more insight from the data they already own.
“BI has seen significant growth over the past two decades, transforming from a specialized IT tool to a core asset for businesses globally,” says Laura Lasher, co-founder of RBI, in a release. “This innovation democratized data access, fostering a data-driven culture within organizations. Unfortunately, the explosion of data that resulted has overwhelmed executives by leaving insight buried in massive data dumps.”
Mortgage lenders are concluding that their big investments in BI software have not resulted in measurable ROI or competitive advantage in the marketplace.
Managing the overwhelming volume of data has been an ongoing challenge but it’s on e that can be overcome with a better approach. Part of that involves new AI technologies, which can allow business analysts to make sense of very large data sets.
But despite current technological advances, it still takes a human to harvest actionable insights from computer analysis. Without human oversight, the compliance risk and high costs of missteps render BI more of a problem than a solution.
“We have access to some very powerful and exciting technologies today,” says Scott Schang, co-founder of RBI. “But they cannot do the job by themselves. Many companies are trying that today, but we’re just not there yet. In our industry, with its significant regulatory oversight, it still takes trained business analysts to ensure that the lender is acting on actual insight and not an AI hallucination.”
Experts from many industries have argued that without proper support from data science departments, businesses risk drowning in data without gaining actionable insights. The first challenge is investing in and staffing such a department. Even when a company does invest, these departments often convert large data sets into spreadsheets that do nothing to make insights clear.
The resulting “analysis paralysis,” where decision-makers are overwhelmed by information and struggle to identify critical insights, is now widespread.
“We call our solution Augmented Analytics,” Lasher says. “Our data scientists will make use of AI to automate data collection and initial analysis, but then task human analysts with harvesting insights and quality control. It’s how we’re redefining BI and unlocking the power in the lender’s information.”
Photo: Markus Spiske