Good lending decisions balance risk and opportunity. While experienced loan officers once relied primarily on their instincts, data now guides these choices. This shift has changed how financial institutions evaluate borrowers, leading to faster decisions and better results.
The Human-Machine Balance
Smart automation handles routine applications while keeping human oversight where it matters most. A regional bank client used risk analytics and automation and reduced manual reviews by 50% without increasing risk by letting their system handle clear approvals and declines automatically. This freed its experienced staff to focus on complex cases that need human judgment so incremental approvals can be identified in the gray areas without increasing adverse selection.
When a growing FinTech client combined automated screening with expert review, it found borrowers it had been missing. Its loan officers now spend time working with promising applicants who need a closer look rather than processing paperwork for obvious decisions.
Finding Patterns in the Numbers
New analytical tools spot connections that even experienced lenders might miss. One lender discovered that combining rental records and phone data with traditional credit reports helped it spot reliable borrowers earlier. Its system flagged potential issues before they became problems, allowing for early intervention.
Machine learning strengthens this approach by finding subtle patterns in large datasets. For example, A top 20 bank client found that borrowers who kept their phone bills up to date verses customers who paid inconsistently performed better than their credit scores suggested. This insight helped them adjust their criteria to approve more qualified applicants while maintaining strong portfolio performance.
Making It Work
Success requires clean, connected data. When a large credit union client tried to combine information from different sources, it first had to create a series of primary keys in disparate dataset so the information could be combined to give them a more holistic view of the customer. The solution involved five different legacy systems acquired in different M&As and resulted in a better underwriting model that increased booked accounts by 8% while reducing credit losses by 20 BPS.
Regular testing keeps these systems sharp. Lenders check their models under various conditions and monitor ongoing performance. One institution caught an emerging risk pattern when it noticed it had a number of consumer indexes built on the housing attributes that were artificially inflated by rental purchase activity. . It adjusted its criteria before the problem affected its portfolio.
Staying Within the Lines
Automated decisions must be fair and transparent. Lenders need to show regulators exactly how they make their choices. We’ve helped numerous banking clients solve this by building advanced reporting and dashboarding features to show compliance with their internal P&Ps and by displaying their various forms of fair lending analysis. They can now explain every decision clearly while still moving quickly on applications.
The Results
Lenders using these methods see concrete improvements. Beyond faster decisions, they’re finding qualified borrowers they used to miss. One of PAG’s longest running clients has used our data warehousing, analytics, and reporting to increase its approval rate by 30% over time while keeping losses stable. More important, they’re helping good borrowers get the credit they deserve.
Transform your lending decisions
Schedule a consultation today to discover how our proprietary analytics tools can help you find qualified borrowers you’re currently missing while strengthening your compliance reporting.
