Make Instant Approvals Your Path To Loan Growth
Zest AI team
September 3, 2021
Credit unions know that digital is a crucial channel for growth and customer loyalty, but they often struggle to succeed in the digital lending game, according to a recent report from Forrester. Chris Skinner, publisher of the popular Finanser blog says, "What I see is banks maintaining systems and approaches, but not innovating. They need to be re-designing products and services around customer needs.
Easier said than done. Consumer expectations are only increasing, driven by new fintech startups that are quickly delivering digital experiences that delight. Credit unions haven't faced so much competition and pressure to execute, especially in loan growth as deposits hit record levels.
Give customers the digital experience they want
One area where credit unions can far exceed consumer expectations is around loan decisioning speed. A 2020 Zest AI/Harris Poll Consumer Credit Survey revealed that 72% of Americans think that credit decisions should be instantaneous, taking no longer than a few seconds, given todays's advances in computing and automation.
But only 30% of consumers expect to be approved or denied immediately. In fact, roughly 1 in 4 Americans expect to wait a day or more to get approved or denied, with close to half of Gen Z (people under 24) saying this. Why not exploit this gap in expectations?
AI-driven lending is proven to deliver more accurate credit risk scores, which means underwriting teams can delegate more of the approval or denial decisions to the algorithm. Zest AI software has helped some credit unions take their auto-decisioning rate from zero to 30% literally overnight. Your institution may already be auto-decisioning 50% or more, but in highly competitive products like indirect auto lending, every incremental point of improvement helps. When automated approvals can go out after your underwriters go home for the day or throughout the weekend, you've got lending that transcends time and space.
Does your underwriting team need to fear being replaced by an algorithm? Hardly. Reducing the number of applications that go to manual review enables more efficient underwriting, better customer service, and allows you to grow faster with the same operational resources. Here's what David Blizzard, CEO of First Service Credit Union has to say: “Let's say that over a six-month period we go from 30% to 60% automated decision-making. That means my folks can spend a lot more time helping more people building performing loans. But even excluding that, we're going to be growing 10% to 15% a year. Within a year or two, we’ll be so much larger and decisioning so much more. A thriving organization always has opportunities for smart people.”
“Let's say that over a six-month period we go from 30% to 60% automated decision-making. That means my folks can spend a lot more time helping more people building performing loans. A thriving organization always has opportunities for smart people.” - David Bleazard, CEO, First Service CU
So, what's the best way to build a real-time auto-decisioning engine with accuracy? Many credit unions are turning to machine learning (ML) models, which use more data and better math than traditional scores and scorecards. ML models can more precisely rank applicants across the risk spectrum. Greater statistical accuracy allows you to set tighter auto-decisioning thresholds with confidence.
Every lender wants something different from their automation play. The main benefits of auto-decisioning with ML are:
Each of these benefits have an impact on customer or member experience with measurably less friction and more time for team members to provide higher-touch service to the people who need it most. And, in auto lending, if you can return a decision quickly and with confidence, dealerships will send you more volume over time so you gain market share.
Bottom line: To keep up with rising consumer expectations and fend of fintech competitors, it's critical to boost auto-decisioning capabilities using new tools and technologies.
Zest AI team
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