Credit & Risk
A Lender's Roadmap to AI Adoption
February 17, 2021
AI adoption in banking and lending is on the rise, fueled by the pressing need for more digital efficiency. Half of the financial organizations in a recent State of AI report from McKinsey & Co. said they’ve adopted AI in at least one business function. More than a quarter plan to invest even more in AI for 2021.
One of the most powerful use cases to emerge for AI/ML is credit risk underwriting. Machine learning-based models are more accurate at predicting default risk and credit eligibility by generating a more holistic view of an applicant. How? By using more credit variables and stronger math than traditional scoring methods to generate a truer representation of borrower risk. Traditional scoring methods are dependent on a couple of dozen variables, while ML models can incorporate hundreds or thousands of data points. This also makes ML models more tolerant of messy data or shifting market conditions. Unemployment is close to twice what it was before the pandemic and millions of Americans still face evictions, yet FICO scores are at record highs. Something is amiss.
What ML models are great at is replacing risky borrowers who may have looked good on paper with more good borrowers overlooked by traditional underwriting techniques. For lenders, ML models boost approvals with no added risk. With more confidence about whom to say yes to, ML also drives higher levels of auto-decisioning. And, by saying yes to more borrowers up and down the credit spectrum, you make a big move toward greater inclusivity.
Even with an easily justified business case, AI projects can struggle to get from proof of concept to production. With AI project failure rates nearing 50%, the need to follow a proven roadmap is critical. So, if you’re a forward-thinking leader on that path to AI that wants to stop and ask for directions, you’ve come to the right place.
So, what does a successful lender’s AI adoption roadmap look like?
Our latest guide shares key milestones and distills best practices at each stage from lenders who have moved to AI-powered underwriting to make more accurate risk assessments, accelerate modeling, and automate lending decisions. Equally as important, you’ll walk away with a clear understanding of how to demonstrate ROI from your AI investment.
Download our latest guide that provides a step-by-step approach with key considerations for each stage to help you:
- Select the right goals and metrics to establish ROI
- Communicate AI’s impact to stakeholders (Executives, Credit Risk, Underwriting, Modeling, Regulatory, and IT)
- Choose a Path to AI Adoption (Buy vs. Build)
- Execute your AI strategy (Project timelines, data considerations, and more)
- Optimize your AI investment
Download A Lender's Roadmap To AI Adoption
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