Innovation in Lending

A Guide to Our Most Popular Insights in 2020

Ken Garcia

December 29, 2020

This widely circulated meme perfectly captured the state of technology adoption in 2020:

Amid the acceleration of all things digital, adoption for machine learning and AI also increased at warp speed and showed no signs of slowing down. According to McKinsey’s The State of AI in 2020 report, companies plan to invest even more in AI in response to the COVID-19 pandemic. And a survey from the World Economic Forum revealed that a significant number of executives from 151 financial institutions in 33 countries say that within the next two years, they expect to become mass adopters of AI and expect AI to become an essential business driver across the financial industry.

As financial institutions look for faster and more effective ways to assess credit and loan eligibility, machine learning models quickly become table-stakes to adapt to the new normal. Machine learning allows lenders to model using more variables, creating a more holistic, clearer picture of an applicant.

With machine learning models, lenders can identify risky borrowers who may have looked good on paper and swap them out for better, creditworthy borrowers overlooked by traditional underwriting techniques. Saying yes to more borrowers up and down the credit spectrum ultimately delivers growth, increased productivity, and more inclusivity.

We write extensively about the benefits and best practices of machine learning underwriting. We wanted to share our top pieces on these topics to inspire your AI strategy for 2021.

For the forward-thinking executive that wants to learn how to use AI for consumer lending:

Look no further than our special report: How AI Is Reshaping Lending, an in-depth guide on using ML to strengthen lender yields, find good new borrowers, and broaden credit access for consumers, especially in near-prime or subprime segments.

For the data-scientist or data-obsessed VP of Credit Modeling

Adopting ML has been held back by the technology’s “black-box” nature—you can’t run a credit model safely or accurately if you can’t explain its decisions. Read our white papers Most AI Explainability Is Snake Oil. Ours Isn’t. Here’s Proof and Robust Explainability in AI Models to understand how the black-box problem is a thing of the past. Explainable ML can provide consumers with more approvals and more precise denial reasons.

Building the business case: industry benchmarks and consumer sentiment

If you’re just starting to build the business case for your ML project and need some data points, take a look at these special reports: Credit Modeling and The Need for Speed from Cornerstone Advisors and the 2020 Zest AI/Harris Poll Consumer Credit Survey.

Also, check out how Discover and Akbank are leveraging AI to meet consumer expectations and deliver better results for every lending objective.

For the exec that wants to correct longstanding fairness and inclusion issues

The path to financial inclusion begins with re-evaluating the status quo and understanding these new technologies and methods. This white paper, The Path to a Fairer Credit Economy, outlines a new approach to help banks move beyond the status quo to lend more inclusively without taking on added risk. You can also watch The Fintech Agenda for Inclusive Finance, a LendIt Fintech webinar, to learn how financial institutions can drive more fairness and inclusion in their business.

Bottom Line: The Future of Lending Lies in AI and Machine Learning

Regardless of where you are on your AI/ML journey, now is the time to think big and act. The McKinsey State of AI 2020 report suggests that in 2021 we could see a divide between AI leaders and companies still struggling to capitalize on the technology. In a future characterized by uncertainty, only financial institutions and credit unions that embrace these advanced technologies will be able to weather future storms.

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