Nobel-winning economist Paul Samuelson had once quipped that Wall Street had predicted nine out of the last five recessions. While the markets may be touted as cautiously pessimistic, it is evident that we are on the cusp of an economic downturn.

The use of AI in credit decisioning can be a game changer for credit unions looking to support their members, especially in light of recessionary trends. Instead of tightening their belts, credit unions can use AI to tighten their underwriting. Through the use of AI, credit unions can provide members with fast access to credit, customized loan options, and transparent and inclusive credit decisions.

But this transition to AI requires careful planning. We must understand and learn from the experiences of risk leaders so that we can take this evolution to fruition.

In this webinar, we bring you perspectives from seasoned risk leaders on how to make this transition. We will cover the following:

• Creating a sustainable, recession-resistant lending toolkit

• AI-enabled fair, transparent lending for enhanced customer relationships

• Differentiating between credit-worthy members in need vs. high-risk ones

• Building early warning systems

• Help realign risk strategies as per the changing economic climate

A recent survey of borrowers across the USA revealed that only 47% of Gen Z respondents in the USA claimed to have an account with a traditional bank, neobank, or Credit Union.

The above stat reflects the emerging disconnect between traditional lenders like Banks and Credit Unions, and point to the generational differences in banking habits and banking expectations. With Gen Z and millennials set to become the most important consumer groups in the next 10 years, lenders need to take into account the fact that new age borrowers have expectations of personalized recommendations, instant decisioning speed, and an easy application process.

The answer to these expectations boils down to two words: Artificial Intelligence.

The way we lend is evolving so quickly that those lenders that try to understand the lending landscape better, and go beyond normal data sources and lending models to gain a deeper insight of who they are lending to, are the ones who are able to grow sustainably. Leveraging and consolidating multiple sources of seemingly unstructured data into actionable credit decisions that help lend to more deserving borrowers and personalize offers in line with modern day expectations is what AI strives to achieve.

In this webinar, we will be deconstructing the expectations of the new generation of borrowers, and how traditional lending methods may fall short in fulfilling these expectations. We’ll showcase how AI has had on transforming lending infrastructure for traditional lenders, and elucidate the impact of AI-enabled lending through real-life examples by lending leaders and AI lending practitioners.

Points to be Covered

• The evolving lender demographic: Advent of Gen Z

• Expectations from the modern lender: Personalization, Instant Service, and much more

• What’s taking so much time? The bottlenecks in the current lending ecosystem

• Looking at lending from the AI lens:

• A blow by blow comparison with current lending methods

• Solving the personalization expectation with richer data

• Solving the Instant service expectation with self learning custom models

• Beyond expectations: How AI is changing the growth story for traditional lenders

• Measuring the impact of AI for modern lending with real stories

The conversation around fair lending has gained traction in the background of resurgent campaigns for an end to racial and gender bias in recent times. Fair lending in its truest sense requires lenders to adopt the least discriminatory alternatives when it comes to processing or decisioning a loan application. 

AI models hold great promise for identifying and enabling traditionally underserved groups.

Through them, we can minimize subjectivity and bias, generate more consistent outcomes, increase efficiency, and make better decisions. Algorithmic and AI-based systems, when properly conceived and managed, can expand opportunities. 

That said, transitioning to AI-based algorithms requires the right regulatory lens and framework. An Advisory from the Consumer Financial Protection Bureau on digital redlining and its impact on fair lending practices highlights the urgent need for the right regulatory framework.

This webinar will attempt to address the questions around the usage of AI to enable fair lending, elucidate how explainable AI supplemented by the right safeguards will serve to supplement compliance guidelines around AI in lending, and get insights from the impactful stories of practicing lenders who have implemented AI in their lending processes.

We will also be attempting to answer questions on fair lending testing and disparate impact analysis done by Scienaptic, demonstrate how Scienaptic is making an impact on fair lending practices, and discuss in detail the recent advisory by CFPB on AI usage in lending, and how it does more good than harm for the AI lending landscape. 

From a simple google search to amazon recommendations to Netflix browsing to Uber rides - AI is already embedded in our lives and shaping our experiences. However, AI in lending is still far from becoming mainstream. Credit experience and availability for borrowers did not significantly improve despite advances in technology and data availability.

This is where AI offers exciting opportunities. Providing customers/ members with the right credit products, approving more members, and providing guidance as they move through their financial lives - from buying cars to saving and investing - lines up perfectly with any lender's mission.

These AI-enabled opportunities will help lenders enhance customer lives, build stronger relationships with their customers and capture a greater share of wallet for their financial products.

The webinar features insights from early adopters and visionaries who are powering their lending processes with AI to say more ‘yes’ to customers and create unique experiences for them.

Highly seasoned risk leaders will share their personal stories on how they are using AI to reduce friction, eliminate costs, and mitigate lending risks. More importantly, they will share recipes on preparing for growth.

Key takeaways: 

• Demystifying AI in credit through real experience sharing

• Roadmap to safely increasing approvals and automation

• How to improve the borrower experience