Transforming auto finance: Gen AI's path to modernization

Capitalizing on the technology’s benefits starts with an unbiased look at existing core systems. Spoiler alert: It won’t be pretty.

It’s been a rough year for the auto finance industry: Inflation and continuing supply chain shortages have driven up vehicle prices and loan amounts, and consumers are feeling the squeeze.

For auto lenders, the macroeconomic pressures are coming at a precarious point: Rapid advances in generative AI have the potential to streamline loan experiences and manage rising delinquencies and costs—yet lenders’ outdated infrastructure limits their ability to capitalize on it.

Now, the lure of generative AI’s potential may be the motivation the auto financing industry needs to tackle its infrastructure limitations. By assessing their core systems and preparing a multi-pronged strategy to revamp them, lenders can create modern, resilient internal systems ready to maximize the use of generative AI to ride out variable market and economic forces.

Benefits that reach across auto lending

Much of generative AI’s appeal stems from its potentially broad reach through organizations. The technology is nothing if not flexible. Within the auto finance ecosystem, it can not only benefit all players—from dealers and lenders to loan servicers—but also help shape a frictionless buying process that closes loans faster and more efficiently.

Car buyers say that convenience is exactly what they’re looking for: Cognizant research found 66% of car buyers want a seamless end-to-end online customer journey. The reality, however, is quite different. Instead of streamlined loan processes with swift approvals and flexible terms, auto lending remains a largely offline, dealership-centric experience. A 2022 survey found only 30% of car buyers applied for credit online and just 12% signed paperwork online.

Generative AI: Corporate jack of all trades

Lending greater urgency for the auto financing industry is the reality that, when it comes to generative AI, pressure to keep up with the industry’s pace of generative AI adoption is on. JPMorgan applied to trademark a product called IndexGPT and is developing a ChatGPT-like service to select investments for customers. In September, Bank of America announced business clients will soon be able to interact with a tool featuring the same capabilities driving Erica, its virtual financial assistant.

What does it all mean for the auto finance sector? The hallmark of generative AI is its ability to create new content of all kinds, and for auto lenders that covers everything from customer conveniences to document and processing assistance for employees.

Here are a few of the ways generative AI can modernize the auto-loan experience and help lenders manage rising costs and loan delinquencies:

1. Augmented CX: Chatbots are already widely accepted by consumers. Generative AI can take bots to the next level, converting them into virtual assistants ready to respond more quickly to borrowers’ inquiries, answer complex customer queries and offer more self-service options for loan management. Later this year, digital bank One Zero expects to debut its generative AI platform capable of providing immediate replies to free-flowing customer queries. The service can analyze customer requests, comprehend their intentions, and provide responses to both general and specific inquiries regarding their accounts. Say goodbye to decision trees and predefined answers.
   
   
2. Personalized financing recommendations: A big part of generative AI’s appeal is its potential to make us better at what we already do, and for auto lending organizations, the implications for financing offers are immense. By analyzing historical data and borrowers’ profiles, generative AI algorithms can offer hyper-personalized loan and repayment options with customized loan amounts, terms and interest rates. Pentagon Credit Union (PenFed), the second-largest credit union in the US, is already calculating the technology’s potential to analyze “digital forensics” that will allow PenFed to deliver customized offers rather than costly marketing campaigns that promise the same rate to 500,000 members.
   
   
3. Streamlined underwriting: Generative AI’s machine learning capabilities allow lenders to perform advanced analyses and achieve a more informed view of applicants’ creditworthiness than just credit scores. By expediting loan decisions, generative AI can reduce processing times, lower lenders’ costs and create a better CX.
   
   
4. Contact center optimization: Customers today have little tolerance for a disconnected, non-personalized support experience. Generative AI can transcribe conversations and generate intelligent call summaries, allowing agents to focus more on engaging with customers and almost eliminate all post-call work. This has the promise of dramatically improving contact center efficiencies. Ally Financial launched an AI platform that automatically generates call summaries for customer care agents after every customer conversation.
   
   
5. Document processing: Unlike traditional AI, generative AI can extract essential information from large volumes of unstructured documents such as pay stubs and tax returns. This focus speeds a key aspect of lending: document processing. More important, the ability to perform semantic analysis to identify discrepancies and inconsistencies in documents is a game-changing opportunity.
   
   
6. Internal assistant: AI chatbots can simplify access to the maze of product, process and policy documentation within lending organizations. This access can massively reduce customer wait times for replies and ensure employees have consistent, real-time access to the latest information.

First up: Evaluate legacy core systems

Looming over the benefits, however, is the need for auto lenders to determine whether their core systems have the scale, flexibility and computational horsepower generative AI requires. In most cases, they won’t like what they find: Legacy infrastructures for origination, servicing and collections operations will typically fall far short of the necessary infrastructure and processing power.

Revamping core systems will require a multi-pronged strategy. The good news is that the journey of realizing the value of generative AI can proceed in phases.

Kick off the evaluation with consideration of these factors:

• What internal-facing opportunities can you focus on first? Take the time to navigate the challenges of system maturity and privacy and compliance issues on internal functions before branching out to support customer-facing capabilities.
  
  
• Is the data infrastructure up to date? Capitalizing on generative AI’s potential, especially for real-time decision-making, takes a centralized data management system that houses all information from the company, including the wide variety of legacy applications, in a single accessible and secure location.
  
  
• Will your platform scale? To make the most of generative AI-powered systems, you need the data accessibility of a cloud-based architecture. Building or migrating to cloud platforms will give your systems the ability to scale in response to rapidly advancing AI solutions.
  
  
• Are you ready for increased cybersecurity? Much of the data lenders will feed into large language models is sensitive. Safeguarding it requires additional cybersecurity measures.
  
  
• Do you have a robust API strategy? The fastest way to prepare legacy platforms for the sheer variety of AI solutions available is through a scalable, secure integration layer that bridges the old world and the new.

The bottom line

The potential impact of generative AI within the auto finance industry is profound, with far-reaching possibilities as a transformative tool. Indeed, it has the potential to become indispensable.

To reap its benefits, however, the industry has to first build the modern technology platforms that provide generative AI with the integration and processing capabilities it needs to churn through massive amounts of data.

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