Generative AI: what’s in it for me? Everyone, including auto and equipment finance providers, wants to know. In my view, it is a technology that will have far-reaching impacts on the lending and leasing business, bringing efficiencies and better experiences for those organizations that incorporate it throughout the value chain.
A few weeks ago I put ChatGPT to the test myself. I needed to write a new charter for the board of directors of a nonprofit I’m on and was curious to see what ChatGPT would come up with. Did it generate a perfectly formed statute that I could use as-is? It did not. But it gave me a great starting point and significantly reduced my time on task; by my estimation, it saved me several hours. With a few tweaks and the addition of details specific to our situation, it helped me write a very useful document that I was able to put on the whiteboard.
By now, car and equipment lenders and lessors have probably heard hundreds of anecdotes like this. But you’re probably still wondering if generative AI is worth implementing or if it’s just the latest advancement that will quickly be superseded by some other cool new technology.
To answer this question, let’s look at four areas of the auto and equipment finance value chain where my colleagues and I see generative AI as invaluable.
Pre-originations and originations
In the recent post by Michael Abbott, Jess Murray and Keri Smith on the Accenture Banking blog, Breaking barriers: exploring how banks scale generative AI for growth, noted that early adopters are already exploring the use of generative AI for marketing. The idea is to use it to scale hyper-personalized marketing content, so that every customer communication is more relevant.
My colleague at the Specialty Finance Center of Excellence Abhishek Rastogi agrees that generative AI could be a boon to the marketing campaigns of auto and equipment financiers. He suggests using data and artificial intelligence to better target marketing campaigns by analyzing customer preferences and desires, to improve conversion rates and increase revenue.
Generative AI could also be used as a sales assistant. With human-sounding interactions combined with its analytics capabilities, it could help answer customer questions directly, or score and segment leads to help sales reps prioritize their efforts. It could also signal opportunities for cross-selling.
My colleague Bailey Carrigan recently helped one of our clients, who has an equipment leasing portfolio, identify proactive marketing and cross-selling opportunities. Using AI to help with data analysis, her team was able to recognize patterns that the client could use as a foundation to improve the marketing of team financial products, launching the next decade of growth and profitability.
Auto and equipment lenders and lessors are always looking for ways to make underwriting better, faster and cheaper. I think generative AI could play an important role in helping financiers see and analyze credit in new ways.
When Abhishek and I were discussing this, he suggested that generative AI could become an underwriter’s assistant. What would that look like? The AI would verify customer information, analyze their payment history, earnings/financial ratios, employer details, credit ratings and exposure, and consider marketing information and news reports before automatically generating a credit assessment report. It could also interact with the underwriter to perform additional checks and automatically update the credit assessment report based on those findings.
Generative AI could also be used for price analysis. Based on similar credit scores, loan-to-value, financial performance, previous loans written and closed, and risk, you could provide the optimal price for the current contract. This would free up considerable bandwidth in terms of time spent by a human subscriber or investment in a separate pricing system.
Managing variable workflows is another common challenge auto and equipment lenders and lessors face. Due to the cyclical nature of this business and the need for monthly and quarterly reports, financiers experience funding bottlenecks several times a year. Our clients often ask for our help in streamlining workflows and finding ways to fill positions with resources from other parts of the organization to help during these exceptionally busy times.
We typically work together to implement a mix of technology and cross-training strategies to fill in the gaps. But with generative AI, I expect variable workflow management to be less of an issue. AI can help the human workforce by performing repetitive manual tasks much faster, reducing workforce variability and freeing people up for higher-value work where human connection is a priority.
As more and more leases are bundled with an asset service and maintenance contract, we know that generative AI will also become an invaluable tool for predictive maintenance. AI models can predict when an asset is likely to fail and schedule maintenance accordingly. This will reduce downtime costs for the financier, as well as protect the resale value of the asset and improve the customer experience.
Generative AI will also be useful for fleet management. It can be used to monitor and manage driver behavior, track the location and status of vehicles, check insurance policy expiration dates, file claims, and track your progress. In addition, you can generate fleet and claims management reports, analyze data, and recommend opportunities for improvement and increased profitability.
end of terms
The end of the term is a lease area where I see another gap that could be filled with generative AI. It always amazes me how few, if any, landlords take advantage of the wealth of information at their fingertips to make data-driven decisions at this critical point in the financing lifecycle. In many cases, financiers don’t even use a spreadsheet to optimize their end of term, let alone sophisticated analytics tools.
Consider the case of a photocopier leased to a customer for 36 months. At the end of the term, as a lessor, how do you maximize profitability?
- Do you make an offer to the customer to keep the copier in place?
- Do you bring the photocopier and sell it?
- Do you extend the term or speed it up?
With the help of generative AI, you could answer these questions and make the optimal decisions for your business.
Another data point that could have a big impact on end-of-term decisions for auto financiers is how a vehicle’s color affects its depreciation and resale value. A survey by iSeeCars.com found that yellow, beige and orange make more money when it comes time to sell, albeit with some variation depending on the vehicle segment. Generative AI could use this information in combination with other factors, such as mileage, wear, geography, and time of year, to determine the best time to sell a rental vehicle and whether it’s worth repainting before selling.
AI could also help with end-of-lease inspections. It could analyze vehicle photos to assess excess mileage, damage and wear charges, and automatically generate vehicle inspection and damage estimation reports. You could even interact with the customer to negotiate the charges and help them through the vehicle return process.
The technology could also help lenders and landlords focus their retention efforts by predicting which customers are most likely to churn. And it could tailor offers to customers based on their individual needs and preferences for cars with more space or lower monthly payments, for example.
The end-of-term use cases for generative AI are so numerous that my prediction is that the entire end-of-term process will become automated in the coming years.
The bottom line
These are just a few examples out of hundreds that illustrate how generative AI is going to change the way auto and equipment finance companies work. It is as Paul Daugherty and his team say in his report, A new era of generative AI for everyone: “The technology behind ChatGPT will transform work and reinvent business.”
Another point from our new report that stood out to me: “We are at a stage in the adoption cycle where most organizations are starting to experiment with consuming basic ‘out of the box’ models. However, the greatest value for many will come when they customize or tune the models using their own data to address their unique needs.” For auto and equipment lenders and lessors who have decades of data to mine, customizing the generative AI foundational model and empowering it to adjust its analysis based on its own data sets will be a key competitive advantage.
Of course, the use of generative AI is not without its risks. In that same report, the authors identify the following six key risk and regulatory questions for potential users of generative AI:
My banking colleagues add three additional risks To this list: modeling hallucinations (believable-sounding answers that may not be accurate), black-box thinking (there’s no way to tell how the generative AI arrived at your answer), and skewed training data (output quality is only as good as the source material).
However, it is my opinion that generative AI will bring immeasurable advantages to auto and equipment finance organizations, especially those with the foresight to use it in combination with their vast reservoirs of underutilized data.
Early adopters are already exploring use cases in these areas of the financial value chain. Will you be one of them?
If you would like to discuss how your finance business could discover new efficiencies across the entire value chain by incorporating generative AI, please reach me.
Also, I recommend reading this Accenture report by Paul Daugherty and colleagues: A new era of generative AI for everyone.
Special thanks to Bailey Carrigansenior manager of equipment finance at Accenture, and Abhishek RastogiAccenture Auto and Equipment Finance Business and Integration Manager, for their generous contributions to this position.