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Four Use Cases for AI in Asset Finance

January 05, 2024, 05:00 AM

As in every other technology sector, asset finance professionals are wondering how AI will enhance their businesses. Current industry conversations don’t touch on specific requests so much as on a general — and correct — intuition that AI will have a big impact on the industry, and early adopters will gain an advantage.

As someone who’s been overseeing the development of asset finance technology for more than two decades, I see four likely use cases for AI in our industry: optimizing leases, mitigating risk, saving time on financial irregularities, and better prioritizing tasks.

While none of these use cases is ready for primetime, here’s what we can expect as AI matures.

Lease Optimization

So much of the risk in asset finance lies in the value left over at the end of leases and in minimizing the assets that end up back on lessors’ hands. AI may be able to help with this by identifying the propensity of a given lease — based on the equipment, the lease term, and the use to which it is put — to be extended and the likely value of the equipment at the end of the initial term. AI can sort through an amount of data that the human mind cannot interpret, recognizing patterns that enable more profitable decisions.

For example, let’s say a model tells a finance company that a piece of agricultural equipment is likely to retain a great deal of value at the end of a 24-month lease based on the type of crops it’ll be used to harvest and the history of that machine’s usage. The company can then optimize its lease for that scenario by offering the lessee a new machine at the end of 24 months. Whereas, if the equipment were likely to have minimal value at the end of the lease, the lessor would anticipate that drop in residual value and plan accordingly, possibly by offering the lessee a discounted rate to lease it for an additional 12 months.

Credit Risk Mitigation

Another liability in asset finance that AI may be able to mitigate is credit risk. Any time a company takes on a lease, it is exposing itself to the risk that the company leasing the asset may not pay. Today, collecting and analyzing data is difficult, so companies rely on hard-coded models. These systems analyze a predetermined and relatively limited set of variables to assess risk and allow them to make decisions.

AI will broaden the variables a company can consider when creating a lease and help them determine which variables are most relevant to a given deal. Not only will this lead to better decisions, but it will also open up risk-based pricing, rewarding customers who are likely to pay and shielding lessors from undue liability.

Time Savings

Asset finance involves plenty of financial irregularities that cost a great deal of time to investigate and remedy. For example, let’s say a captive loans thousands of laptops to a government entity. The government sends one payment, which is not equal to the sum of the laptop schedules. Currently, the captive needs to employ staff to research and determine the application of the payment.

In the future, AI will help companies overcome these challenges. Instead of hundreds of human hours dedicated to identifying and rectifying the source of the unapplied cash, AI will be able to recognize who’s short and deduce the likely reason for the problem (or at least indicate whether there’s a pattern). Little improvements like this will add up to thousands of saved hours per year in enterprises.

Similarly, AI will help with fraud prevention, saving time and money. Superior pattern recognition will identify fraudulent customers, vendors, and partners before they enact harm, sparing companies money, time, and reputational liability.

Workflow Prioritization

Asset finance companies often have hundreds of employees using software to structure their days. One person might have dozens of tasks in a given day. Currently, that person might determine the most urgent task based on intuition.

AI will replace this painstaking and inefficient process of human assumptions with data-driven analysis. So, asset finance employees will be able to work on the most urgent task first and work their way down the list in order of true priority.

Collections offer another key example of this. Let’s say a collections officer has 100 accounts on their call-list. AI will be able to inform them of whom to call first based on a combination of who is most likely to pay and who will deliver the most meaningful financial outcome. This means time saved for the collection officer and more money recovered for the company.

AI’s Timeline in Asset Finance

AI isn’t quite ready for the big show yet. One obstacle is data organization. Asset finance companies need clean data to generate clean insights — otherwise, the “garbage in, garbage out” principle applies.

Another impediment to adoption is knowing the questions that, when answered with the help of AI, will grow portfolios, reduce operating costs, and create a better customer experience. AI may have a role to play in answering those very questions better and faster than humans.

The newness of AI also presents organizational barriers to adoption. Generally, tech companies make investment decisions based on customer needs. But if customers only have a vague sense that they need AI without clamoring for any one solution in particular, it is hard for AI companies to determine what they should build.

Nevertheless, AI is coming. The technology is developing rapidly, its potential impact is clear, and those who explore and adopt it proactively will have an edge over competitors mired in the old way of doing business.

Jeff Lezinski
EVP, Product Management | Odessa
As Odessa’s EVP, Product Management, Jeff Lezinski is responsible for the overall strategy and vision of the Odessa Platform. Since joining the organization in 2004, Lezinski has performed in a variety of roles across Odessa, including delivery consulting, presales, and the Customer Advisory Board. He currently serves on the ELFA Finance & Accounting Committee and acts as a liaison to industry and regulatory bodies.
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