What Generative AI has in Store for Equipment Finance?

This article examines the potential implications of the future of generative AI for equipment finance, exploring how it could revolutionize how businesses finance their operations.

Fremont, CA: Though AI is still relatively new in the equipment finance industry, it has been around for a while in other fields like managed services. As it develops, the greatest potential for AI is found in generative AI applications, where deep learning models generate unique, rich content using enterprise data.

There are currently no detailed models explaining how generative AI will do this. To envision this future, a bold rethinking of how individuals performing routine equipment finance duties may utilize AI-driven insights derived from intuitive data analysis to make high-level decisions that benefit them, their employers, and their clients will be necessary. 

A digital ecosystem that facilitates a co-piloting partnership between the people who will use the insightful analyses to promote more fruitful customer relationships and the generative AI engines that interpret the data is necessary to turn this difficult-to-envision goal into a hard-and-fast reality.

Terms Defined, Distinctions Clarified

First, by describing the many components of AI and their differences, we can better understand what AI is and what it can accomplish. Following that, while discussing a use case for collections, we may think more broadly about what a disruptive business program based on generative AI might include.

Predictive analytics, machine learning (ML), and predictive AI are related but separate concepts within AI.

Predictive analytics is an older, more procedural method, usually expert-driven. It predicts future events based on past patterns, data, and statistics, and it has evolved to occasionally include machine learning.

ML is machine learning (ML) using data and historical events to identify patterns and forecast future events.

With predictive AI, things can get murky because some fervent marketers mistake predictive analytics for predictive AI. ML algorithms built from past data are used by predictive AI to continuously spot patterns, spot emerging trends, and generate predictions.

After defining terminology, it's critical to highlight the primary distinction between predictive analytics and artificial intelligence: While AI is capable of self-learning, predictive analytics—which occasionally uses machine learning—needs human assistance to analyze data, spot trends, and investigate assumptions.

Optimizing Sales

An application with generative AI has the potential to supplement sales personnel. It might compile sales leads into interest-based groups that could respond well to certain product and service marketing campaigns and then score those leads to increase close rates. This could make linking lenders with potential customers looking to finance equipment easier based on their unique demands.

There are other sales angles to consider. Pay-per-use products may benefit from artificial intelligence due to their inherent predictive usage. AI predictive analytics has the potential to power new pay-per-use product applications for a wider range of equipment types and, more precisely, target those opportunities based on geography and industry type.

Simplifying Document Generation and Processing

Imagine when sales personnel can focus more intently on prospects' business and equipment finance needs while AI co-pilots their conversations. Together with a risk-assessment AI co-pilot, this sales AI co-pilot may identify the best combinations of deal qualities and values to generate several proposals for the client while gathering data to be entered into an electronic ledger. Without requiring human involvement, the generative AI documentation co-pilot analyzes the data and creates the loan and lease agreements that go along with each proposal, promptly meeting all legal, credit, and risk standards.

AI may automatically examine third-party-originated material purchased and sold throughout the capital markets syndication process and compare it to the characteristics and data values the seller presents to identify any errors.

Expediting Credit Underwriting Decisions

The ability of AI to analyze data is crucial for credit underwriting. Automated retraining of credit scoring models and faster underwriting judgments that are more likely to be correct and prevent defaults are made possible by the vast quantity of data that AI technologies can process in real-time, including an applicant's income, job, and credit history. This removes prejudice from equipment finance and leasing systems, making them more dependable than conventional credit underwriting systems. Underwriters will be able to concentrate on obtaining and analyzing more information from borrowers that AI cannot access by using AI to automate routine analysis and offer deeper insights into possible unforeseen risks.