Asset-Based Lending Sector Needs to Embrace Data and AI
Adjusting to more complex datasets should be a top priority in asset-based lending now that AI is set to become a bigger factor in borrower health, advises Tiger Group Executive Managing Director Bradley W. Snyder.
In an April 5 Industry Insights column for ABL Advisor (“What Asset-Based Lending Can Learn from AI in the Grocery Sector”), Snyder points to OpenAI’s bombshell release of ChatGPT and cites the more prominent role of AI-based tools in the grocery business.
“Bigger companies like Kroger and Walmart have long spent billions of dollars on data and tech,” he explains. “As AI and similar solutions have become more accessible, opportunities have started to emerge for smaller borrowers to get in on the act.”
Indeed, Snyder writes, independent grocery wholesalers and retailers are already taking advantage of a slew of digital solutions offered by Instacart—including a smart cart system created by artificial intelligence startup Caper AI.
The digitally-connected, AI-driven carts “are a far cry from what shoppers have been pushing around the supermarket ever since Sylvan Goldman of Oklahoma patented the grocery cart in 1940,” Snyder opines, likening the carts to “all-in-one tech platforms and computer servers on wheels.”
The executive describes driving out to a Cincinnati suburb on a recent business trip to visit a Kroger store that was piloting Caper AI’s tech. The latest iterations of the camera-equipped carts are able to auto-detect items by drawing from a deep-learning database of more than 20 million product images. “Shoppers can simply toss products into some versions of these carts—no barcode-scanning required—and they are instantly added to the bill,” Snyder notes.
While the system does make shopping easier, he adds, it also allows grocers to tap into the manifold benefits of deep learning. Caper AI’s technology analyzes shopping patterns and responds by recommending various products and deals—including targeted ads paid for by any of 5,500 CPG companies. “In addition to collecting shopper-purchase data, the Caper AI system tracks where people go in the store and collects other data that grocers can later use to manage inventory and optimize store layouts,” Snyder writes.
As he sees it, asset-based lenders and appraisal and disposition firms can count on retailers, grocers and other borrowers to get more sophisticated about collecting and analyzing customer and product data in the years ahead. An explosion of third-party vendors will give them more innovative, AI-based ways to collect and analyze massive amounts of information.
That means assessments of borrower health and asset value will increasingly need to account for the role of AI and tech at the company. “Is the borrower woefully behind on these fronts or pushing ahead?” Snyder asks. “To what extent have merchants used these tools to unlock the full potential of their inventory or customer base? What is the potential value of the data the company has collected and the systems it employs? Is this information actionable?”
In response, appraisal and disposition firms that already employ in-house and consultant experts on FF&E, retail inventories and all manner of industrial equipment will need to bring on board expertise more typical of a data scientist than a merchant, he writes.
Tiger, for one, has ramped up its data-collection and analysis capabilities by hiring quants and developing new systems that leverage deep-learning methodologies and terabytes of increasingly available borrower data. Tiger is now working with a raft of borrowers to dramatically improve their performance in areas such as inventory rebalancing, customer segmentation, advertising and marketing analysis, and more.
Concludes Snyder: “As the ABL sector evolves, merchants will still make plenty of valid assessments based on their gut feelings and years of experience. In other cases, though, the Aha! moment will come from an AI that has sifted through an ocean of data in a matter of seconds.”