In a word? Yes.
In 2023, Skyquest estimated that the global commercial lending market was worth approximately $9.7 trillion. They project the market to expand to around $23.06 trillion by 2032, reflecting a compound annual growth rate (CAGR) of 10.1% from 2025 to 2033.
Though we are not commercial lenders, we develop technological solutions for the commercial lending industry. Here’s our take on where AI belongs and the role it can play in facilitating commercial lending’s industrial growth.
Here are three key reasons why commercial lending is growing. The following are non-exclusive, as several factors contribute to commercial lending’s ongoing growth.
Embedded lending (EL) represents a crossover between commercial lending and embedded finance services. It enables the provision of credit or loans directly through non-financial platforms, such as e-commerce sites, retail apps or travel services. This model gives customers access to financing at the point of need without relying on conventional financial institutions. Sources project the embedded lending sector to grow by three times – every year, supporting the overall growth of the global lending market.
Of course, firms might not possess the technological capabilities to facilitate embedded lending (on their own). As an in-depth EY article mentions, AI technology facilitates EL particularly well. Consequently, firms deploying embedded lending may, instead, turn to fintechs.
Skyquest claims, ‘The rise of fintech disruptors has redefined lending norms, offering digital convenience and customized solutions. Collaboration between traditional institutions and fintech innovators has emerged as a strategic trend, bridging the gap between credibility and technology.’
We thoroughly agree that collaborations between fintech and traditional institutions are a significant trend founded on the credibility of lenders and the innovative technological offerings of fintechs. We’ve addressed the significance of these collaborations (in a previous article), ‘Commercial lenders have been forced to rethink their business models in recent years. In turn, the innovative solutions developed by fintechs (often leveraging new technologies like AI) rely on adoption by larger companies, which release the funds necessary for further development.’
In other words, collaboration between lenders and fintechs means lenders can drive a competitive advantage through technology, reaching more customers and offering more flexible and convenient solutions.
We can attest that building a competitive technological mechanism for lenders, such as automated credit decisioning, underwriting and more, requires extensive resources and expertise. Practically, that means a team of developers (e.g. machine learning developers) spending hundreds of hours experimenting with solutions. However, not every lender will have the in-house resources to make that happen.
Alternative funding sources, such as peer-to-peer lending, crowdfunding, marketplace lending platforms, etc., allow those traditionally overlooked by lending models (e.g. individuals with a low credit score) to access credit.
AI can make alternative funding more ‘intelligent’. AI excels at analysis – even with the small details – meaning it can examine alternative credit data, such as rental payments, service subscriptions and more, reducing financial exclusion whilst fulfilling ESG goals. It can also support industry growth by helping different lenders access new markets.
As AI developers, we might be biased in saying that AI is a key driver in the commercial lending industry’s growth.
Though not a fully refined technology, AI offers a solution for all lenders when deployed strategically alongside human expertise. Let’s explore how AI can grow companies (in turn), scaling the commercial lending industry.
Let’s examine two somewhat disparate ways that AI facilitates growth in the commercial lending industry.
On its own, the word ‘productivity’ doesn’t mean much. However, a survey by Airbase shows that 38% of surveyed respondents spent more than 25% of their time completing manual tasks (with the amount of time increasing in larger finance teams). By replacing these manual tasks (e.g. data entry, reconciliation, transferring data, etc.) with a low-cost, automated alternative, these businesses could boost their staff productivity and grow profits.
As for a practical example? We worked with DF Capital to reduce their time to process invoices (for asset financing) by 95%, meaning they could turn to more client-facing activities. By eliminating unscalable manual processes, forward-thinking businesses can focus on what’s profitable (which manually extracting data from a bank statement certainly isn’t.).
By identifying riskier customers faster (i.e. applicants with a higher risk of default), commercial lenders can provide more convenient services for all customers while opening up access to credit for underrepresented groups. Additionally, using AI’s sophisticated analytical abilities means they can personalise loan pricing.
The University of Bath estimates that doing so can boost lending profits by 34% (Note: This experiment involved retail channels like car dealerships, which may mean it has limited applicability).
Therefore, businesses that can invest in improving their data quality and decision-making algorithms may likely benefit from more profitable and fairer decision-making.
Many describe commercial lending as ‘dynamic’ – an apposite term for an industry successfully facilitating technological disruption. We’ve addressed how lenders and fintechs can work well together, and such collaborations are now driving exponential growth in the lending industry. When safely and tactically deployed, AI takes consumers on this journey, delivering fairer and more convenient lending services.
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