Source: American Banker - 04/14/2025

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Here's a scenario every commercial lender knows: you want to offer a business credit product – say an SBA loan.

Suddenly, you're building a team – hiring a business development officer, sales support analyst, credit analyst, underwriter, closer, legal specialist... and the list keeps growing. Then comes the added complexity of varied uses of proceeds, industries, and commercial entity structures – and now you're hunting for a specialist who knows how to structure a real estate–backed business acquisition loan for each state in your footprint.

How can you scale business lending when customers expect a consumer-like experience, but the economics don't match mid-market deals – and there's no universal risk marker like a FICO score to rely on?

This is where the AI revolution stands apart from past "silver bullets" – from the wave of automation hype to the now-faded big data and machine learning craze of 2018. What sets single-platform, multi-agent, agentic AI apart is its ability to seamlessly infer, act, communicate, and apply subject matter expertise across multiple roles.

In simple terms, imagine a system that can take on every role involved in the end-to-end lifecycle of a credit application. Every use case, jurisdiction, and borrowing structure is handled within a single platform and without requiring the kind of budget you're used to.

To dig into this further – first, what are multi-agent, agentic AI systems in the context of lending? We are no strangers to AI being deployed in the customer service space, where it acts as a glorified search-and-assist agent – until the customer wants to speak to a live person to take an action. Agentic AI goes beyond this simple task, functioning like a virtual employee capable of decision-making and autonomous action.

In the commercial lending context, this means more than just having AI summarize and present information like a credit memo with highlighted strengths and weaknesses of the applicant. It means having an AI system that can also run automated checks and act based on those outcomes, such as requesting additional documentation from the applicant or approving an application and advancing it to the next stage while notifying all relevant stakeholders.

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It is capable of assuming different roles, depending on the context of each application it reviews. For example, the AI can tap into specialized agents depending on the task – whether it's guiding a borrower through document submission, performing a compliance check, or preparing legal closing documents.

• As a customer-facing sales agent, it helps applicants understand the process and submit the correct documentation.
• As a quality assurance support agent, it ensures compliance by keeping up with evolving standard operating procedures and autonomously returning noncompliant files to sales for correction.
• As a legal agent, it identifies when a loan is fully underwritten, prepares the loan package, and delivers it directly to the applicant.

Clients using multi-agent, agentic AI systems can immediately see improvements in efficiency and reductions in both operational and credit risk exposure. For example, users of LenderAI have reported over a 50% reduction in SMB lending operating expenses, along with up to a 17% decrease in SBA guarantee denials – even with limited AI capabilities.

The full application of AI across the entire business lending lifecycle has the potential to fundamentally reshape how financial institutions operate. Early signs of this shift are already visible, as banks begin taking thoughtful steps toward AI integration, all while appropriately prioritizing privacy, security, and regulatory compliance.

The final piece of the efficiency puzzle is the concept of a "single platform" – an AI-enabled architecture with native capability modules like telephone, email, document generation, e-signature, and more. Consider the countless hours your technology teams spend integrating separate tools, trying to move data between core systems and third-party modules – only to find the promised efficiencies compromised by integration gaps. That's the risk of multi-platform, point AI solutions: they may excel at one task, but only that one. The inefficiencies they create are subtle yet costly. They are reflected in ballooning tech budgets and persistent manual workarounds caused by systems that don't communicate smoothly.

One thing is clear: the years ahead will bring continued economic and regulatory uncertainty and pressure banks and financial institutions to respond with greater speed and flexibility. Without implementing the right AI platforms, institutions risk either missing key opportunities or absorbing costs that are no longer sustainable. The technology is ready, and a clearer regulatory framework is quickly taking shape. Change can be daunting, but it's also inevitable. Now is the time to take thoughtful, incremental steps – so when the moment comes, you're ready to move at full speed.

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