The $1.4T Small Business Lending Opportunity for Banks

There is a significant gap between the demand for small business credit and the willingness of banks to provide it. The small business lending market is estimated to be $1.4T, yet half of small business credit needs go unmet. 

Small businesses make up over 43% of US GDP, meaning this problem affects not just the businesses themselves, but also the many individuals who work for them and the economy as a whole. 

Due to their community relationships and higher flexibility, mid-sized banks are well-positioned to fill this gap, but they are facing stiff competition. Innovative fintechs are changing how small business lending works by streamlining origination, expanding access to capital, and reducing fraud risks. While this helps banks by enabling them to meet these needs faster, it opens the door for non-bank lenders to take up market share. 

Banks can seize the small business lending opportunity by embracing the same technology that is setting fintechs apart. By leveraging AI-powered tools, banks can grow SMB lending revenues and deliver a faster and better customer experience, all without increasing risks of fraud or default. 

The SMB Lending Market Advantage for Midsized Banks

Midsized banks with between $1B–$10B in assets are best equipped to take up market share in the underserved small business credit market. These institutions are closer to their communities, often serving a local area or specific industry, and offer a more personalized touch than the big national banks. At the same time, they are flexible enough to adopt new technology much faster than big banks, which makes them better equipped to compete with fintech lenders.

Additionally, large banks have created this SMB lending market gap by routinely denying loans that fall outside of their traditional credit box. Legacy underwriting models use rigid criteria that often fail to accurately reflect a business’s financial health, particularly for those operating on non-standard business models, which are numerous in today's world. 

Example: Evaluating Fluctuating Cash Flow

For example, an e-commerce company might have inconsistent cash flow that fluctuates with shopping seasons such as “back-to-school” and “black Friday”. The company wants a line of credit to boost inventory levels ahead of the busy seasons, but traditional lenders may flag the company as high risk due to the lower cash flow during the slower periods. 

A midsized bank that leverages advanced digital tools and AI analytics to assess creditworthiness could dive deeper and unveil a previous track record of success over past busy seasons. With more flexibility and faster tools, the mid-sized bank can serve the large segment of the modern SMB lending market whose needs are unmet due to rigid traditional credit boxes. 

Banks that use a modern SMB lending strategy that goes beyond traditional methods have an advantage. They will be able to build a more robust lending program that captures a larger share of the market without increasing their risk profile. AI-powered tools for analyzing risk and advanced cash flow analytics are providing a faster and more comprehensive view of creditworthiness. These tools open the door for more small businesses to be served by the banks that use them.  

Four challenges blocking banks from growing their SMB lending portfolios

This SMB lending opportunity can’t be seized without overcoming some challenges. We’ve identified four common obstacles that mid-sized banks must overcome to capitalize on the underserved small business market. 

1. Manual underwriting slows decisions: Manual work in the underwriting process slows efficiency, delays decision-making, and is more error-prone than digital flows. This makes it difficult for banks to scale, as they can’t process applications quickly, and customer experience suffers. 
   
   
2. Fragmented data sources: Many banks, especially older ones that experienced growth during a non-digital age, have critical information siloed between different departments, platforms, or branches. They can’t quickly get a unified customer view, which further slows down the application process and decreases accuracy in risk assessment. 
   
   
3. Outdated credit models: Legacy credit models provide a lagging snapshot of a borrower’s financial health that doesn’t tell the whole story. Rigid criteria with a narrow focus and particular risk aversions make it difficult for many healthy small businesses to qualify for loans. 
   
   
4. Compliance and regulatory hurdles: Non-standardized and siloed data create a fragmented system that makes it difficult for banks to meet the compliance and regulatory requirements for SMB lending. Lack of fast access to accurate records makes compliance and audit processes slow and painful. 

How AI-Powered Decisioning Unlocks SMB Lending Growth

To overcome the challenges listed above and to unlock the speed and scalability needed to serve small businesses, mid-sized banks can leverage AI-powered decisioning platforms. With them, they can qualify more borrowers faster, but without accepting higher-risk customers. 

Here’s how:

• Automate underwriting: With API integrations to pull data in and configurable automated workflows to process that data, lenders can instantly approve or decline loan applications. For borderline applications, the AI-powered system can flag them for manual review. 

• Manage risk: AI-driven lending insights increase a lender’s ability to manage risk because they provide a broader range of data points to analyze than traditional models. This gives lenders a better, faster, and more complete view of creditworthiness and risk profiles than traditional underwriting systems. 

• Approve more qualified borrowers: Expanding their credit box with AI-powered, real-time insights opens the door to more borrowers that typical decisioning systems disqualify. When evaluated with modern systems, these borrowers show better financial health than with the rigid, inflexible legacy underwriting models. 

• Better compliance and fraud risk practices: An expanded view of borrowers doesn’t just help with potential loan default risk; it also helps with fraud and compliance. Quickly analyzing more data points through AI-powered models helps lenders quickly flag high fraud-risk applicants. It also helps pull data for regulatory compliance and audits faster, which in turn allows lenders to process more applications.  

Decisioneer by Rapid Finance empowers mid-sized banks to capitalize on the open opportunities in the small business credit market. It uses AI-decisioning to accelerate loan decisions, automate lending workflows, approve more qualified borrowers, and improve the overall lending experience. 

Decisioneer users can create an additional revenue stream by referring denied loan applicants to the Rapid Finance Funding Network. For every loan product placed through the network, banks receive a referral fee. Additionally, they can find potential new clients who were denied loans elsewhere. 

The Roadmap for Mid-Sized Banks to Grow SMB Lending

To build a modern SMB lending program that enables them to capitalize on this underserved market, banks can follow a strategic four-step roadmap.

1. Assess portfolio and decline rates. First, assess your current lending portfolio and identify why applications are being rejected. Identifying the types of customers you serve and those you don't will help you pinpoint gaps in your portfolio and uncover the most significant opportunities for growth. You will likely find that altering your credit box to include an expanded real-time customer view will qualify many customers that you previously rejected.
   
   
2. Modernize decisioning with AI. Source an AI-powered decisioning platform that automates manual underwriting, streamlines lending workflows, and helps you make faster, more accurate decisions. AI-decisioning enables you to serve more qualified borrowers outside your traditional credit box and scale your lending program by automating manual work and allowing underwriters to get a data-backed second opinion.
   
   
3. Use data to refine and expand reach. The data and insights from your AI-decisioning platform can reveal underserved segments and allow you to tweak your product offering. It creates a continuous feedback loop to better align your loan product positioning with market needs, qualified customers, and sustainable growth.
   
   
4. Leverage partner networks. Referral networks such as the Funding Network enable banks to monetize applications that are not a fit. This generates additional revenue through referral fees and preserves relationships with denied customers by offering them alternative financing solutions. 

An AI-Decisioning Platform Built for Lenders, by Lenders

The potential for mid-sized banks to serve SMB businesses is immense, as traditional underwriting has left much of the market behind. Leveraging AI-decisioning tools enables banks to expand their lending reach and provides small businesses with greater access to capital for growth. 

Choosing the best underwriting and decisioning tools matters. While many providers might offer “AI-powered” tools, those with real-world small business lending experience understand what works in the SMB lending world and what doesn’t. 

With over 20 years in the small business lending industry, Rapid Finance is the fourth largest small business lender in the US, with over $1B funded. Our AI-decisioning platform, Decisioneer, and The Funding Network fuse those decades of lending expertise with modern, AI-powered tools. The result is a system that allows lenders to expand their portfolios into new markets without additional risk. 

Interested in expanding into the small business market with modern, AI-powered decisioning? 

Contact Rapid Finance to explore Decisioneer andc the Funding Network.