The Impact Of AI On Small Business Lending

Sarvesh Baveja is Head of Risk and Data Science at Fundbox Inc.

Small businesses often struggle to access capital, especially those with annual revenues under $100,000. Traditional lending models, which rely on manual underwriting processes, often make it seem too risky to offer loans to small- to medium-sized businesses (SMBs).

Fortunately, AI is transforming this landscape, making it both more efficient and accessible for SMBs to secure the financing they need to succeed.

The Struggles Of Small Business Lending

Unlike consumer lending, which benefits from standardized credit scoring and well-established data reporting, small business lending presents a unique set of challenges, such as:

1. High underwriting costs: The manual processes involved in evaluating small business loans are expensive. Since underwriting requires human effort, the profits from smaller loans often do not justify the associated costs.

2. Business diversity: Small businesses operate across various industries, each with vastly different financial structures. And local market conditions, such as operating in New York City versus the Midwest, further complicate risk assessment.

3. Lack of reliable data: Unlike consumer lending, where most financial transactions are reported to credit bureaus, many small businesses only report financials to the IRS, and often with tax minimization strategies in mind. This leads to outdated and unreliable data.

4. Macroeconomic sensitivity: Small businesses are more vulnerable to factors such as tariffs, local regulations, and supply chain disruptions, requiring lenders to evaluate a wider range of external risks.

5. Higher rates of failure: A significant percentage of small businesses fail within their first few years. Unlike individuals who have a single credit history, entrepreneurs may start multiple businesses over time, further complicating credit assessments.

AI’s Role In Transforming Small Business Lending

In my experience, there are several ways AI can help lenders overcome these challenges so that SMBs can access the funding they need:

1. Automated data collection: AI can aggregate financial data from sources such as bank transactions, accounting software, tax filings, and even real-time sales data. This eliminates the need for manual documentation and accelerates the underwriting process.

2. Enhanced risk assessment: AI models analyze vast amounts of structured and unstructured data, including business reviews and news articles, to build a comprehensive risk profile. This helps lenders differentiate between high-risk and low-risk businesses more accurately.

3. Education and personalized recommendations: In the future, AI-powered virtual agents will be able to assist small business owners by educating them on credit management and guiding them toward suitable loan products. This will expand credit access and improve financial literacy among entrepreneurs.

Regulatory Hurdles And Limitations In AI-Driven Lending

AI has a massive amount of potential in this industry, but there are still some challenges that need to be addressed. The Equal Credit Opportunity Act (ECOA) and the Fair Credit Reporting Act (FCRA) require lenders to provide clear explanations for adverse lending decisions. However, AI-driven models often operate as “black boxes,” making it difficult to pinpoint exact reasons for credit denials.

To address this, collaboration between government regulators, private-sector lenders, and academic institutions is essential. Policymakers should work alongside AI developers to establish transparent frameworks that balance innovation with consumer protection.

Also, while AI significantly enhances lending processes, it does have limitations. AI still struggles with making complex judgment calls, such as evaluating a business owner’s reputation. AI is also only as effective as the data it processes—and since small business’s financial records are sometimes inconsistent or incomplete, AI models might struggle with reliability.

While AI can greatly assist in the lending process, this just shows that the human element is still necessary in making final decisions.

The Future Of AI In Small Business Lending

Over the next few years, AI is expected to drive some major advancements in small business lending. Expanded access to capital, improved risk prediction, and better credit education for SMBs are a few of the things to look forward to in the near future.

AI is making underwriting more efficient, improving data accessibility, and enabling lenders to evaluate risk with greater accuracy. While regulatory and technological hurdles remain, the potential for AI to improve access to capital is undeniable.

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