How AI is reshaping the credit scores of SMEs

Small and Medium Enterprises (SMEs) are the backbone of the UK economy, contributing significantly to private sector turnover and employment. Ensuring adequate funding for SMEs is crucial to sustaining this growth. Over recent years, the SME lending landscape in the UK has undergone significant changes, presenting both opportunities and challenges that are reshaping the market.

Key trends in SME lending

Emergence of challenger banks and alternative lenders: SMEs often face bureaucratic hurdles with traditional high-street banks. According to the British Business Bank, over two-thirds (68%) of total SME lending in 2025 came from either challenger and specialist banks or non-bank lenders, with challenger and specialist banks alone accounting for 60% of gross SME bank lending, up from 39% in 2012. This shift has intensified competition among lenders.

Increased access to data: Real-time access to customer data enables enhanced analysis and insights about borrowers. Lenders are now developing sophisticated, data-driven credit policies to make better lending decisions.

Automation and scalable technology: Heightened competition has driven the adoption of technology and automation across credit policies, streamlining customer journeys and providing SMEs with faster credit-as-a-service.

What is a credit score, and why is it relevant for SMEs?

A credit score is a critical tool used to assess the creditworthiness of an organization. According to the latest data from the Insolvency Service, over 12,400 companies entered insolvency between January and June 2025, with a notable concentration of cases in retail, construction, and hospitality. Although the number of insolvencies has risen in recent years, as a proportion of all registered companies, they remain less common than during the 2008-09 financial crisis. Against this backdrop, credit scores take a vital position in the overall assessment of SME borrowers, enabling banks and other lenders to make informed lending decisions.

For SMEs, a strong credit score can unlock better financing opportunities. According to British Business Bank, a business’s credit score determines:

• The amount a business can borrow.
• The applicable interest rate.
• Whether a loan application will be approved.

In addition, credit scores can influence day-to-day business dealings, such as negotiating contracts or tenders. Since business credit scores are publicly accessible, they carry implications beyond just borrowing.

Traditional credit scoring models assess factors such as payment history, debt-to-income ratios, and credit histories. However, these models often fail to capture the unique aspects of SMEs, such as customer sentiment or dynamic market behavior. This gap can lead to inaccurate decisions and potential defaults.

How AI is revolutionizing credit scoring for SMEs?

AI-driven technology is transforming credit scoring, offering solutions to the limitations of traditional models:

Faster decision-making: AI automates credit scoring by identifying and extracting key data from loan application documents, reducing manual processing times and improving underwriter decision-making.

Scalability and efficiency: With growing SME loan demand, AI allows lenders to scale efficiently, handling high application volumes without compromising speed or accuracy.

Reduction in bias: Traditional systems often emphasize historical credit data. AI can help mitigate these biases, enabling fairer lending practices and enhanced regulatory compliance. However, careful oversight is required to avoid perpetuating biases from historical data.

Dynamic scoring: AI-powered systems can adjust credit limits or terms proactively based on changing financial behaviours, ensuring real-time relevance.

Fraud detection: Predictive AI models can detect suspicious financial patterns, preventing fraud in real-time.

Time and cost savings: Automating document extraction and verification significantly reduces processing times and operational costs.

Benefits of AI-driven credit scoring for SMEs

By leveraging AI, lenders can:

• Streamline operations, reducing time-to-credit for SMEs.
• Enhance creditworthiness evaluations with nuanced, real-time data.
• Empower SMEs with access to fair and timely funding, fostering their growth.

Conclusion

The integration of AI into the lending landscape is no longer a competitive advantage, it’s a necessity. Technologies like Digilytics’ RevEL are equipping lenders with the tools to operate with unprecedented speed, precision, and efficiency. In a fluctuating economy, the ability to innovate and embrace data-driven, adaptive credit policies will shape the future of SME financing in the UK.

By investing in AI, lenders can not only scale their operations but also empower SMEs to reach their growth potential, ensuring a more resilient and dynamic economic landscape.