Nuance of Consumption of Data for SME Lenders in the UK

Small and Medium Enterprises (SMEs) form the backbone of the UK economy, contributing significantly to employment and economic growth. However, SME lending has historically been a challenging sector for financial institutions due to the diverse risk profiles and limited financial history of many small businesses. With the evolution of data-driven decision-making, SME lenders are now leveraging various forms of data to streamline underwriting processes, assess creditworthiness, and enhance their risk management frameworks.

This blog explores how SME lenders in the UK consume data, the different types of data they rely on, the formats in which they consume it, and how these insights empower underwriters to make informed lending decisions.

The Shift to Data-Driven Lending

Traditionally, SME lending decisions were largely based on manual assessments of financial statements, collateral, and business owner experience. While these factors remain important, the rise of digital finance has introduced new data sources that offer deeper insights into an SME’s financial health and repayment ability.

According to reports from leading UK SME lenders, there has been a significant shift towards integrating digital data sources into lending decision processes. For example, Paragon Bank also has auto-decisioning system which uses machine learning AI to support their specialist underwriters.

Forms of Data Consumed by SME Lenders

SME lenders rely on multiple data sources to enhance their underwriting models. Below are the key types of data they consume:

1. Traditional and Credit Bureau Data

Traditional data remain a core component of SME lending decisions. Traditional data includes balance sheets, bank statements, management accounts, and cash flow statements to determine an SME’s financial stability. In addition, credit bureau reports from agencies like Experian, Equifax, and TransUnion provide insights into an SME’s credit history, past repayment behaviour, and existing financial obligations. For example, , Metro Bank uses both Financial Statements and Credit Bureau data to tailor their offerings and also assess SME creditworthiness.

2. Data from Open Banking

With Open Banking regulations enabling businesses to share their banking data securely, SME lenders can now access real-time transaction histories, income patterns, and cash flow trends. This data helps lenders assess a business’s financial health beyond static financial statements, offering a dynamic view of income and expenditure trends. . Iwoca, for instance, has leveraged Open Banking to streamline its loan application process.

3. Alternative Data Sources

SME lenders are increasingly incorporating alternative data sources to improve their underwriting models. Some examples include:

• • Government records (Companies House): Companies House provides key business information that helps lenders assess an SME’s financial health and stability. Data from Companies house can reveal profitability, governance details and liabilities. Frequent director changes or dissolution warnings can signal instability, making this data a useful supplement to traditional credit assessments. OakNorth uses data from Companies House to monitor corporate governance and early signs of distress
• • Supply chain and trade data: Information on supplier payments, invoice records, and procurement transactions can indicate the financial strength of an SME. Iwoca integrates trade payment data through IwocaPay and offers trade credit.
• • Digital and Payment Data: With SMEs increasingly using digital payment platforms, lenders can analyse transaction data from payment gateways, point-of-sale (POS) systems, and e-commerce platforms. These insights reveal revenue streams, customer demographics, and seasonality in business performance, all of which contribute to better risk assessment. Liberis has partnered with Epos to utilise latter’s POS technology to provide funding solutions to SME’s.

4. Income and employment verification

Employment verification through digital payslips and information from payroll records like regular wage payment is an indicator of income and business stability. For example, work report by many credit bureaus provides digital employment and income verification service for lenders.

How SME Lenders Consume Data

The various data types mentioned above are consumed by SME lenders through different methods. The three primary ways lenders access and utilize this data are:

1. API-Based Data Consumption

APIs (Application Programming Interfaces) allow lenders to directly access and integrate data from various sources into their underwriting systems. Open Banking APIs, credit bureau APIs, and financial aggregator APIs enable seamless data exchange in real time, providing lenders with up-to-date financial insights.

Through API-based consumption, lenders can:

• • Automate data collection and reduce manual errors.
• • Continuously monitor borrower financial health.
• • Integrate multiple data sources into a single underwriting model.
• • Improve response times in loan decision-making processes.

Growth Lending, for example, has automated its payment processes via API integration. This enhancement will provide the SMEs with rapid access to funds.

2. UI-Based Data Consumption

While API integrations provide automation, some lenders still rely on user interfaces (UIs) for data retrieval and analysis. Many financial institutions use dashboards, portals, and online platforms to access and interpret data manually. This method is commonly used when:

• • Lenders need to manually review documents and reports.
• • Underwriters require additional context beyond raw data.
• • Human judgment is required to interpret complex financial statements.

Many SME lenders in UK today are using UI-based systems for credit underwriting, allowing human oversight while leveraging AI-powered insights.

3. Categorized and Segmented Data based consumption

One of the key aspects of data consumption by SME lenders is the ability to categorize and segment data for better underwriting decisions. Instead of reviewing raw financial statements or transaction logs, underwriters receive pre-categorized data that helps them quickly assess an SME’s financial position. This categorized data includes:

• • Revenue breakdown: Differentiating between recurring revenue, seasonal income, and one-time transactions.
• • Expense categorization: Identifying fixed costs, variable expenses, and discretionary spending.
• • Cash flow segmentation: Highlighting trends in inflows and outflows to detect inconsistencies.
• • Debt and liability structuring: Organizing outstanding obligations to assess repayment capacity.

By structuring data in a categorized format, underwriters can make faster and more informed lending decisions while reducing the chances of overlooking key financial risks. . For example, Equifax helps categorize information for lenders to ease the assessment from a lending standpoint.

Benefits of Data-Driven SME Lending

Leveraging diverse data sources has brought several benefits to SME lenders, including:

• • Enhanced Risk Assessment: Integrating real-time financial data, credit bureau information, and alternative data sources allows for a more comprehensive evaluation of an SME’s financial health. This results in more accurate risk assessments and reduces the likelihood of defaults.
• • Faster Decision-Making: Automated data collection through API integrations and streamlined UI-based review processes save underwriters time and enables quicker loan approvals. SMEs benefit from reduced waiting times, which is crucial for businesses needing immediate capital.
• • Improved Customer Experience: Data-driven processes facilitate personalized loan offers tailored to an SME’s unique financial profile. This not only enhances customer satisfaction but also fosters long-term relationships. Lenders leveraging technologies for document processing can reduce their underwriting time by 30% and improve lending journey for the customer.
• • Cost Efficiency: Automation and advanced data analytics reduce the need for manual processing, lowering operational costs and enabling lenders to offer competitive interest rates and fees.
• • Competitive Edge: Adopting data-driven approach through advanced data consumption methods provides SME lenders with a competitive advantage. This allows them to respond to SME funding requests faster, improving customer satisfaction and increasing market share.

Conclusion

The landscape of SME lending in the UK is undergoing a transformation, driven by the increasing consumption of diverse data sources. Insights from SME Lenders show a strong shift towards API-driven data integration, AI-enhanced risk models, and Open Banking adoption. The Centre for Finance, Innovation and Technology (CFIT) and Open Bankinghighlighted in their report that enhancing accessibility of data by moving to more up-to-date digital format and verification using alternative sources could lead to greater levels of automation making it easier for SMEs to go through loan application, and lead to better decision by the lenders.

As SME lenders continue to embrace data-driven decision-making, the future holds immense potential for more inclusive and efficient lending models, ultimately fostering the growth and success of UK SMEs.

Author: Akshat Dev, Partnership Development Strategy at Digilytics AI

Follow us on : Twitter | LinkedIn