How Lenders Can Scale Operations

UK SME Lending Surge: How Lenders Can Scale Operations

Business lending by high street banks has increased by 14% year-on-year to £4.6 billion in Q1 2025, marking the sixth consecutive quarterly rise and the highest amount since the second quarter of 2022 [1]. This sustained growth pattern indicates a fundamental shift in SME financing needs rather than a temporary spike. For financial institutions, this presents both an opportunity and a significant operational challenge: how to scale lending operations efficiently while maintaining robust risk management in an environment of tightening credit conditions.

The surge is particularly pronounced in lending to the smallest businesses with annual turnover of up to £2 million, which has increased by nearly 30% year-on-year [2]. This disproportionate growth in smaller loans creates unique operational pressures, as these facilities typically have higher processing costs relative to loan size. Meanwhile, certain sectors are driving this growth more than others, with agriculture, manufacturing, wholesale and retail, and health sectors leading the charge [1].

As financial institutions navigate this evolving landscape, they must implement strategic operational changes to capitalise on the growth opportunity while managing risk effectively. This article explores key strategies for scaling SME lending operations in the current market environment.

Modernising Loan Origination Systems to Handle Increased Volume

The 14% increase in SME lending volumes is creating significant operational bottlenecks for financial institutions still relying on outdated loan origination systems. According to UK Finance data, this sustained growth across six consecutive quarters has put unprecedented pressure on processing capabilities, with many lenders struggling to maintain service levels while managing increased application volumes [1]. These outdated systems were not designed to handle the current volume and complexity of loan applications, particularly from smaller businesses.

Modern loan origination systems offer substantial benefits in processing efficiency and risk assessment accuracy. For instance, in the bridging finance sector, which has seen loan book values exceed £10.3 billion, lenders with advanced systems have demonstrated remarkable processing capabilities. One example is West One Loans, which completed a complex £11.1 million bridging loan in just six days [3]. This level of efficiency is becoming a competitive necessity rather than a luxury as the market continues to grow.

In the UK bridging finance sector specifically, the implementation of modern loan origination systems has led to a 28.6% quarterly increase in bridging completions, with total completions reaching £2.30 billion in Q4 2024 [3]. This demonstrates how technological modernization directly correlates with increased lending capacity and market growth.

The impact of modernizing loan origination systems extends beyond just processing speed. Operational efficiency has emerged as a key driver of digital transformation in capital markets, with nearly 90% of respondents in a recent UK and European survey believing digital transformation will enhance operational efficiency [21]. Data is the top priority for improving data capture and transformation, with 57% of firms planning to invest in workflow automation within the next year.

What features should financial institutions prioritise when modernising their loan origination systems? Automation of document processing, integrated credit scoring, and configurable workflows that can adapt to different loan types and sectors are essential components. These capabilities not only accelerate processing times but also enhance risk assessment accuracy, a critical consideration as credit conditions tighten.

Integrating AI-Driven Credit Risk Assessment for Faster Decision-Making

With credit conditions tighten, financial institutions must balance increased loan demand with prudent risk management. Artificial intelligence and machine learning technologies are transforming credit risk assessment, enabling lenders to make faster, more accurate decisions while maintaining robust risk controls.

Financial institutions are increasingly adopting AI to enhance credit risk management. AI applications, including machine learning and large language models, are improving credit assessments, delivering personalised client advisory services, and automating language-intensive processes [4]. These technologies enable lenders to process higher volumes of applications while simultaneously applying more sophisticated risk analysis.

The effectiveness of AI in credit risk assessment has been demonstrated through measurable improvements. AI-driven credit scoring models have lowered default rates by 40% for some lenders [22]. Additionally, AI-powered risk management tools can enhance decision-making accuracy by up to 30% in financial institutions [23]. These statistics highlight the tangible benefits that UK lenders can achieve through AI implementation.

The rapid adoption of AI tools in the financial sector is accelerating, though not without risks. A recent survey of UK financial institutions found that while nine in ten firms use AI tools, only 18% have implemented internal guidelines and policies for the technology [24]. This highlights both the widespread adoption and the need for better governance frameworks as these technologies become central to lending operations.

UK firms are already benefiting from integrated AI tools, with 78% of C-suite leaders across industries already integrating these tools into their workflows [25]. The study also found that 81% believe these tools will fundamentally reshape their organisations' structure. As AI adoption increases, 90% of executives are now having conversations about its implementation, indicating the strategic importance placed on these technologies for operational efficiency.

For SME banks and fintech lenders specifically, AI implementation has shown remarkable results. IBM has set a goal to save $2 billion in 24 months by applying its own AI technologies to internal operations and has already achieved $3.5 billion in savings [26]. The company's approach involved a structured focus on cost efficiency, workforce efficiency, and operational efficiency—a model that financial institutions can adapt to their loan origination processes.

In the UK context, financial institutions implementing AI for credit risk assessment face specific regulatory challenges. The Financial Conduct Authority (FCA) has established guidelines requiring explainability and transparency in AI-driven lending decisions. UK lenders must ensure their AI systems comply with these regulatory frameworks while still delivering operational efficiencies.

The UK and US are advancing towards comprehensive AI regulation to ensure transparency and fairness in financial services [18]. This regulatory evolution highlights the need for explainable AI in financial services, particularly in credit decision-making processes. As one expert notes, "Without structured governance, AI can become more of a liability than an asset, as it continues to shape the industry, manufacturers must balance innovation with risk management to mitigate regulatory, security and accuracy risks in AI-powered tools" [18].

UK financial institutions are addressing these regulatory challenges through various approaches. Some are implementing hybrid models that combine AI-driven insights with human oversight to ensure compliance with FCA requirements for explainable decisions. Others are developing comprehensive model governance frameworks that include regular validation and testing protocols. These compliance strategies are essential for UK lenders seeking to scale their AI-driven credit assessment capabilities while navigating the complex regulatory landscape.

Sector-Specific Lending Strategies Amid Varying Growth Patterns

The 14% overall increase in SME lending masks significant variations across industry sectors. The uneven distribution of lending growth across sectors suggests that a one-size-fits-all approach to scaling operations would be inefficient. Financial institutions need to develop sector-specific expertise and operational workflows that account for the unique characteristics and risk profiles of high-growth sectors.

Notable increases in new lending were observed in agriculture, real estate, health, and recreation sectors, while manufacturing, transport, and storage experienced declines [27]. Each of these sectors has distinct operational characteristics, cash flow patterns, and risk factors that require tailored assessment approaches. By developing sector-specific lending strategies, financial institutions can allocate resources more efficiently and implement appropriate risk controls for each sector.

The agriculture sector, for instance, has shown particularly strong growth in lending demand, requiring specialized understanding of seasonal cash flows, equipment financing needs, and commodity price risks [1]. Manufacturing businesses, meanwhile, have seen significant output increases since March, according to a report by Lloyds. The latest UK sector tracker revealed that food and beverage manufacturing saw a growth of 51.1, with both sectors reporting a rise in demand measured by new orders [28].

In Oxfordshire, distribution centres have evolved into technology-driven logistics ecosystems, with the rise of digital transformation across the sector [29]. Modern technologies now support stock visibility, staff scheduling, and real-time monitoring of warehouse operations. These tools have been crucial for improving warehouse efficiency. Companies have moved away from paper-based processes in favour of integrated digital tools that help minimise picking errors, improve inventory tracking, and streamline fulfilment processes.

The shift towards alternative lenders is also significant, with 60% of SME lending now coming from outside of the main high street banks [27]. This indicates a fundamental change in the lending landscape, creating opportunities for specialized lenders who can develop sector-specific expertise and operational capabilities.

How can lenders effectively implement sector-specific strategies? The key lies in combining industry expertise with data-driven insights. By analysing sector-specific performance data and risk indicators, lenders can develop targeted underwriting criteria and monitoring protocols. This approach enables more accurate risk assessment and more efficient resource allocation, optimising loan origination processes in response to tightening credit conditions.

Scaling Operations for Small Business Lending Acceleration

With lending to the smallest businesses increasing by nearly 30% year-on-year [2], financial institutions face unique operational challenges in processing higher volumes of smaller loans. These smaller facilities typically have lower margins but higher processing costs relative to loan size, requiring highly efficient, automated processes to maintain profitability.

To efficiently scale small business lending operations, financial institutions must implement streamlined processes specifically designed for smaller loan amounts. This includes simplified application procedures, automated credit scoring models calibrated for small business risk profiles, and efficient documentation requirements that balance risk management with processing speed.

Digital channels play a crucial role in scaling small business lending operations. By offering online application portals and mobile interfaces, lenders can reduce the administrative burden on both applicants and internal staff. These digital tools can capture essential information, verify identity, and initiate preliminary credit assessments, significantly reducing the manual processing required for each application.

The number of new loans and overdrafts approved for small businesses has increased by 37% and 8% respectively compared to Q1 2024 [2], highlighting the growing volume pressure on operational systems. This surge in application volume requires lenders to implement highly scalable processes that can flex with demand without proportional increases in operational costs.

Fast Loan UK provides an instructive example of how lenders can efficiently scale their small business lending operations. The company has been recognised for its quick and efficient financial solutions, with transparent lending practices and a commitment to responsible lending [30]. Their rigorous assessment process ensures they lend only what borrowers can afford to repay, while their speed and flexibility allow them to meet diverse financial needs efficiently.

What specific operational changes are most effective for scaling small business lending? Automation of routine tasks, implementation of risk-based pricing models, and development of standardized documentation packages can significantly reduce processing costs while maintaining service quality. These changes enable lenders to handle increased volumes without proportional increases in staffing costs.

Accounts Receivable Automation as a Competitive Differentiator

As credit conditions tighten, financial institutions can gain a competitive edge by integrating accounts receivable (AR) automation into their loan management software. AR automation currently holds the lowest commercial maturity score among fintech solutions studied, compared to accounts payable automation which is closer to scaling [7]. This underdevelopment represents a significant opportunity for financial institutions to differentiate themselves.

By incorporating AR automation into loan origination processes, lenders can gain deeper visibility into borrower cash flow patterns and payment histories, enabling more accurate risk assessment while simultaneously improving operational efficiency. This is a critical advantage as credit conditions tighten and competition for qualified borrowers intensifies.

Implementing AR automation in lending workflows presents specific challenges that institutions must navigate. The integration typically requires a phased approach, beginning with data standardization and system integration before moving to full automation. Financial institutions should expect a 3-6 month implementation timeline for basic functionality, with more comprehensive integration taking 9-12 months [7].

Resource requirements for successful AR automation implementation include dedicated IT resources, process redesign specialists, and staff training programs. Many institutions find that partnering with specialized fintech providers accelerates implementation and reduces internal resource demands. The most successful implementations focus on specific use cases initially, such as automating receivables data collection for risk assessment, before expanding to more comprehensive functionality [7].

AR automation provides several specific benefits for loan origination and management. It enables real-time monitoring of borrower receivables, providing early warning of potential cash flow issues. It also streamlines the collection and analysis of financial data, reducing the administrative burden on both lenders and borrowers. Additionally, it provides deeper insights into customer payment behaviour, enhancing the accuracy of credit risk assessment.

Alternative Financing Models for Managing Credit Constraints

As traditional credit conditions tighten, financial institutions can maintain growth by diversifying into alternative financing models. Revenue-based financing is an area primed for significant growth, driven by the rise of digital-native SMBs such as e-commerce and SaaS companies. Despite this, invoice financing remains another promising segment [7].

These alternative financing models often rely on different risk assessment methodologies and data points than traditional loans, allowing financial institutions to continue serving SMEs that might otherwise be excluded under stricter credit criteria. They also potentially maintain healthier risk profiles through different security structures.

The British Business Bank's 2025 report highlights the evolving finance markets reaching entrepreneurs from different backgrounds and regions, indicating a shift towards more inclusive and varied financing options [19]. This diversification of finance markets enables access for entrepreneurs from various backgrounds and regions, creating new opportunities for lenders who can adapt their operations to support these alternative models.

The unregulated lending market, covering non-FCA-regulated loans, is expected to grow to £12.2bn by the end of 2025 [31]. Bridging finance is crucial for acquisitions, refurbishments, and short-term liquidity. Despite uneven growth in the housing market, developers are facing slower than expected exit timelines and are using developer exit loans to hold or market completed schemes while avoiding distressed sales or expensive refinancing with high-street lenders.

"Fintech innovations have expanded alternative financing options, offering CFOs new ways to access capital. These include subscription and fee-based online lending marketplaces and blockchain applications, providing better terms and greater flexibility for managing working capital." [12]

Integrating alternative financing models into existing loan origination frameworks requires operational adaptations. Lenders need to implement specialised assessment criteria, monitoring protocols, and servicing procedures tailored to each financing model. By developing these capabilities, financial institutions can diversify their lending portfolios and maintain growth despite tightening credit conditions.

The rise of digital-native SMBs has created particular demand for revenue-based financing options that align repayment schedules with business cash flows [7]. For financial institutions, this represents an opportunity to expand their product offerings while leveraging many of the same operational capabilities and risk assessment frameworks used in traditional lending.

The alternative financing landscape has also seen the emergence of innovative structures such as Net Asset Value (NAV) lending, which provides loans secured by the value of a private equity fund's existing portfolio of assets [13]. This approach offers enhanced liquidity solutions and allows lenders to mitigate risks associated with market volatility and individual asset underperformance through cross-collateralization.

Leveraging Headroom in Existing Facilities

Despite the 14% lending increase, SMEs maintain approximately 50% utilisation rates on existing credit facilities [2]. This represents a significant opportunity for financial institutions to increase lending volumes without the full operational burden of new customer acquisition and onboarding.

By implementing data-driven portfolio management strategies, lenders can identify existing customers with growth potential and unutilised credit capacity, enabling targeted expansion of existing relationships. This approach is particularly valuable as credit conditions tighten and new customer acquisition becomes more challenging.

Advanced loan management software can play a crucial role in identifying and activating this untapped potential. By analysing usage patterns, financial performance indicators, and market trends, these systems can highlight opportunities for increasing facility utilisation among existing borrowers. This enables lenders to grow their loan books efficiently without proportionally increasing their operational burden.

The 50% utilisation rate has remained steady despite the overall lending growth [2], indicating that many SMEs have substantial headroom within their existing facilities. For financial institutions, this represents a significant revenue opportunity that can be activated with relatively low operational cost compared to new customer acquisition.

A recent example of facility expansion comes from Amazing AI plc (AAI), a global fintech group specialising in online consumer loans, which has amended the terms of its Loan Agreement and increased the maximum amount of the loan facility from £2 million to up to £5 million effective 1 July 2025 [32]. This move demonstrates how financial institutions can expand existing facilities to support growth without the operational complexity of establishing entirely new lending relationships.

Financial institutions are implementing specific strategies to leverage this headroom effectively. Some are conducting targeted portfolio reviews to identify borrowers with strong financial performance but low facility utilisation, then proactively engaging these customers with tailored growth financing proposals. Others are implementing automated monitoring systems that flag underutilised facilities meeting certain criteria, triggering relationship manager outreach [2].

Environmental Sustainability in Lending Practices

Over the next few years, there will be a heightened focus on environmental sustainability within lending practices, with financial institutions prioritizing green financing options. The British Business Bank's 2025 report indicates that smaller businesses are increasingly considering external finance to fund environmental sustainability initiatives [19].

This growing emphasis on environmental considerations presents both challenges and opportunities for lenders. On one hand, it requires developing new expertise in assessing the environmental impact and sustainability of borrower business models. On the other hand, it opens up new market segments and product opportunities in green financing.

Financial institutions can prepare for this shift by implementing environmental, social, and governance (ESG) criteria into their loan origination processes. This includes developing specialized assessment frameworks for green loans, training staff on sustainability evaluation, and implementing monitoring systems to track the environmental impact of lending portfolios.

The Future Homes Standard, due in June 2025, is accelerating demand for energy-efficient housing [31]. Green finance options are being adapted to meet developer goals and offer cost advantages. This regulatory change is creating new lending opportunities in the property development sector, particularly for financial institutions that can efficiently assess and process green financing applications.

By positioning themselves at the forefront of sustainable finance, lenders can not only capture growing market demand but also prepare for increasingly stringent regulatory requirements around climate risk and environmental impact. This proactive approach to environmental sustainability can become a competitive differentiator in the evolving lending landscape.

Conclusion: Building Scalable Lending Operations for Sustained Growth

The 14% surge in SME lending presents a significant opportunity for financial institutions, but capitalising on this growth requires strategic operational enhancements. By modernising loan origination systems, integrating AI-driven credit risk assessment, implementing sector-specific lending strategies, and exploring alternative financing models, lenders can scale their operations efficiently while maintaining robust risk management.

Looking ahead, business lending is expected to continue its growth trajectory, with forecasts indicating increases of 5.6% in 2025 and 6.2% in 2026 [20]. This sustained growth underscores the importance of implementing scalable lending operations that can accommodate rising volumes without proportional increases in operational costs.

The UK Government's June 2025 Spending Review outlined a £39 billion ten-year affordable housing programme, including £10 billion via Homes England, aimed at helping small-to-medium-sized developers overcome planning constraints [31]. This initiative is likely to further stimulate lending demand in the property development sector, requiring lenders to prepare their operational capabilities accordingly.

As the lending landscape continues to evolve, financial institutions that invest in operational efficiency and technological innovation will be best positioned to capture market share and drive sustainable growth. The key lies in building scalable systems and processes that can adapt to changing market conditions and borrower needs.

In this environment, kennek's end-to-end lending management platform offers financial institutions a comprehensive solution for scaling their SME lending operations. By combining automation, real-time data, and flexible API infrastructure, kennek streamlines every stage of the lending lifecycle, from origination to servicing and reporting. This enables lenders to handle increased volumes efficiently while maintaining robust risk management and compliance.

With the right operational strategies and technological infrastructure, financial institutions can turn the current surge in SME lending into a sustainable growth opportunity, delivering value to both shareholders and the broader economy.

Our Opinion

The sustained growth in UK SME lending underscores a critical need for financial institutions to move beyond legacy systems. We see this surge not just as an opportunity, but as a clear signal that the market requires operational infrastructure capable of handling increased volume and complexity with precision. Scaling efficiently while navigating tightening credit conditions demands integrated, automated workflows across the entire lending lifecycle. Our view is that leveraging real-time data and incorporating advanced technologies, such as AI for risk assessment, within a governed framework is essential for maintaining both speed and accuracy, particularly when addressing the diverse needs of different sectors and the specific challenges of high-volume, smaller loans.

We believe the future of SME lending lies in flexibility and data-driven strategy. As alternative financing models gain traction and factors like environmental sustainability become integral to credit decisions, lenders require platforms that can seamlessly adapt to new product structures and incorporate evolving data requirements. Unlocking the potential within existing portfolios also relies on robust data analytics. Ultimately, we contend that a unified, intelligent lending management platform is not merely an enhancement; it is the foundational requirement for lenders to manage risk effectively, achieve operational excellence, and drive sustainable growth in this dynamic market.

References

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