UK Lenders: Combat Rising Non-Accrual Rates with Better Monitoring

The Alarming Rise in Non-Accrual Rates: Understanding the Current Private Credit Landscape

The private credit sector is facing a troubling trend that demands immediate attention from UK lenders. Recent data from the US market—which often serves as a bellwether for global credit conditions—reveals a significant deterioration in loan performance. Business development companies (BDCs), which provide valuable insight into the broader private credit market as they offer tax-efficient access to this otherwise opaque sector, reported that nearly a third of their loan book failed to make payments in Q1 2025, amounting to a staggering $1.4 billion. More concerning still, 27% of these non-accruals were new instances, with 54 BDCs reporting at least one new non-accruing loan in the first quarter [1].

This trend is particularly relevant for UK lenders as the private credit market has expanded rapidly globally. While specific UK non-accrual data is less transparent than the US market, the interconnected nature of global credit markets suggests similar pressures are building in the UK private lending sector. Jake Pollack, head of global credit financing at J.P. Morgan, notes that there is "potential increase in stress across all corporate credit portfolios due to increased public credit and private credit stress" [20]. This observation applies to both US and European markets, including the UK, where private credit has become an increasingly important funding source.

The broader industry data supports these concerns. In Q3 2024, net realized losses for BDCs escalated to $864 million, up from $518 million in the previous quarter and $528 million in the same period the prior year [26]. Additionally, the weighted average non-accrual rate for public and non-traded/private BDCs decreased slightly to 1.43% in Q3 2024 from 1.7% in Q2 2024, yet this figure remains significant and indicates ongoing stress in the private credit market [26].

In the UK specifically, the financing market has seen significant activity in recent months. According to data from Savills, debt capital for the UK property market is now coming from lenders in 47 countries, a record number [22]. This diversification of funding sources, while positive for market liquidity, creates additional monitoring challenges as each capital provider may have different risk appetites and lending criteria.

While the Bank of England has expressed concerns regarding risk management practices within the private equity sector, highlighting issues such as insufficient transparency in valuation practices and leverage levels [9], the UK market has shown some positive signs—with new mortgage completions increasing by 50.4% in Q1 according to Bank of England data [2]. However, the simultaneous 7.2% rise in the UK's total stock of possessions suggests that despite growth in lending volumes, credit stress remains a significant challenge.

For UK lenders, particularly SME banks and fintech lenders, these statistics underscore the urgent need to enhance monitoring capabilities across their loan portfolios. The traditional reactive approach to credit risk—waiting until payments are missed before taking action—is increasingly inadequate in today's volatile economic environment. Instead, forward-thinking institutions are implementing sophisticated monitoring systems that can identify potential defaults before they materialise, enabling proactive intervention that preserves both borrower relationships and portfolio performance.

Early Warning Systems: Implementing AI-Driven Risk Indicators for Loan Portfolio Management

The integration of artificial intelligence into credit risk management represents a transformative opportunity for UK lenders seeking to address rising non-accrual rates. Leading financial institutions are already deploying hundreds of AI use cases, with a growing emphasis on enhancing risk management systems that can detect subtle patterns in borrower behaviour long before traditional indicators would signal trouble [3].

The UK's Financial Conduct Authority (FCA) has taken a proactive approach to encouraging AI innovation, launching a "supercharged sandbox" where approved financial services firms can explore new AI applications using Nvidia's high-performance accelerated computing products. Testing is set to begin in October, with potential uses for AI to detect and prevent authorised push payment fraud and stock market manipulation [25]. This regulatory support provides UK lenders with a unique opportunity to develop and test advanced monitoring capabilities in a controlled environment.

"The system combines explainable AI (XAI) with real-time credit activity monitoring to provide financial guidance to a broad audience,"

— Jeevani Singireddy, Fintech Expert [27]

Singireddy's framework includes intelligent credit monitoring that alerts users to changes in credit status or new credit inquiries. Research involving 112 participants found a significant increase in user satisfaction and trust when financial advice was accompanied with real-time credit monitoring and clear explanations [27].

However, implementation challenges remain significant. A recent study found that only 25% of AI initiatives are currently delivering on their promised ROI, a troubling statistic given the volume of AI projects companies are pursuing globally [28]. This underscores the importance of careful planning and execution when implementing AI-driven monitoring systems.

Recent innovations in this space include a hybrid quantum-classical deep neural network approach for credit risk assessment in the banking sector. This framework combines quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA), aiming to enhance the accuracy and efficiency of credit risk evaluation [15]. Such cutting-edge approaches demonstrate how rapidly the field is evolving beyond conventional credit scoring models.

For UK SME banks and fintech lenders, these technologies are particularly valuable as they often lack the extensive risk management teams of larger institutions. AI-driven systems can provide enterprise-grade monitoring capabilities without requiring significant headcount increases, enabling smaller lenders to compete effectively while managing credit risk. How can these institutions ensure their AI implementations deliver measurable improvements in non-accrual rate reduction?

Real-Time Portfolio Visibility: Creating a Single Source of Truth for Loan Performance

A fundamental challenge for many UK lenders is the fragmentation of data across multiple systems, creating blind spots that prevent comprehensive portfolio monitoring. This siloed approach to data management makes it virtually impossible to identify emerging risks in real time, leaving institutions vulnerable to sudden increases in non-accrual rates.

The solution lies in creating a unified data platform that serves as a single source of truth for loan performance. By consolidating information from origination systems, servicing platforms, accounting software, and external data sources, lenders can achieve the comprehensive visibility needed to monitor portfolio health effectively.

The global syndicated loan market, valued at approximately US$6 trillion, has faced significant inefficiencies due to manual processes, fragmented technology, and a complex vendor ecosystem, leading to poor data quality and increased operational costs [16]. These challenges mirror those faced by many UK lenders, particularly in the private credit space where deal structures are often complex and monitoring requirements are substantial.

"As powerful as our platform is now, we continue to build every day toward something greater," said Cynthia Sachs, Founding CEO and Board Member of Versana [16]. This sentiment reflects the industry's recognition that continuous improvement in data integration and visibility is essential for effective credit risk management. Versana's platform, launched in December 2022 to address inefficiencies in the global syndicated loan market, provides near-real-time digital data that increases transparency and efficiency—enabling market participants to enhance self-service capabilities, automate manual workflows, and access up-to-date post-origination portfolio positions [16].

This approach aligns with broader industry trends toward real-time data analysis and integrated monitoring frameworks. As noted by industry experts, future innovations in loan portfolio management are increasingly focused on consolidating data streams to enable more informed decision-making [4]. The benefits extend beyond risk management to include enhanced operational efficiency, improved regulatory compliance, and more transparent stakeholder reporting.

A study by Accenture shows that 76% of banks anticipate open banking usage to grow by more than 50% by 2026 [21]. This trend is particularly relevant for UK lenders, as the UK has been at the forefront of open banking adoption. Open banking APIs enable lenders to access real-time transaction data, account information, and financial behavior patterns directly from customers' bank accounts (with appropriate consent). This provides a much richer and more current view of borrower financial health than traditional credit bureau data or periodic financial statements.

For UK lenders, the implementation of open banking-enabled monitoring can transform credit risk management by providing:

• Early detection of cash flow issues through real-time transaction monitoring
• Automated covenant compliance checking using current account data
• More accurate affordability assessments based on actual spending patterns
• Reduced reliance on lagging indicators like credit scores

For UK lenders, particularly fintech companies with their modular technology stacks, the implementation of such unified platforms represents a critical step in combating rising non-accrual rates. By eliminating information silos and enabling real-time monitoring of key risk indicators, these systems provide the visibility needed to identify troubled loans at the earliest possible stage—when intervention is most likely to succeed.

Sector-Specific Monitoring Strategies: Tailoring Approaches to High-Risk Lending Segments

The UK lending market encompasses diverse segments with unique risk profiles, requiring tailored monitoring approaches rather than one-size-fits-all solutions. This is particularly evident in the rapidly growing later life lending sector, which presents both significant opportunities and distinctive challenges for credit risk management.

Recent data from UK Finance highlights the scale of this opportunity, with residential loans to older borrowers increasing by 7.6% in Q1 2025. Buy-to-let lending in this demographic now accounts for 21.5% of total BTL loans, reflecting the growing importance of this market segment [5]. Even more striking is the overall growth in later life lending, with 38,510 new loans advanced to older borrowers in Q1—a remarkable 33.5% increase year on year, with the value of this lending reaching £6.1 billion, an increase of 42.6% compared with the same quarter a year before [6].

"Older borrowers are increasingly seeking flexible, tailored solutions to their financial needs," said Simon Webb, managing director of capital markets and finance at later life lender LiveMore [5]. This growth could be attributed to rising cost of living, delaying paying off their mortgages until later in life, or higher interest rates and increasing household bills. Industry players also noted an increase in demand for customised later life lending products.

However, this growth brings unique monitoring challenges that require specialized risk management approaches. Later life lending involves providing financial products such as mortgages to older borrowers, presenting unique risk management challenges due to factors like income stability, health considerations, and regulatory constraints.

One key best practice is the development of tailored affordability assessments that consider the specific financial circumstances of older borrowers. This includes evaluating pension income, savings, and potential healthcare costs to ensure that loan repayments remain manageable throughout the loan term. Another critical practice is the incorporation of flexible product features, such as lifetime mortgages or equity release schemes, which allow borrowers to access funds without the burden of monthly repayments [12].

The property lending sector also presents unique monitoring challenges. According to data from Savills, debt capital for the UK property market is now coming from lenders in 47 countries, a record number [22]. The residential and living sectors were mostly favoured, while the offices, retail and secondary logistics sectors reported an improvement in lending sentiment compared to the previous year. This diversification of funding sources requires sophisticated monitoring frameworks that can account for varying risk appetites and lending criteria across international capital providers.

Another emerging sector requiring specialized monitoring is Net Asset Value (NAV) lending, which has gained traction as private equity funds seek new liquidity solutions. This form of lending, secured by the value of a fund's existing portfolio, has seen significant growth, with projections ranging from $50 billion to over $100 billion in annual volume by the end of the 2020s [19]. The unique risk profile of NAV lending—where loan performance is tied to the valuation and performance of underlying portfolio companies—requires specialized monitoring approaches that can track both fund-level metrics and the health of individual portfolio investments.

What specific monitoring frameworks are most effective for managing concentration risks across these diverse lending segments?

Regulatory Compliance and Stress Testing: Building Resilient Loan Portfolios

As regulatory scrutiny intensifies in response to rising credit stress, UK lenders must integrate compliance requirements into their monitoring frameworks. Regulatory stress testing, in particular, offers a valuable tool for identifying potential vulnerabilities before they manifest as non-accrual loans.

The Bank of England (BoE) has established comprehensive stress testing requirements to ensure the stability of the financial system. The BoE's approach involves subjecting major UK banks and building societies to concurrent stress tests, evaluating their ability to withstand severe but plausible economic shocks. These tests consider factors such as significant declines in GDP, increases in unemployment, and substantial drops in asset prices [13].

In 2024, the BoE announced a softer stress test scenario compared to previous years, predicting a lower peak in UK inflation and a smaller decline in global GDP. Despite the softened conditions, the scenario continues to test resilience, including anticipating a 20% drop in world trade due to heightened geopolitical tensions [14].

Recent regulatory developments in the United States provide insights into potential future directions for UK regulation. In May 2024, U.S. federal banking regulators finalized new reporting requirements for bank loans and commitments to nonbank financial entities. These requirements aim to improve the banking system's understanding and supervision of credit concentrations and risks, applying to insured depository institutions with over $10 billion in total assets [17]. Such developments suggest a growing regulatory focus on the interconnections between traditional banks and the expanding private credit sector—a trend that UK lenders should monitor closely.

With the rapid growth of digital banks and alternative lenders, regulatory bodies are intensifying scrutiny, particularly concerning data privacy and algorithmic bias. Evolving regulatory frameworks are being developed to address these issues [23]. Financial institutions will need to enhance their compliance mechanisms and conduct rigorous stress testing to ensure portfolio resilience.

A case study from a large bank holding company illustrates the practical application of stress testing for portfolio management. The bank engaged external experts to develop a methodology for internal stress testing across a multi-sector, fixed-income portfolio, including Comprehensive Capital Analysis and Review (CCAR) requirements. This approach delivered quarterly reporting for internal stress testing and annual reporting of CCAR testing requirements, providing the bank with in-depth knowledge and speed of delivery regarding stress testing requirements [16].

Similarly, a major corporate credit union sought outside consultation to deliver pre-purchase advisory and ongoing portfolio surveillance for their asset-backed securities (ABS) investments. The implementation of monthly valuation, stress testing, and risk analytics, as well as ABS shelf analysis, break-even, and credit sensitivity analysis for residential mortgage-backed securities and ABS portfolios, enabled the credit union to meet its business need for independent risk analysis, valuation, and ongoing surveillance [16].

Forward-thinking lenders are leveraging these regulatory requirements to enhance their monitoring capabilities. By incorporating stress testing scenarios into their regular portfolio analysis, these institutions can simulate adverse economic conditions and identify loans that might become troubled under stress. This proactive approach not only satisfies regulatory expectations but also strengthens portfolio resilience through early identification of at-risk exposures.

Intervention Strategies: Developing Effective Workout Processes for At-Risk Loans

Identifying troubled loans through enhanced monitoring is only the first step; effective intervention is equally critical for preventing non-accrual status. Recent data suggests that timely intervention can make a significant difference—while the UK's total stock of possessions rose by 7.2% in Q1 2025, new arrears cases actually fell by 1.7% from the previous quarter and were 1.2% lower than the year before [2]. This divergence indicates that proactive engagement with at-risk borrowers can prevent troubled loans from progressing to more serious delinquency.

The inability to detect early warning signs of loan deterioration has resulted in a 27% increase in new non-accrual loans and significant financial losses across the industry. This highlights the critical importance of not only identifying at-risk loans but also having structured intervention protocols in place to address issues before they escalate.

Effective loan portfolio intervention strategies are essential for financial institutions to manage credit risk and maintain portfolio health. One key success metric is the reduction in non-performing loan (NPL) ratios, indicating a decrease in the proportion of loans that are in default or close to default. A successful intervention strategy should lead to a noticeable decline in NPL ratios over time.

Another important metric is the improvement in recovery rates, which measure the percentage of defaulted loan amounts that are successfully recovered. Enhanced recovery rates suggest that intervention strategies are effective in mitigating losses from defaulted loans. Additionally, monitoring changes in loan loss provisions can provide insights into the effectiveness of intervention strategies. A decrease in provisions may indicate improved credit quality and reduced expectations of future losses.

Successful intervention strategies begin with clear triggers based on monitoring system alerts. When early warning indicators suggest a loan may be deteriorating, a structured response protocol should be activated immediately. This might include direct borrower contact, financial assessment, and the development of tailored workout options based on the specific circumstances.

Private capital is increasingly collaborating with banks to offer solutions in asset-based finance. These collaborations include portfolio sales, partnerships on new originations, and customized transactions known as synthetic risk transfers (SRTs), where banks shift risk from their balance sheets to other investors [19]. These innovative structures can provide valuable tools for managing troubled loans, allowing lenders to reduce exposure to at-risk sectors while maintaining client relationships.

The private credit market is projected to grow significantly, reaching nearly US$2 trillion in assets under management by 2024 [24]. This expansion reflects a shift towards alternative lending solutions, with institutions seeking flexible and efficient platforms to manage diverse lending portfolios. As this market grows, the need for sophisticated intervention strategies becomes even more critical.

The range of potential interventions is diverse, from simple payment plans and temporary forbearance to more comprehensive loan modifications or restructuring. The key is to engage early, when the borrower still has financial flexibility and before the situation deteriorates further. By developing structured workout processes triggered by monitoring alerts, lenders can significantly reduce the number of loans that progress to non-accrual status.

Technology Integration: Overcoming Legacy System Limitations in Loan Management Software

For many UK lenders, legacy systems represent a significant barrier to implementing advanced monitoring capabilities. These outdated platforms often lack the flexibility to incorporate new data sources, apply sophisticated analytics, or provide the real-time visibility needed for effective credit risk management.

The challenge of technology integration is particularly acute in the banking sector, where core systems may be decades old and difficult to replace. Industry analysis highlights this as a key obstacle, with a 2024 banking outlook emphasising the necessity for financial institutions to modernise their technology infrastructure to remain competitive [8].

Legacy technology infrastructure limits the implementation of AI-driven risk assessment capabilities essential for predictive loan monitoring. This limitation is particularly problematic as the complexity of loan portfolios increases, with lenders operating across diverse sectors with unique risk profiles that require sophisticated analytical capabilities to monitor effectively.

However, complete system replacement is not always necessary or practical. Modern loan management software solutions can overcome legacy limitations through API-driven architectures that extract data from existing systems and present it in unified dashboards. This approach enables lenders to enhance monitoring capabilities without the cost and disruption of wholesale technology transformation.

Busey Bank's experience illustrates the transformative potential of modern loan management software. As Van Dukeman, Chairman, President & CEO of Busey Bank, noted: "(nCino) really transformed manual origination, credit, and closing activities for the origination or renewal of commercial loans to an automated process" [18]. By implementing nCino's Commercial Loan Origination System, the bank streamlined front- and back-office processes such as origination, underwriting, risk management, and reporting, significantly reducing manual processes and enhancing operational efficiency.

Another example comes from a large European financial services company that required an AutoML system tailored to its unique requirements to build high-quality machine learning models efficiently across its financial services ecosystem. The company developed LightAutoML, an AutoML system designed to meet its specific needs, enabling the rapid development of machine learning models that performed at the level of experienced data scientists. This system was piloted and deployed in numerous applications, significantly accelerating the model development process while maintaining high quality.

The UK's Financial Conduct Authority (FCA) has given banks the approval to test artificial intelligence in a bid to encourage more risk-taking in the UK financial sector. The FCA has launched a "supercharged sandbox" where approved financial services firms will be able to explore new AI applications using Nvidia's high-performance accelerated computing products [25]. This regulatory support for AI experimentation provides an opportunity for UK lenders to develop and test advanced monitoring capabilities in a controlled environment.

Kennek's platform exemplifies this approach, providing a comprehensive loan management solution that integrates with existing systems through flexible APIs. By creating a layer of modern functionality on top of legacy infrastructure, such platforms enable enhanced monitoring capabilities without requiring complete system replacement. This makes advanced credit risk management accessible to institutions of all sizes, regardless of their current technology state.

For UK SME banks and fintech lenders, Kennek offers specific advantages in combating rising non-accrual rates. The platform combines automation, real-time data, and flexible API infrastructure to streamline every stage of the lending lifecycle, from origination to servicing and monitoring. This enables lenders to identify troubled loans earlier and intervene more effectively, directly addressing the challenge of rising non-accrual rates.

Key features particularly relevant to UK lenders include:

• Real-time portfolio monitoring with customizable risk indicators tailored to specific lending segments
• Automated covenant tracking and compliance monitoring
• Integrated workflow tools that trigger intervention protocols when early warning indicators are activated
• Comprehensive reporting capabilities that provide transparency for stakeholders and regulators

By leveraging these capabilities, UK lenders can overcome the limitations of legacy systems and implement the sophisticated monitoring frameworks needed to combat rising non-accrual rates effectively.

Conclusion: A Proactive Approach to Credit Risk Management

The alarming rise in non-accrual rates across the private credit sector demands a fundamental shift in how UK lenders approach portfolio monitoring. The traditional reactive model—waiting until payments are missed before taking action—is increasingly inadequate in today's volatile economic environment. Instead, forward-thinking institutions are implementing sophisticated monitoring systems that can identify potential defaults before they materialise, enabling proactive intervention that preserves both borrower relationships and portfolio performance.

Fragmented data systems create blind spots in portfolio visibility, preventing timely identification of at-risk borrowers before payment failures occur. This challenge is compounded by outdated manual monitoring processes that cannot scale to handle complex loan portfolios across diverse sectors with unique risk profiles. The lack of structured intervention protocols triggered by early warning indicators results in missed opportunities to prevent troubled loans from progressing to non-accrual status.

The evidence is clear: institutions that embrace enhanced monitoring capabilities are better positioned to navigate credit stress. The 27% increase in new non-accrual loans reported among BDCs serves as a stark reminder of what happens when monitoring systems fail to provide adequate early warning. Conversely, the divergence between rising possessions and falling arrears in the UK market demonstrates that timely intervention can prevent troubled loans from deteriorating further.

To effectively combat rising non-accrual rates, UK lenders should take the following actionable steps:

1. Implement AI-driven early warning systems that can detect subtle patterns in borrower behavior before traditional indicators signal trouble
2. Create unified data platforms that eliminate information silos and provide comprehensive portfolio visibility
3. Develop sector-specific monitoring approaches tailored to high-risk segments like later life lending and NAV financing
4. Leverage regulatory stress testing to identify potential vulnerabilities before they manifest as non-accrual loans
5. Establish structured intervention protocols triggered by early warning indicators
6. Overcome legacy system limitations through API-driven integration with modern loan management platforms

By adopting these strategies, UK lenders—from established banks to emerging fintech companies—can significantly enhance their ability to combat rising non-accrual rates. The financial institutions that embrace this proactive approach to credit risk management will be better positioned to navigate the challenges ahead. By identifying troubled loans earlier and intervening more effectively, these lenders can maintain portfolio quality even as economic conditions fluctuate. In an increasingly competitive market, this capability represents not just a risk management imperative but a significant competitive advantage.

Our Opinion

The increase in non-accrual rates across private credit underscores a critical failing in traditional lending operations. We see this directly stemming from reactive monitoring practices, where lenders wait for distress signals rather than anticipating them. Compounding this is the widespread issue of fragmented data, trapped in disparate systems, which creates significant blind spots and prevents the comprehensive, real-time visibility essential for effective portfolio management. This reliance on outdated legacy infrastructure fundamentally limits the ability of lenders to implement the sophisticated, data-driven approaches necessary to navigate today's volatile market conditions and manage credit risk effectively.

We believe the path forward requires a decisive shift towards proactive, integrated credit risk management. Establishing a single source of truth for all loan data is paramount, enabling real-time portfolio monitoring with configurable risk indicators tailored to specific lending segments. We know that leveraging modern, API-driven technology is key to overcoming legacy limitations, facilitating the necessary data consolidation and enabling structured intervention protocols triggered by early warning signs. This approach not only enhances portfolio resilience and mitigates losses but also drives operational efficiency, positioning lenders to maintain quality and competitive advantage in a challenging environment.

About the Author

Xavier De Pauw is the co-founder & CFO of kennek, a complete lending software for alternative credit. A seasoned banker turned fintech entrepreneur, Xavier spent 10 years at Merrill Lynch specialising in structured finance before co-founding challenger banks MeDirect Group and MeDirect Bank Belgium, building a €2.5 billion balance sheet. His extensive experience in traditional banking and fintech innovation provides unique insights into the challenges UK lenders face when implementing advanced monitoring systems to combat rising non-accrual rates.

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