The UK private credit market is facing an alarming crisis as non-accrual rates surge to unprecedented levels. Recent data from market data platform Solve reveals that US business development companies (BDCs) reported nearly a third of their loan book failed to make payments in Q1 2025, amounting to a staggering £1.4 billion in troubled loans. More concerning still, 27% of these non-accruals represent new instances, with 54 BDCs reporting at least one new non-accruing loan in the first quarter [1]. This significant proportion of new non-accruals indicates an accelerating problem rather than a legacy issue, suggesting UK lenders need to prepare for continued deterioration in loan performance.
The sectors most severely affected include commercial real estate, particularly office and retail spaces, which have faced persistent challenges since the pandemic shifted working patterns. Additionally, consumer-facing businesses in the hospitality and retail sectors are showing heightened stress, with loan performance deteriorating rapidly amid inflationary pressures and reduced consumer spending. These sector-specific vulnerabilities highlight the need for targeted monitoring and intervention strategies [1].
This trend serves as a critical warning signal for UK lenders, as BDCs represent a proxy for the broader private credit market. The situation is particularly concerning given the rapid expansion of private credit markets. S&P Global Ratings reported that non-performing loans in private credit markets have nearly tripled, with default rates expected to rise as creditor leniency diminishes [21]. Financial Times highlighted that over 10% of loans involved payment-in-kind (PIK) interest substitutions in late 2024, suggesting underlying borrower stress [22].
The global private credit market has experienced substantial growth, even as traditional markets face volatility. Barclays Private Bank reported that private credit assets under management surged from less than $500 billion in 2012 to over $1.6 trillion in 2024, with expectations to reach $2.8 trillion by 2028 [23]. Morgan Stanley highlighted that private credit expanded to approximately $1.5 trillion at the start of 2024, up from $1 trillion in 2020, and is estimated to soar to $2.6 trillion by 2029 [24].
"Financial covenants... can add value in an uncertain market," notes Wellington Management, underscoring the need for robust risk management practices in today's private credit environment [12].
Key Takeaways: The 30% non-accrual rate represents a significant and accelerating crisis in UK private credit, with new non-accruals accounting for 27% of the total. Sector-specific vulnerabilities are emerging, particularly in commercial real estate and consumer-facing businesses. This trend demands urgent attention from lenders, who must strengthen their risk management capabilities to navigate this challenging environment.
As non-accruals continue to rise, financial institutions are increasingly turning to advanced technologies to identify early warning signs before loans deteriorate. Artificial Intelligence (AI) and machine learning are transforming loan monitoring systems, enabling lenders to move from reactive to proactive portfolio management.
Research has demonstrated that AI models, including logistic regression and deep neural networks, can effectively predict loan defaults. Studies show that these technologies can reduce the default risk of issued loans by as much as 70%, highlighting their potential in improving credit risk models [11]. Recent advancements include a hybrid quantum-classical deep neural network framework for credit risk assessment, which leverages quantum deep learning techniques to enhance the accuracy and efficiency of credit risk evaluation [13].
In the UK specifically, the Financial Conduct Authority (FCA) has recently launched a "supercharged sandbox" where approved financial services firms can explore new AI applications using high-performance accelerated computing products. This initiative, set to begin in October 2025, aims to encourage more risk-taking and innovation in the UK financial sector, with potential applications for AI in detecting and preventing authorised push payment fraud and stock market manipulation [25]. This regulatory support for AI innovation provides UK lenders with a unique opportunity to develop and implement advanced early warning systems under regulatory guidance.
UK financial advisers are overwhelmingly positive about AI's potential, with a recent survey by Dynamic Planner finding that 94% of advisers expect AI to be positive for the industry. The survey, which included over 400 advice professionals across the UK, revealed that 86% of advisers expect to increase their client base over the next 12 months, with expectations that AI will reduce the cost to serve and enable firms to service more clients [26]. This positive outlook suggests that UK financial institutions are ready to embrace AI-powered early warning systems as part of their loan management strategies.
Financial institutions are rapidly adopting Large Language Models (LLMs) to enhance credit assessments and automate language-intensive processes. These AI technologies enable real-time monitoring and early warning systems, helping banks intervene before risks materialize and reducing potential losses [2]. The integration of AI, particularly deep learning and big data analysis, represents a significant opportunity for lenders to detect subtle patterns that human analysts might miss, potentially identifying troubled loans months before they officially become non-accruals.
Key early warning indicators that AI systems can monitor include:
The power of AI in loan monitoring extends beyond simple pattern recognition. Advanced systems can analyse both structured financial data and unstructured information such as news reports, social media sentiment, and industry trends to create a comprehensive risk profile for each borrower. This holistic approach enables lenders to anticipate potential defaults with greater accuracy and implement mitigation strategies before loans deteriorate.
Key Takeaways: AI-powered early warning systems represent a critical tool for UK lenders facing rising non-accruals. The FCA's "supercharged sandbox" initiative provides regulatory support for AI innovation in financial services, while survey data shows that 94% of UK financial advisers expect AI to positively impact the industry. Implementation of these technologies has demonstrated significant benefits, with studies showing potential default risk reductions of up to 70%.
Even the most sophisticated AI-powered loan monitoring systems will fail without high-quality data. Effective credit risk management fundamentally requires comprehensive, accurate information, yet many institutions struggle with poor data quality that hampers their ability to perform accurate risk assessments and comply with regulations [3].
McKinsey's research finds that incomplete and inconsistent data is a primary cause of risk assessment failures, and that robust data governance is now fundamental to sustainable credit operations [3]. This is particularly critical as data quality issues represent a significant blind spot for many lenders, especially as they scale their portfolios across increasingly diverse markets.
According to McKinsey, effective data governance frameworks should include several key components:
McKinsey further emphasizes that organizations should establish a dedicated data governance function with clear authority and accountability. This function should be responsible for setting data standards, monitoring compliance, and driving continuous improvement in data quality across the organization. Additionally, they recommend implementing data quality metrics that are regularly tracked and reported to senior management, ensuring that data quality remains a strategic priority [3].
The complexity of today's lending environment demands sophisticated data management capabilities. Recent data indicates that debt capital for the UK property market is now coming from lenders in 47 countries, a record number, with residential and living sectors mostly favoured [4]. This diversification of lending sources introduces additional complexity to risk assessment, as different lenders may have varying underwriting standards and risk appetites, making robust data governance essential for effective cross-portfolio analysis.
A case study from Emprise Bank demonstrates the impact of implementing comprehensive loan management systems. By deploying nCino's end-to-end lending management platform, the bank achieved up to 100% reduction of manual reports for credit portfolio management and reduced data re-keying during sizing, underwriting, and spreading by 75% [15]. This significant improvement in data quality and efficiency directly translated to enhanced credit risk management capabilities.
"(nCino) really transformed manual origination, credit, and closing activities for the origination or renewal of commercial loans to an automated process. We went from doing a lot of the nuts and bolts manually to doing it in an automated process," said Van Dukeman, Chairman, President & CEO of Busey Bank [15].
Implementing robust data governance frameworks allows institutions to enhance decision-making, comply with regulatory requirements, and protect sensitive information. As the private credit market continues to grow, the importance of establishing clear data standards, ownership, and quality control processes becomes paramount. Lenders must invest in data infrastructure that ensures consistency across origination, servicing, and monitoring functions to create a single source of truth for portfolio analysis.
Key Takeaways: Data quality and governance form the foundation of effective loan management and are critical for addressing the non-accrual surge. McKinsey's research highlights the need for comprehensive data governance frameworks that include clear standards, ownership, integration architecture, validation processes, and regulatory alignment. Case studies demonstrate that implementing robust data management systems can significantly improve efficiency and risk management capabilities, with one bank achieving a 75% reduction in data re-keying and 100% reduction in manual reporting.
The complexity of today's lending environment demands sophisticated cross-portfolio risk assessment capabilities. The diversification of lending sources across 47 countries introduces additional complexity to risk assessment, as different lenders may have varying underwriting standards and risk appetites [4]. This fragmentation makes it essential for loan management systems to incorporate cross-portfolio analysis capabilities that can identify sector-specific vulnerabilities and concentration risks that might contribute to the non-accrual surge.
The Bank of England's Financial Stability Report noted that private equity-backed companies account for around 15% of UK corporate debt, with high concentrations in sectors such as finance, insurance, professional services, and information and communications [9]. This concentration poses a risk, as shocks in these sectors could have amplified effects on the private credit market.
The integration of data and analytics has become crucial in private credit platforms. Advanced analytics, AI, and machine learning are being employed to assess risks across various factors, including borrower performance and macroeconomic conditions [16]. This technological evolution enables more sophisticated cross-portfolio analysis than was previously possible, allowing lenders to identify sector-specific vulnerabilities before they manifest as non-accruals.
A practical application of this approach can be seen in the case of Versana, which collaborated with EY to develop a digital platform that digitally captures agent banks' deal data in near-real-time. This platform provides increased transparency and efficiency to the market, enabling participants to identify risks across their portfolios more effectively [17]. Since its launch in December 2022, the platform has helped market participants increase operational efficiencies and reduce costs associated with legacy analog processes, while also enhancing their ability to monitor portfolio risks.
Additionally, a significant portion of market-based finance debt for private equity-backed firms comes from riskier sources, such as leveraged loans and high-yield bonds. This reliance on higher-risk financing increases the potential for defaults, especially in a higher interest rate environment [9]. Understanding these sector-specific vulnerabilities is crucial for lenders and investors to assess risk accurately and develop strategies to mitigate potential losses.
Effective cross-portfolio analysis requires both technological capabilities and analytical frameworks that account for correlations between different risk factors. For example, a downturn in the commercial property sector might impact not only direct property loans but also loans to businesses that depend on commercial real estate, such as retail or hospitality companies. By understanding these interconnections, lenders can develop more nuanced risk mitigation strategies.
Key Takeaways: Cross-portfolio risk assessment is essential in today's complex lending environment, where debt capital comes from 47 different countries and private equity-backed companies account for 15% of UK corporate debt. Advanced analytics, AI, and machine learning enable lenders to identify sector-specific vulnerabilities before they manifest as non-accruals. Understanding interconnections between different risk factors allows for more nuanced risk mitigation strategies that can help address the non-accrual surge.
Fraud continues to be the most common crime in the UK, with methods constantly evolving according to UK Finance's annual fraud report. Ben Donaldson, managing director of economic crime for UK Finance, calls for a system-wide approach to countering fraud, rather than tactical interventions [5]. This persistent threat likely contributes significantly to non-accruals, particularly in private credit markets where due diligence may be less standardised.
"2025 is the year of adaptive fraud prevention," according to UK Finance, which emphasizes the need for system-wide, rather than tactical, anti-fraud strategies as fraud remains the UK's most common crime [5].
This approach is particularly relevant for private credit lenders, who must implement comprehensive, multi-layered defense mechanisms to protect their portfolios from increasingly sophisticated fraud schemes. The call for a system-wide approach aligns with the need for loan management systems to incorporate comprehensive fraud prevention capabilities that adapt to evolving threats, rather than relying on isolated tactical measures.
The UK's Financial Conduct Authority (FCA) has recognized the potential of AI in combating financial fraud, giving banks approval to test AI applications in a "supercharged sandbox" environment. This initiative aims to encourage innovation in fraud detection and prevention, with specific focus on authorized push payment fraud and market manipulation [25]. This regulatory support provides UK lenders with an opportunity to develop and implement advanced fraud prevention systems that can adapt to evolving threats.
Beyond the financial sector, other UK authorities are also investing in AI for fraud detection. For example, Cheshire Police recently received a £300,000 grant to invest in artificial intelligence to identify complex stalking behaviours at an early stage. The AI will be trained using information provided by the force's HRU and the Suzy Lamplugh Trust to identify stalking behaviours regardless of whether the word "stalking" is mentioned [27]. This approach demonstrates how AI can be used to detect patterns of concerning behaviour that might not be immediately obvious to human analysts, a capability that could be valuable for financial fraud detection as well.
Specific AI-powered fraud detection tools gaining traction in the industry include:
Companies like Feedzai have launched products that enable network fraud intelligence without compromising customer privacy, leveraging the power of AI through access to high-quality data [5]. These technologies represent the cutting edge of fraud prevention in lending, helping institutions identify and mitigate fraud-related risks before they result in non-accruals.
Modern fraud prevention must move beyond static rules and checklists to employ dynamic, AI-powered systems that can detect unusual patterns and emerging fraud techniques in real-time. In the next three years, the persistence and evolution of fraud schemes will intensify risk for B2B lenders—particularly as digital origination and portfolio diversification grow. Lenders with outdated, siloed fraud controls will face rising non-accruals linked to economic crime, while those with adaptive, system-wide fraud prevention will gain material portfolio resilience.
Key Takeaways: Fraud remains the most common crime in the UK and contributes significantly to non-accruals in private credit portfolios. The FCA's "supercharged sandbox" initiative provides regulatory support for AI innovation in fraud detection and prevention. Adaptive, system-wide fraud prevention strategies that leverage AI technologies such as behavioral biometrics, network analysis, and document verification are essential for protecting portfolios from emerging threats and reducing non-accruals linked to economic crime.
While preventing new non-accruals is crucial, lenders must also develop effective strategies for managing existing troubled loans. The UK's short-term lending sector is expected to grow by £12.2 billion in 2025, with the total outstanding loan book in the sector surpassing £10 billion for the first time ever [6]. This significant growth, particularly in a high interest rate environment, suggests that borrowers are increasingly turning to alternative financing options that may carry higher risk profiles.
Different loan types require tailored restructuring approaches to maximize recovery and minimize losses:
For consumer loans specifically, UK lenders are developing more sophisticated restructuring strategies that account for the unique characteristics of different borrower segments. For later life lending, which saw a 7.6% increase in Q1 2025, lenders are implementing age-specific modification programs that consider retirement income, life expectancy, and healthcare costs when restructuring troubled loans [7]. These programs often include:
UK Finance has recently emphasized the importance of supporting vulnerable customers, particularly in the context of green home upgrades. Their report urges for a coordinated strategy to increase demand for energy-efficient home improvements, highlighting the need for "green mortgage" models where energy-efficient upgrades are bundled into mortgage products, reducing financial strain and encouraging uptake [28]. This approach could be adapted for restructuring troubled loans, particularly for borrowers who are struggling due to high energy costs or other sustainability-related challenges.
Common loan restructuring strategies include:
A case study from a major corporate credit union demonstrates the impact of external expertise in managing troubled assets. The credit union engaged NewOak to provide scenario modeling for their student loan asset-backed securities portfolio under base, pessimistic, and optimistic cases, along with monthly valuation, stress testing, and risk analytics. This approach enabled them to effectively assess performance and develop corresponding findings, resulting in a significant improvement in their ability to manage troubled loans [17].
Key performance metrics for evaluating restructuring success include:
Implementing these strategies requires careful consideration of the borrower's financial health, market conditions, and regulatory implications. Lenders must balance the need to recover funds with the potential benefits of supporting borrowers through restructuring, aiming to achieve mutually beneficial outcomes that maximise recovery while maintaining relationships.
Key Takeaways: Effective restructuring of existing non-accruals requires tailored approaches for different loan types. For consumer loans, particularly in the growing later life lending segment, UK lenders are implementing age-specific modification programs that consider retirement income and healthcare costs. UK Finance's emphasis on "green mortgage" models offers insights for restructuring loans for borrowers facing sustainability-related challenges. Key restructuring strategies include Amend and Extend, Debt-for-Equity Swaps, and Payment-in-Kind arrangements, with success measured through metrics such as Return to Performing Status Rate and Modification Sustainability.
The regulatory environment for private credit continues to evolve, with implications for how lenders manage non-performing loans. UK Finance reported that residential loans to older borrowers increased by 7.6% in Q1 2025, while buy-to-let lending in the same demographic accounted for 21.5% of total BTL loans [7]. This growth in later life lending introduces additional regulatory considerations, as these borrowers may be considered vulnerable customers under UK financial regulations.
The UK government has recently published its first consultation on reform of the Consumer Credit Act 1974, a 50-year-old legislation that has been anticipated and hoped for by the industry. The proposal includes a focus on information requirements and sanctions, but also suggests that reliance on the Consumer Duty and FCA supervision and enforcement might change this [29]. This reform could have significant implications for how lenders manage non-performing loans, particularly in the consumer credit space.
Recent regulatory developments include new reporting requirements for bank loans and commitments to nonbank financial entities, finalized by U.S. federal banking regulators in May 2024. These changes aim to improve the banking system's understanding and supervision of credit concentrations and risks, affecting institutions with over $10 billion in total assets [18]. Similar regulatory trends are emerging in the UK market, requiring lenders to adapt their compliance frameworks.
The Financial Stability Report from November 2024 further emphasized the interconnectedness of private credit markets with leveraged loans and high-yield bonds. It noted that shocks to highly indebted corporates could spill over to these markets, potentially leading to correlated stresses and increased default rates [8]. This interconnectedness creates additional regulatory challenges, as authorities seek to monitor and mitigate systemic risks.
This trend, combined with the rising non-accrual rates, suggests that loan management systems must incorporate sophisticated regulatory compliance capabilities that can adapt to evolving standards while efficiently managing troubled loans across diverse borrower segments. Automated compliance features can reduce regulatory risk while improving non-accrual management, ensuring that restructuring efforts comply with current regulations.
Effective regulatory compliance requires not only adherence to current rules but also the ability to anticipate and adapt to future regulatory changes. Loan management systems should provide flexible frameworks that can be updated as regulations evolve, ensuring that lenders remain compliant without disrupting their operations.
Key Takeaways: The regulatory environment for private credit is evolving, with the UK government's consultation on reform of the Consumer Credit Act 1974 potentially having significant implications for non-performing loan management. The growth in later life lending introduces additional regulatory considerations related to vulnerable customers. Loan management systems must incorporate sophisticated compliance capabilities that can adapt to evolving standards, with automated features that reduce regulatory risk while improving non-accrual management.
The 30% surge in non-accruals across UK private credit portfolios represents a significant challenge that requires a comprehensive response. Lenders must implement integrated loan management systems that combine AI-powered early warning capabilities, robust data governance, cross-portfolio risk assessment, adaptive fraud prevention, effective restructuring tools, and automated regulatory compliance.
The adoption of microservices architecture is gaining traction as lending businesses transition to integrative microservices to digitize operations at their own pace. Cloud-based microservices allow companies to add services as modules, facilitating adaptation to regulatory changes and enhancing operational efficiency [19]. This approach enables lenders to build flexible, adaptable systems that can evolve with changing market conditions and regulatory requirements.
Investors are increasingly emphasizing asset-based finance within private credit, applying a relative value lens to avoid value traps and focusing on sectors like music royalties and synthetic risk transfers, while approaching emerging consumer loan products with caution [20]. This shift in investment strategy requires loan management systems that can accommodate diverse asset classes and risk profiles.
Kennek's end-to-end lending management platform offers financial institutions the tools they need to address these challenges effectively. By providing a single system of record for all lending activities, Kennek enables lenders to maintain data integrity, implement sophisticated risk monitoring, and streamline restructuring processes when loans become troubled.
As the private credit market continues to grow and evolve, lenders that invest in comprehensive loan management capabilities will be better positioned to navigate the challenges of rising non-accruals. By taking a proactive, data-driven approach to portfolio management, these institutions can minimise losses, maintain regulatory compliance, and emerge stronger from the current market turbulence.
The path forward requires not only technological investment but also a cultural shift toward more proactive risk management. By embracing advanced loan management systems and developing the analytical capabilities to leverage them effectively, UK lenders can turn the current non-accrual crisis into an opportunity to strengthen their operations and build more resilient portfolios for the future.
The current surge in non-accruals across UK private credit portfolios is not merely a cyclical challenge; it underscores fundamental weaknesses inherent in outdated lending infrastructure. We view the 30% rate as a clear signal that reliance on siloed data, manual processes, and reactive risk management is no longer sustainable. Effective portfolio resilience begins with establishing a single source of truth for all lending data. Without this foundational data integrity, even the most advanced analytical tools are rendered ineffective, leaving lenders exposed to preventable losses and operational inefficiencies that compound risk during periods of market stress.
Addressing this complexity demands an integrated, technology-driven approach. We maintain that lenders must adopt platforms that unify loan origination, servicing, monitoring, and reporting. This enables the proactive identification of risk through sophisticated analytics, provides essential cross-portfolio visibility to manage concentration risks, and facilitates adaptive strategies for fraud prevention and effective restructuring. Building robust, compliant portfolios in today's volatile market requires moving decisively towards intelligent, connected systems that provide the necessary control and insight to navigate challenges and secure future growth.
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 and acquiring 32,000 retail clients. He also co-founded Fintech Belgium, serving as president to foster fintech growth. Most recently, he led Strategic Innovation & Marketing at the private bank, Degroof Petercam, driving impactful digital transformations.
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[3] The evolving role of credit portfolio management, McKinsey
[4] Debt for property from record number of countries, Daily Business
[5] 2025 is the year of adaptive fraud prevention, say experts, Biometric Update
[6] Bridging Finance Set For £12.2bn Surge – Why Demand Keeps Rising Across The UK, Business Manchester
[7] Later life lending rises sharply in Q1 2025: UK Finance, Mortgage Professional America
[8] Financial Stability Report - November 2024, Bank of England
[9] Financial Stability Report - June 2024, Bank of England
[10] Keynote address at the DealCatalyst AFME European Direct Lending, Bank of England
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[12] 2025 Private Credit Outlook: 5 Key Trends, Wellington Management
[13] Quantum Deep Learning for Credit Risk Assessment in Banking, arxiv.org
[14] Machine Learning Workflow to Enhance Credit Default Prediction, arxiv.org
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[17] Case Studies, NewOak
[18] United States Alternative Lending Market Business Report 2024-2029, BusinessWire
[19] Lending Business Technology Trends, LeadSquared
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[22] Private credit stress showing as troubled loans increase, Financial Times
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[26] 94% of advisers expect AI to be positive for the industry, Financial Reporter
[27] AI to identify complex stalking behaviours at an early stage, Today's Family Lawyer
[28] UK Finance Urges Action To Boost Demand For Green Home Upgrades, Crowdfund Insider
[29] Unpacking the future: navigating the reform of the Consumer Credit Act, Dentons