UK banks are confronting a concerning trend as non-performing asset (NPA) provisions increased by 8.3% sequentially to ₹25,242.9 crore in the March 2025 quarter, marking a 9.2% year-on-year rise. This represents the second consecutive quarter of increases, signalling a persistent deterioration in loan quality that demands immediate attention from financial institutions [1].
The surge in provisions is primarily driven by stress in microfinance portfolios, with 22 out of 29 banks in the sample reporting increased loan loss provisions—the highest number in at least 13 quarters. This widespread pattern indicates a systemic issue rather than isolated cases, suggesting a broader deterioration in asset quality across the sector [1].
Industry analysts at IDBI Capital project that this microfinance stress is likely to persist for several quarters, potentially resulting in fresh slippages that could further strain bank balance sheets. Similarly, Motilal Oswal Financial Services expects credit costs to remain elevated through the first half of the current fiscal year, with possible moderation only in the second half [1].
The current NPA surge reflects broader economic challenges affecting borrowers' ability to maintain loan repayments. The NPA provisioning has gradually increased after hitting a low of ₹18,169.5 crore in the 2023 quarter but remains below the level of ₹30,000 crore seen three years ago [1].
As UK banks grapple with rising NPA provisions, predictive analytics has emerged as a critical tool for early identification of at-risk accounts. Recent data indicates that UK banks' stage 3 (credit-impaired) loans stood at 2.03% of total systemwide loans at half-year 2024, with non-performance across retail and wholesale lending books increasing slightly compared with end-2023 [2].
"AI is transforming private credit by enabling lenders to analyze financial data more deeply and efficiently," according to industry analysis. "This technological adoption allows for increased origination and underwriting of private credit opportunities without expanding resources, potentially reducing loss ratios through more accurate credit models" [3].
A compelling case study demonstrates the power of predictive analytics in action. Santander implemented AI-driven predictive models to analyze historical data and real-time account behaviors, enabling the bank to identify at-risk customers before defaults occurred. This approach led to a decrease in loan defaults and improved financial health for both the bank and its customers [4].
Real-time data integration has proven particularly effective for credit risk management. A U.S.-based commercial lender implemented an AI/ML-based data analytics solution that processed real-time data to predict customer delinquency with 93% accuracy. This system analyzed various data points, including transaction histories and payment behaviors, enabling the lender to proactively identify high-risk customers and implement targeted intervention strategies, resulting in a significant reduction in loan defaults [5].
For UK microfinance portfolios specifically, early warning indicators include changes in payment frequency, increases in payment delays, shifts in transaction behavior, and correlations with specific economic indicators affecting borrower segments. Employment stability metrics are particularly crucial, given the challenging economic environment facing many borrowers.
Continuous monitoring of loan portfolios through automation represents another powerful strategy for addressing the NPA challenge. Traditional monitoring approaches often rely on periodic reviews that may miss early signs of deterioration, whereas automated systems provide real-time assessment of portfolio health.
Financial institutions are currently wasting approximately 25% of their technology budgets on inefficient testing and quality assurance practices [6]. By redirecting these resources toward automated loan monitoring systems, banks can establish continuous portfolio assessment protocols that identify deteriorating loans much earlier in their lifecycle.
Over 70% of financial service providers have accelerated digital transformation initiatives to stay competitive, but this focus often overlooks quality [6]. The most successful financial institutions deploy strategic automation frameworks aligned with their business objectives, delivering faster releases with fewer defects, lower costs, and reduced production defects [6].
Automated loan monitoring enables more efficient resource allocation, allowing institutions to focus their attention on high-risk accounts while maintaining appropriate oversight of the entire portfolio. These systems can track regulatory changes in real-time, automatically adjusting monitoring parameters to maintain compliance while providing auditable documentation trails.
While early detection and monitoring are essential, preventing high-risk loans from entering the portfolio in the first place represents the most effective strategy for reducing NPA provisions. Advanced loan management software strengthens the underwriting process through data-driven decision making, incorporating diverse data points and sophisticated analytics.
The financial sector has recognised the importance of enhanced underwriting capabilities, with over 400 lending institutions currently relying on API-driven risk assessment tools to assess risk, automate collections and significantly advance financial inclusion [7]. These technologies enable more comprehensive risk evaluation at the origination stage, serving as a critical first line of defence against future NPAs.
Enhanced underwriting frameworks incorporate multiple data sources beyond traditional credit scores, including open banking data, transaction patterns, and behavioral analytics. This comprehensive approach enables lenders to make more informed decisions about loan approvals, reducing the likelihood of defaults and subsequent provisions.
By implementing robust underwriting frameworks, financial institutions can maintain healthier loan books from the outset, reducing their exposure to the microfinance stress that's driving the current surge in provisions. This approach not only minimises the risk of future NPAs but also enhances overall portfolio quality and stability.
For loans that do show signs of stress, AI-driven collection strategies offer a powerful means of improving recovery rates and reducing the need for provisions. Traditional collection approaches often apply standardised methods across all delinquent accounts, whereas AI-powered systems can create personalised intervention strategies based on borrower profiles and behaviour patterns.
With microfinance stress projected to continue for several quarters [1], financial institutions need sophisticated collection strategies that go beyond traditional approaches. AI-driven collection modules can analyse borrower behaviour, payment patterns, and communication preferences to develop personalised intervention strategies that significantly improve recovery rates.
These systems determine optimal contact times, communication channels, and repayment plan structures tailored to individual borrower circumstances. By analyzing historical data, AI models can forecast the likelihood of repayment and suggest the most appropriate action to take for each specific case.
The social factors affecting borrowers must be considered when designing collection strategies. Financial stress often leads to isolation and mental health challenges, requiring more compassionate and effective intervention approaches that address underlying issues rather than simply pursuing repayment.
As regulatory frameworks evolve to address AI integration in financial services, compliance requirements for loan loss provisioning are becoming increasingly complex. Advanced loan management software can automate regulatory reporting processes, ensuring consistent application of provisioning rules across portfolios.
These systems track regulatory changes in real-time, automatically adjusting provisioning calculations to maintain compliance while providing auditable documentation trails that satisfy regulatory scrutiny. By streamlining compliance and reporting processes, financial institutions can reduce administrative burden while improving the accuracy of provision calculations.
The approach not only enhances regulatory compliance but also provides greater transparency and confidence in the institution's risk management practices. Automated systems ensure that provisioning calculations are consistent, accurate, and fully documented for regulatory review.
Fragmented risk assessment systems prevent effective identification of concentration risks and emerging default patterns. Modern loan management software addresses this challenge by integrating with other financial systems to provide a comprehensive view of risk across diverse lending portfolios.
By connecting disparate financial systems and data sources, loan management software can create a holistic view of risk across entire lending portfolios. This comprehensive perspective enables financial institutions to identify concentration risks, correlation patterns, and emerging trends that might not be visible when examining individual loans or portfolios in isolation.
Integration capabilities enable institutions to develop more effective strategies for managing rising NPA provisions by providing a complete picture of portfolio health. This holistic approach supports better decision-making and more targeted risk management interventions.
The 8.3% sequential increase in NPA provisions facing UK banks represents a significant challenge that requires a comprehensive response. By implementing advanced loan management software that incorporates predictive analytics, automated monitoring, enhanced underwriting, AI-driven collections, streamlined compliance, and robust integration capabilities, financial institutions can effectively address the root causes of rising provisions.
How can financial institutions best prepare for the continued microfinance stress in the coming quarters? What specific technologies offer the most immediate impact for reducing NPA provisions?
As the microfinance stress continues to impact UK banks, those that adopt these technological solutions will be better positioned to identify potential defaults early, take proactive measures to prevent deterioration, and manage existing NPAs more effectively. The result will be stronger balance sheets, improved financial stability, and greater resilience in the face of economic uncertainty.
kennek's end-to-end lending management platform offers financial institutions the comprehensive capabilities needed to address these challenges. By providing real-time monitoring, automated workflows, and integrated risk management tools, kennek enables banks to maintain healthier loan books while reducing the administrative burden associated with managing NPAs.
In an environment where NPA provisions are projected to remain elevated for several quarters, investing in robust loan management software represents a strategic imperative for UK banks seeking to protect their balance sheets and ensure long-term financial stability. Financial institutions that embrace these technological solutions will not only weather the current storm but emerge stronger and more resilient in the face of future challenges.
The current pressure on loan portfolios, particularly evident in microfinance, underscores a fundamental truth: managing credit quality effectively requires a comprehensive, integrated approach. We maintain that relying on fragmented systems and manual workflows is insufficient in today's environment. To build resilience and ensure portfolio health, lenders must adopt platforms that provide real-time visibility and control across the entire lending lifecycle, from initial underwriting to ongoing monitoring and collections. This integrated perspective is essential for identifying and mitigating risk proactively.
We believe the most immediate and lasting impact on reducing NPA provisions comes from implementing integrated technology that enables proactive risk management. This means strengthening underwriting with diverse data, automating continuous portfolio monitoring for early detection, and applying intelligent strategies for collections. These capabilities are most effective when unified within a single system of record, eliminating data silos and providing the efficiency and precision necessary to navigate challenging market conditions and maintain robust financial stability.
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 both traditional banking and fintech innovation provides unique insights into the technological solutions needed to address modern lending challenges.
[1] Bad loan provisions increased in March qtr amid microfin stress, Economic Times
[2] U.K. Banks Are Well Positioned For Sustained Strong Performance After First-Half Results, S&P Global Ratings
[3] AI-Powered Private Credit Will Shape The Future Of Lending, Forbes
[4] AI in Banking [20 Case Studies], DigitalDefynd
[5] How Banks Use Real-Time Data to Improve Credit Risk and Minimize Defaults, Anaptyss
[6] Choosing the right automation tools for financial applications, Dev.to
[7] Remita and the future of Africa's digital economy: A Nigerian blueprint, BusinessDay