In a competitive SME finance market, UK challenger bank Allica Bank has made a strategic move to enhance its lending infrastructure. The bank recently announced a partnership with commercial lending technology provider Lendscape to strengthen its asset finance operations [1]. This collaboration aims to streamline operations, accelerate integration with broader systems, and enable swifter responses to market demands—providing valuable insights for other SME lenders seeking to modernise their technology stacks.
As traditional banks continue to retreat from certain lending areas due to tighter regulations, alternative lenders and challenger banks have significant opportunities to fill these gaps. The private credit industry has expanded dramatically to approximately $2 trillion, up from $500 million a decade ago, driven largely by companies turning to private credit firms for financing due to flexible and reliable loan terms. This growth has been particularly pronounced amid market volatility caused by fluctuating tariff policies, which has pushed businesses to seek more adaptable financing solutions [7]. This substantial growth creates opportunities for agile lenders who can offer adaptable financing solutions. However, capitalising on these opportunities requires robust, flexible technology infrastructure that can support efficient lending operations at scale. Allica Bank's approach offers a compelling blueprint for other institutions looking to enhance their SME lending capabilities.
At the core of Allica Bank's technology selection is Lendscape's API-first platform, designed to support agility and continuous innovation [1]. This architectural approach represents a fundamental shift in how challenger banks approach system integration, enabling lenders to create modular, flexible systems that can be rapidly reconfigured to address emerging market opportunities or regulatory changes without complete platform overhauls.
For SME lenders, API-first architecture delivers several critical advantages. It facilitates seamless integration with broader banking ecosystems, including credit bureaus, open banking providers, and accounting platforms. This interconnectivity enables lenders to access richer data sets for credit decisions while creating more streamlined customer experiences. Additionally, API-first platforms allow for incremental system enhancements rather than disruptive replacements, reducing technical debt and implementation risks.
Lendscape's platform specifically benefits Allica Bank through its ability to integrate with the bank's existing systems while providing the flexibility to adapt to changing market conditions. As Niv Subramanian, CEO of Allica Bank, noted, the bank selected Lendscape for "its openness and clarity, and the flexibility and speed of its platform for evolving its ecosystem" [1]. This highlights how API-first architecture enables Allica Bank to maintain a competitive edge by quickly adapting to market changes and customer needs without being constrained by rigid technology infrastructure.
Recent industry data reinforces the importance of this approach. A 2024 report indicates that AI can reduce loan approval times by 25%, with some banks processing applications in under 24 hours [17]. Additionally, AI-driven credit scoring models improve accuracy by 30% compared to traditional methods [17]. These improvements are only possible with flexible, API-first architectures that can integrate advanced technologies seamlessly.
A significant trend in SME lending is the shift from traditional collateral-based models to more sophisticated cash flow-based assessment approaches. This evolution requires robust data processing capabilities and integrated risk assessment frameworks that modern loan management platforms can provide.
The National Credit Guarantee Institution (NCGI) model demonstrates how lenders can extend credit by focusing on the viability and cash flow of SMEs rather than hard collateral [2]. This approach represents a significant departure from traditional models that heavily relied on physical collateral, which often excluded promising businesses with strong cash flows but limited assets. As Deputy Finance Minister Dr. Harshana Suriyapperuma emphasized, banks need to "assess business cash flow and viability, not just collateral, and adopt financing models that empower innovation and reduce risk premiums for aspiring entrepreneurs" [2].
Traditional collateral-based lending models have historically restricted SME financing opportunities due to inadequate technology for assessing cash flow viability and alternative credit metrics. Advanced loan management systems with sophisticated data analytics capabilities are essential to operationalise cash flow-based lending, enabling lenders to accurately assess business viability through automated financial analysis.
Cloud-based loan management platforms are enabling this transformation by providing seamless integration with existing banking ecosystems. These platforms integrate with CRM systems, accounting software, and third-party APIs, enhancing customer relationship management and financial reporting [18]. This integration capability allows lenders to access comprehensive data sets necessary for accurate cash flow assessment.
Modern lending platforms facilitate this transition by integrating alternative data sources that provide deeper insights into business performance. These include:
By leveraging these alternative data sources, lenders can develop more accurate risk profiles for SMEs without relying solely on traditional collateral valuations, expanding access to financing for viable businesses that may lack substantial physical assets.
One of the most instructive aspects of Allica Bank's approach is its methodical implementation strategy. As part of the collaboration, Lendscape provided Allica with early sandbox access to test the platform's integration capabilities, performance, and adaptability from the outset [1]. This strategy allows lenders to validate technical compatibility, identify integration challenges, and refine workflows before committing to full-scale deployment.
The sandbox testing approach substantially reduces the operational risks associated with core system migrations—a critical consideration given that financial institutions often face high implementation failure rates of new lending systems due to insufficient testing environments and integration validation prior to deployment. By thoroughly testing integrations and workflows in a controlled environment, lenders can identify potential issues early, refine requirements, and ensure that the system will deliver expected benefits before full deployment.
For SME lenders considering loan management system upgrades, this methodical approach provides a valuable blueprint, demonstrating how proper testing methodologies can significantly enhance implementation success rates and accelerate time-to-value. Allica's early sandbox access allowed them to validate not just technical compatibility but also assess how the platform would perform under various operational scenarios, ensuring a smoother transition when moving to full implementation [1].
Real-world implementations demonstrate the value of this approach. As the CEO of Gaia, a UK-based lender dedicated to making IVF accessible, noted: "LendFusion made the transition effortless, and gave us the scalability and automation we needed to grow without increasing our team size" [10]. This experience highlights how proper implementation methodologies can lead to seamless transitions and immediate operational benefits.
"The future of finance demands tangible results, not just flashy demos, according to attendees" at Money 20/20 Europe, particularly in discussions about embedded finance for SMEs and AI integration [15].
Artificial intelligence capabilities within modern loan management systems are revolutionising credit risk assessment for SME lenders. These technologies enable more accurate borrower evaluations, real-time risk monitoring, and early warning systems that can significantly improve lending decisions and portfolio performance.
Banks are increasingly leveraging AI to revolutionise credit risk management, enabling accurate evaluation of borrowers' credit status, timely identification of potential risks, and comprehensive decision support [3]. These capabilities are particularly valuable for evaluating smaller businesses with limited credit histories, allowing lenders to identify promising opportunities that might be overlooked by traditional scoring methods.
Financial institutions are significantly investing in AI to enhance risk management processes. A 2024 report states that 85% of financial services institutions plan to increase their AI and machine learning investments in cybersecurity, with AI-enabled risk management systems capable of reducing operational risks by 50% [19]. This substantial investment reflects the growing recognition of AI's potential to transform risk management practices.
The integration of AI into loan management systems represents a transformative development for SME lenders, enabling them to process vast quantities of structured and unstructured data to generate more accurate risk assessments. Recent advancements in AI for credit underwriting are particularly promising. AI is transforming private credit by enabling lenders to analyze financial data more efficiently, allowing for increased deal volume without additional staffing [8].
Research has also introduced reinforcement learning frameworks for credit underwriting, aiming to improve decision-making processes in credit scoring and underwriting [9]. These models have shown potential in outperforming traditional methods under certain conditions, offering lenders new tools to enhance their risk assessment capabilities.
For challenger banks with ambitious growth targets in the SME lending space, selecting a loan management system with the right capabilities is essential. The British Business Bank's achievement of £5 billion in lending through its structured guarantee programmes demonstrates the substantial market opportunity for efficient lending operations [4]. However, achieving this scale requires loan management systems capable of processing high volumes of applications, managing complex guarantee structures, and maintaining compliance across diverse lending products.
Recent data from the British Business Bank reveals that challenger and specialist banks have steadily increased their share of SME lending, accounting for 60% of gross lending in 2024, up from 59% in 2023 [14]. In 2024, these institutions provided £37.3 billion of the £62.1 billion gross lending to smaller businesses, outperforming the UK's big five banks for the fourth consecutive year [14]. This trend underscores the growing importance of efficient loan management systems for these market leaders.
A common challenge for financial institutions is the inability to scale lending operations efficiently as portfolio growth outpaces operational capacity, creating processing bottlenecks and increased costs. Many lenders find themselves adding headcount linearly as their portfolio grows, which is unsustainable in the long term.
Key system requirements for scaling lending operations include:
The experience of Planet42, a car subscription company in South Africa, illustrates the importance of these capabilities. By implementing an automated loan management system, they were able to "expand without being held back by manual processes" [10], managing their growing customer base efficiently without adding administrative staff. Within 8 months of implementation, they achieved a 45% reduction in manual processing time and were able to scale their loan book by 65% with only a 15% increase in operational staff [10].
The dynamic nature of the UK's financial regulatory environment requires loan management systems with configurable compliance frameworks that can rapidly adapt to new requirements. The Financial Conduct Authority's recent consultation on potential reforms to simplify mortgage issuance and advisory rules highlights this ongoing evolution [5].
Many financial institutions struggle with compliance frameworks that lack flexibility to adapt to evolving regulatory requirements, creating significant operational burdens and compliance risks. For SME lenders, this environment necessitates systems with flexible compliance frameworks that can be reconfigured without extensive redevelopment. Systems lacking this flexibility often create significant operational burdens as lenders struggle to implement regulatory changes across fragmented technology stacks.
Recent regulatory developments illustrate this challenge. The European Securities and Markets Authority (ESMA) has published final reports on Regulatory Technical Standards and Guidelines on Liquidity Management Tools, which were significantly amended by the Directive 2011/61/EU on Alternative Investment Fund Managers review [20]. These changes provide greater flexibility in the design of activation limits for redemption restrictions and eliminate the requirement for Liquidity Management Tools to be applied uniformly across all share classes.
At EBAday, a major event for the banking and financial community, key discussions focused on the dual demands facing the industry: innovation and regulation. Chris Skinner, a commentator on financial markets, highlighted that "while banks are eager to keep pace with technological advancements, their enthusiasm is often tempered by stringent regulatory requirements" [21]. This sentiment was also expressed among industry leaders who voiced concerns about potential regulatory hurdles that could slow the adoption of new technologies.
Allica Bank's selection of a modern loan management system demonstrates recognition that regulatory adaptability is a critical requirement for SME lenders, providing a blueprint for how challenger banks can future-proof their operations against evolving compliance requirements.
A significant challenge for financial institutions is difficulty quantifying ROI on technology investments due to inadequate measurement frameworks for both operational efficiencies and strategic benefits. To address this challenge, lenders should establish comprehensive metrics that capture both immediate operational improvements and longer-term strategic advantages.
Operational efficiency metrics should include:
Strategic benefit metrics should focus on:
Case studies demonstrate significant ROI from loan management system implementations. Provira, a financial provider assisting beneficiaries in accessing inheritance funds, implemented an automated loan management system that streamlined operations and enabled them to provide faster service to clients [10]. The company reduced manual administrative work by approximately 60%, leading to 40% quicker loan processing times and a 25% increase in client satisfaction ratings [10].
The first day of Money 20/20 Europe in Amsterdam highlighted this growing emphasis on measurable results rather than flashy demos, particularly in discussions about embedded finance for SMEs and the integration of Artificial Intelligence [15]. As traditional lending models become increasingly misaligned with the needs of modern businesses, particularly online sellers, the future of finance demands tangible results from technology investments.
As the UK Finance forecast of an 11% increase in gross mortgage lending for 2025 indicates [5], significant growth opportunities exist for lenders with efficient operations. Those with modern loan management systems can capture disproportionate market share by processing higher application volumes without corresponding increases in operational costs.
For SME lenders considering technology upgrades, kennek's end-to-end lending platform offers a comprehensive solution that addresses many of the challenges highlighted in Allica Bank's approach. By providing a unified system for the entire lending lifecycle, kennek enables lenders to streamline operations, enhance risk management, and scale efficiently without the integration complexities of fragmented systems.
The kennek platform's configurable workflows and real-time monitoring capabilities support both collateral and cash flow-based lending models, while its API-first architecture ensures seamless integration with existing systems and data sources. This comprehensive approach allows lenders to modernise their operations incrementally, reducing implementation risks while delivering immediate operational benefits.
Allica Bank's technology upgrade provides a valuable blueprint for other SME lenders seeking to enhance their lending infrastructure. By focusing on API-first architecture, enabling cash flow-based lending models, employing thorough testing methodologies, and selecting systems with the right capabilities for growth and regulatory adaptation, lenders can position themselves for success in the competitive SME finance market.
We observe a clear imperative for SME lenders: the need to move beyond fragmented legacy systems towards integrated, intelligent infrastructure. The market demands agility, driven by the growth in private credit and the shift towards challenger banks. Achieving this requires not just adopting individual technologies like API-first architecture or AI, but fundamentally streamlining the entire lending lifecycle. We believe that relying on disparate point solutions creates unnecessary complexity, hinders scalability, and ultimately limits a lender's ability to efficiently assess risk, manage portfolios, and adapt to regulatory changes. True efficiency and competitive advantage come from a unified platform that simplifies operations from origination through to maturity.
Our perspective is grounded in the conviction that making lenders' lives simpler is paramount. We provide the end-to-end platform necessary to operationalise sophisticated approaches like cash flow-based lending and AI-driven risk assessment without the typical integration burden. We know that robust testing is vital, but our integrated approach inherently reduces many common implementation risks. By consolidating workflows and data sources, we enable lenders to scale operations efficiently, enhance compliance, and gain the measurable ROI essential for growth. This is not merely a technological upgrade; it is a strategic shift towards a more intelligent, efficient, and profitable future for B2B lending.
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 expertise in digital transformation and fintech innovation provides valuable insights into modern lending infrastructure challenges and solutions.
[1] Allica Bank selects Lendscape to strengthen SME lending infrastructure, IBS Intelligence
[2] New credit guarantee will break collateral barriers for local SMEs: ADB, Daily Mirror
[4] British Business Bank reaches £5bn lending milestone - £401m provided across the South West, Insider Media
[5] UK financial watchdog consults on steps to simplify mortgage rules, Reuters
[6] 2025 Private Credit Outlook: 5 Key Trends, Wellington Management
[7] Companies turn to private credit during tariff turmoil for loans, Reuters
[8] AI-Powered Private Credit Will Shape The Future Of Lending, Forbes
[10] Case Studies: Loan Management Software, LendFusion
[11] Goldman Sachs looks to expand private equity credit lines as dealmaking picks up, Reuters
[12] NAV lending, Wikipedia
[13] The $1tn shadow bank lending boom, Financial Times
[14] Challenger and specialist bank lending hits record high overall proportion smaller businesses, British Business Bank
[15] Embedded finance & AI: Day 1 at Money 20/20 sets demand for measurable ROI, bobsguide.com
[16] Importance of Compliance Management in times of transition, JD Supra
[17] AI in Banking Statistics, SEO Sandwich
[19] AI Use in Financial Services Statistics, WorldMetrics
[21] Chris Skinner: banks prioritize innovation amid regulatory concerns, Traders Union