The future of secured lending in the UK and Europe: From bottlenecks to AI-driven growth

2026-04-28 · Source: Thoughtworks Insights · Field: Finance & Economics — Banking & Financial Services, FinTech & Digital Financial Services · Depth: Intermediate, short

Summary

The UK and European secured lending industries face significant "process bottlenecks" despite modern digital front-ends, stemming from fragmented backend infrastructure, manual processes, approval delays, data fragmentation, and reporting burdens. Key challenges include orchestrating third parties, reliance on PDF uploads instead of Open Banking/PSD2/payroll APIs, and poorly organized collateral data leading to conservative underwriting. The sector is undergoing transformation driven by seven trends: tighter capital and regulation (e.g., Basel 3.1, CRR3, 72.5% output floor effective Jan 1, 2025, and UK PRA rebasing Pillar 2 by March 2026), AI-powered underwriting (with the EU AI Act classifying creditworthiness evaluation as "high-risk" by August 2026, contrasting with the UK's existing framework approach), growth of embedded and platform lending (UK saw a 38% increase in embedded lending API usage in 2024), efficiency focus in mortgages, auto lenders balancing affordability and EV risk, expansion of SME credit via Open Banking, and productizing the collateral lifecycle into unified data products.

Key takeaway

For Directors of AI/ML or VPs of Engineering navigating secured lending modernization, you must prioritize investments in high-demand areas like B2B embedded lending and data-driven mortgage origination. Establish robust AI model governance frameworks that comply with both the EU AI Act and UK FCA Consumer Duty. Your teams should build reusable data products for each asset class to ensure real-time exposure data for IFRS 9, Basel 3.1/CRR3 capital provisioning, and cross-border reporting.

Key insights

Secured lending in the UK/EU is bottlenecked by fragmented data and manual processes, necessitating AI and data product strategies amidst evolving regulations.

Principles

• AI in lending requires strict governance.
• Data fragmentation hinders underwriting.
• Unified data products improve reporting.

Method

Lenders should invest in high-demand domains like B2B embedded lending and data-driven mortgage origination, establish AI model governance, and build reusable data products for each asset class.

In practice

• Use Open Banking for real-time cash-flow analysis.
• Adopt e-closings and digital identity verification.
• Integrate with land registries via APIs.

Topics

• Secured Lending
• AI Underwriting
• Regulatory Compliance
• Data Products
• Open Banking
• Embedded Lending

Best for: CTO, Executive, AI Product Manager, Director of AI/ML, VP of Engineering/Data, Legal Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by Thoughtworks Insights.