AI in Asset Finance: Practical wins now, human-in-the-loop, bigger impact ahead

Reimagining asset finance with AI: practical wins now, bigger shifts ahead

Published: 30 September 2025

Asset finance is early in its AI program. A live poll during the latest Asset Finance Connect webcast showed more than a quarter of delegates haven't started. Progress is uneven, with most traction in document processing and chatbots, and most friction in integration, data quality and skills.

What firms are doing today

Early wins are in low-risk, repeatable work. As Karan Oberoi (Solifi) put it: chatbots for quick support and document intelligence for faster approvals and leaner operations. External-facing bots are now viable thanks to stronger tooling and controls.

Haydock Finance moved from pilots to embedded operations. Christian Brough highlighted proposal loading and fraud detection, with a clear rule: keep humans in the loop. "The technology is all around supporting that, doing the heavy lifting. We're still 100 percent manually in control."

Liberty Leasing focused first on digitising workflows and connecting siloed systems. Ed Ockleford shared contained trials like automated bank statement analysis and internal audit checks. The principle is simple: assist, don't bypass.

Where the sector struggles

Audience polls confirmed the pattern: document processing leads, then credit and risk assessment. Integration and skills are the top barriers, with compliance close behind.

Oberoi's advice: fix the data foundation. Consolidate context across systems so models can do useful work. Richard Huston called this "context engineering" - connecting the right data to the right model at the right moment.

Standalone vs modular platforms

Haydock began with a standalone AI system to learn safely, then moved to embedded tools in proposal workflows and fraud detection, with manual fallbacks in place. Cautious, then confident.

Liberty prefers modular systems with shorter contracts, strong APIs and easy swap-out if value isn't there. Flexibility reduces lock-in as AI capabilities evolve.

ROI and value creation

Ignore the "zero ROI" headlines. Oberoi noted that value shows up over time if you define outcomes upfront. It's not instant; it is measurable.

Brough's team runs an AI Working Group with a live roadmap of 12-15 use cases. Each project moves forward only with a clear benefits case. That discipline prevents vanity pilots.

Ockleford added that softer wins matter: staff confidence, adoption, and seeing AI as assistive. Culture change is part of the return.

The next 2-4 years: what will change

• Operational cost: meaningful reductions as document processing, case preparation and internal audit get automated end-to-end.
• Decision speed: days compress to minutes across proposals, KYC, fraud and exceptions - with human approvals where needed.
• Customer experience: clearer answers, faster service, and proactive support informed by richer context.

Brough expects expectations to spike: customers won't wait, and headcount alone won't cover demand. Ockleford warned against overreach in sensitive moments like first collections calls - judgment and empathy still matter. Oberoi's frame is augmentation: equip teams with context and insights so they operate like "super-humans."

What to do now: a practical playbook

• Pick two use cases with clear payback: document intake/classification and bank statement analysis are proven starting points.
• Define guardrails: human-in-the-loop, audit trails, role-based access and clear fallbacks.
• Do context engineering: connect core systems, surface the right data at the right step, and strip noise.
• Measure what matters: cycle time, cost per case, accuracy/exception rates, NPS and staff satisfaction.
• Adopt modular tooling: APIs first, short contracts, easy swap if value stalls.
• Stand up an AI Working Group: cross-functional owners, a live roadmap, and stage gates tied to ROI and risk.
• Train your people: prompt patterns, QA for AI outputs, and policy basics for data use and customer interactions.
• Align with risk guidance: use frameworks like the NIST AI Risk Management Framework and the UK ICO's guidance on AI and data protection.

Signals to watch

• Agentic workflows scoped to small domains (e.g., internal audit sampling, fraud triage) with strict oversight.
• Richer doc intelligence that reads, compares and reasons across multi-document packs in seconds.
• Embedded credit tools that pre-assemble cases with evidence, improving consistency and speed without removing approvals.
• Better integrations from vendors: cleaner APIs, event streams and connectors to core platforms.

Bottom line

AI is already paying off in knowledge management and document processing. The larger gains depend on data readiness, tight scoping and change management. Keep people in control, make integration your priority, and scale what proves value.

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