Banks and lenders are increasingly turning to AI-driven automation to streamline complex workflows and reduce operational friction. Finova’s latest launch highlights how conversational AI is being embedded directly into lending ecosystems to improve efficiency and broker experience.
The UK mortgage market is witnessing a significant shift toward autonomous technology as Finova, a prominent provider of cloud-based lending software, debuts its new Broker Assist AI agent. This launch represents a major milestone in the digital roadmap of the firm, marking the first live deployment of agentic artificial intelligence within its lending ecosystem. Developed in collaboration with Covecta, the tool is specifically designed to eliminate the common bottlenecks that delay mortgage applications and frustrate intermediary networks.
Broker Assist functions as an intelligent, conversational layer integrated directly into the existing broker portal. It allows users to query complex, lender-specific policies and criteria in real time through a fully authenticated interface. This integration means that brokers no longer need to engage in context switching—the practice of leaving their workflow to manually search through static PDF documents or external websites. Instead, the AI provides immediate, accurate answers, ensuring that applications are completed correctly at the point of initial submission.
A partnership announced in January between Finova and Covecta provided the technological foundation for this rollout. Unlike generic language models that lack industry context, these pre-configured agents are built around the specific operational logic of the mortgage and savings sectors. This specialisation enables brokers to self-serve information regarding lending criteria without needing to contact lender support desks or call centres for routine clarifications.
The benefits for lenders are equally significant, focusing on operational efficiency and cost reduction. By improving the quality of applications before they reach the underwriting stage, the tool helps minimise the “rework” loop, in which files are sent back for corrections. Lenders utilising this technology can expect higher conversion rates and a notable decrease in the volume of inquiries reaching their contact centres. This streamlining of the origination process allows financial institutions to accelerate their innovation cycles across their wider broker networks.
Rowan Clayton, the Chief Product Officer at Finova, explained that the initiative is centred on removing friction from the broker journey. He noted that the era of slow application times and costly manual errors is coming to an end. By embedding conversational AI directly inside the portal, the firm is helping intermediaries deliver better outcomes for borrowers through faster, data-driven decision-making. He further emphasised that this is just the beginning of a broader AI strategy, with plans to embed additional intelligent automation across the entire lending lifecycle in the coming months.
This move underscores a broader trend in the UK fintech sector where “production-ready” AI is replacing experimental pilots. As the mortgage market continues to face pressure for greater speed and transparency, the ability to provide instant, verified policy information becomes a critical advantage. Finova intends to dedicate further resources to refining every aspect of the broker experience, positioning its platform as a high-tech hub for the next generation of digital lending.
What this means for the industry
• AI is becoming embedded directly into core lending workflows
Finova’s launch demonstrates how conversational AI is moving beyond standalone tools and becoming part of the operational backbone of financial services platforms.
• Mortgage processing is shifting toward real-time decision support
Instant access to lending criteria reduces delays and errors, enabling brokers to complete applications more accurately at the first attempt.
• Operational efficiency is becoming a key competitive differentiator
Lenders adopting AI-driven tools can reduce rework, lower support costs and improve conversion rates across their broker networks.
• Specialised AI models are gaining traction over generic solutions
Industry-specific AI agents, trained on sector logic and policies, are proving more effective than general-purpose models in regulated environments.
• The lending lifecycle is moving toward full automation
This rollout signals a broader transition toward intelligent, end-to-end automation across origination, underwriting and broker engagement processes.
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