AI and data reshape the credit lifecycle for lenders

During a webinar with FIS, representatives from Chartis Research and FIS outlined findings from the Chartis Credit Lending Operations 2026 report and discussed ways banks can modernise commercial lending operations without replacing core systems.

Panelists described lenders moving away from disconnected point solutions toward operating models that link data, processes, systems, partners and staff into a single lending ecosystem. The change is driven by pressure to shorten turnaround times, strengthen risk oversight and provide a consistent borrower experience.

Speakers gave examples of AI applications across the credit lifecycle. Underwriting can use automated document processing and decisioning; servicing can apply workflow automation; risk teams can use continuous portfolio surveillance to detect early signs of stress.

Real-time analytics and API-enabled architectures were presented as enablers for faster, more consistent decisions. Integrating data across front, middle and back offices helps credit officers and risk managers see up-to-date borrower exposures and cash flows. APIs connect internal systems with third-party services and external data such as account transactions or vendor financials.

Panelists outlined modernisation approaches that avoid full system replacement. Options include adding orchestration layers above legacy systems, deploying targeted AI models for specific tasks and building API layers to surface data for analytics and automation. These approaches are intended to raise operational efficiency while preserving platforms that run core lending functions.

Organisational change was identified as a necessary complement to technology updates. Successful programmes require cross-functional governance, clearer data ownership and closer integration of external partners. The Chartis report found that improvements depend on aligning people, processes and incentives so information flows through the credit lifecycle and decision rights are clear.

Risk management featured prominently. Panelists said continuous monitoring and automated alerts can improve oversight of concentration risk and emerging credit stresses. They added that AI models can flag borrower deterioration but must be supported by model governance, explainability and operational controls so automated signals feed defined human review steps.

Speakers described effects on the borrower experience: automation and unified data can shorten turnarounds and reduce manual steps that delay closings. Faster, more transparent credit decisions can help relationship managers match financing to client needs.

Panel participants included Anish Shah, Research Director at Chartis Research; Dale Glajchen, VP and Head of Commercial Loan Servicing and Syndication at FIS; and Tim Probst, Global Head of Commercial Loan Servicing, Enterprise and Client Strategy at FIS, with Sharon Kimathi moderating. Panelists advised prioritising high-impact use cases such as underwriting automation, document ingestion and portfolio surveillance, and using staged implementations and strong governance to maintain operational resilience.