AI, APIs and Data Reshape Commercial Lending Operations

Chartis Research and FIS report that banks are replacing disconnected lending tools with integrated, AI-driven end-to-end lending platforms that use real-time analytics and API-enabled connections to support underwriting, risk monitoring and loan servicing. The findings appear in the Chartis Credit Lending Operations 2026 report.

The change responds to demands on commercial lenders to speed decisions, cut manual processing and tighten oversight while providing a smoother experience for borrowers. The report documents a move away from point solutions toward unified operating models that link data, processes, systems, partners and staff across the credit lifecycle.

Chartis and FIS identify concrete AI use cases now in deployment or pilot. Machine learning models are being applied to underwriting and credit assessment to augment human review and to automate routine workflow tasks. Similar models are used for continuous portfolio monitoring to flag shifts in exposure or borrower behavior in near real time. Automation is also being introduced into servicing functions to route tasks, handle exceptions and improve operational efficiency.

Real-time analytics and API connectivity are being used to make those applications work in legacy environments. Banks are using APIs to draw data from internal systems and external sources so analytics operate on a broader customer and exposure profile. The continual feed of data supports faster decision-making by credit officers and portfolio managers and enables automated triggers for risk mitigation and servicing actions.

Bank executives describe modernisation approaches that keep existing core systems while adding new capabilities on top. Integration typically proceeds via APIs and is accompanied by changes to operating models to support cross-functional teams. Projects that combine targeted technology updates with revised governance and processes aim to preserve operational resilience while improving information flow across underwriting, portfolio management and servicing.

The Chartis report highlights organisational change as a requirement for progress. It recommends clear operating models that define data responsibilities, incorporate external partners where appropriate, and apply consistent controls across the credit lifecycle.

A webinar hosted with FIS will present these themes and practical steps for banks. Panel participants include Dale Glajchen, VP and Head of Commercial Loan Servicing and Syndication at FIS; Anish Shah, Research Director at Chartis Research; and Tim Probst, Global Head of Commercial Loan Servicing, Enterprise and Client Strategy at FIS, with Sharon Kimathi serving as moderator. The session will examine how real-time analytics and API-enabled ecosystems affect decision-making and which AI use cases offer the largest operational impact.

Market complexity and rising borrower expectations are cited as background drivers for the change. Advances in data integration are enabling banks to combine information from across organisations to form a fuller view of creditworthiness and portfolio risk. Many institutions are pursuing staged modernisation and stronger operating models to increase efficiency, sharpen risk oversight and improve portfolio performance while avoiding full system replacements.