AI, APIs reshape commercial lending lifecycle

Banks are replacing disconnected lending systems with integrated, AI- and data-driven platforms to accelerate underwriting, strengthen risk monitoring and automate borrower servicing. Institutions are using real-time analytics and API connectivity to link data, processes, systems, partners and staff across the credit lifecycle.

Pressure to reduce costs, make faster credit decisions and provide a consistent borrower experience has driven the shift. Findings from the Chartis Credit Lending Operations 2026 report highlight industry attention on moving from fragmented point solutions to unified lending operations that provide greater visibility and consistent controls across origination, assessment, management and servicing.

Financial firms and technology providers are prioritising AI use cases with measurable impact. Underwriting and credit assessment are being targeted for model-driven automation. Workflow automation is being applied to reduce manual handoffs and shorten approval times. Risk monitoring and portfolio surveillance are being augmented with AI and real-time analytics to detect shifts in borrower behaviour and sector exposures more quickly. Servicing functions are using automated communications and decision logic to manage collections and customer interactions.

Real-time analytics and API-enabled ecosystems are connecting internal records and external data sources. Consolidated customer and exposure data is being made available to credit officers and risk teams to support timelier decisions and continuous portfolio monitoring. APIs are used to integrate third-party services, cloud data stores and legacy platforms so banks can coordinate end-to-end processes without replacing core systems all at once.

Many institutions favour incremental modernisation over full system replacement. Common approaches include adding an API layer over existing systems, deploying targeted AI models for high-impact tasks, and implementing orchestration tools to unify workflows. These hybrid strategies seek improved decision-making and portfolio performance while preserving operational resilience and limiting disruption to daily lending activities.

Operational change accompanies the technology work. Banks are adjusting operating models to enable cross-functional collaboration, integrating external partners where needed, and establishing governance for data, models and automation. Clear priorities and proof-of-value pilots are being used to identify initial use cases focused on underwriting accuracy, process speed and early risk detection before wider rollouts.

A webinar hosted with FIS will examine how real-time analytics and API-enabled connectivity affect decision-making in commercial lending. Participants include Dale Glajchen, VP and Head of Commercial Loan Servicing and Syndication at FIS; Emily Bogan, Global Head of Lending Origination and Credit at FIS; and Anish Shah, Research Director at Chartis Research. Sharon Kimathi will moderate.

Historically, lenders relied on separate point solutions and siloed workflows that limited the ability to view customers and exposures across the organisation. Advances in data integration are making consolidation easier, while banks continue to build the people, processes and governance needed to use that data across the full lending lifecycle.