AI and data reshape commercial lending lifecycle

Chartis Research’s Credit Lending Operations 2026 report says banks are shifting from siloed lending systems to integrated, AI-driven platforms that use real-time analytics and API-enabled ecosystems to speed underwriting, improve risk monitoring and streamline loan servicing.

Lenders are replacing disconnected point solutions and separate workflows with operating models that connect data, processes, systems, partners and staff. The report links the change to pressure to cut processing times, make faster credit decisions, strengthen oversight of loan portfolios and provide a smoother borrower experience as market conditions and client expectations change.

Chartis identifies specific uses for artificial intelligence across the credit lifecycle. AI models are being applied to underwriting and credit assessment to surface borrower signals, to workflow automation to reduce manual tasks and to continuous monitoring to detect deteriorating credit or concentration risk earlier. Real-time analytics, fed by integrated data sources and delivered through APIs, give loan officers and risk managers current information rather than static reports.

Data integration projects are combining internal systems and external feeds to produce fuller views of customers and exposures. Chartis reports that those views are used for pricing, targeted portfolio management and identifying cross-sell or restructuring opportunities. Banks are prioritising incremental modernisation steps such as adding API layers, deploying analytics engines and automating specific workflows while retaining core systems.

The report highlights changes to operating models. Institutions are creating structures to promote collaboration among credit, risk, IT and client teams and are allowing selective integration of external partners where appropriate. Chartis notes firms are addressing how to scale AI and automation while preserving operational resilience and meeting regulatory requirements, and how to design data flows that support consistent decisions across origination, servicing and portfolio management.

Chartis lists open practical questions for banks implementing these designs, including which AI use cases offer the highest return across underwriting, automation, monitoring and servicing; how to sequence integration projects to avoid business disruption; and how to ensure governance, explainability and compliance as models take on more decision-making.

A webinar in association with FIS will discuss real-time analytics and API ecosystems for lending. The panel will include Dale Glajchen, vice president 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 moderating.