AI and data reshape end-to-end lending platforms

Chartis Research and FIS report that banks are moving from siloed lending systems to integrated, AI- and data-driven end-to-end platforms to speed underwriting, improve risk monitoring and streamline servicing across the credit lifecycle. The findings cite the Chartis Credit Lending Operations 2026 report as the basis for the conclusions.

Banks are consolidating data, processes, systems, partners and staff into unified lending operations. The reported drivers include pressure to raise efficiency, accelerate decisions, strengthen oversight and meet changing borrower expectations. The shift is supported by wider use of artificial intelligence, real-time analytics and API-enabled connectivity.

The report identifies high-impact AI use cases covering automated underwriting, workflow automation, continuous risk monitoring and loan servicing. Real-time analytics and API ecosystems are being used to gather internal and external data, producing a more complete view of customers and credit exposures and enabling faster action compared with batch-based systems.

Financial institutions are prioritising practical modernisation that enhances existing systems rather than replacing them. Common approaches include layering analytics and automation on top of core platforms, connecting legacy systems through APIs and establishing collaboration frameworks across credit, risk, operations and technology teams. These steps aim to maintain operational resilience and limit business disruption while improving information flow across the credit lifecycle.

Work on data integration is speeding access to financial statements, payment records and third-party information, which supports more accurate credit assessments and ongoing portfolio surveillance. API-enabled connectivity also broadens the range of external partners-such as fintech firms and data providers-that banks can use to augment decisioning and servicing without building every capability in-house.

The report notes operational changes are discussed alongside technology updates. Successful projects require clear operating models that define roles, data governance and exception handling so automation complements human decision-making. Lenders are also specifying which lending processes should remain manual, which can be augmented by AI and which can be fully automated, with attention to audit trails and model governance.

A webinar in association with FIS will bring together industry practitioners and researchers to examine how real-time analytics and API ecosystems improve decision-making, and how banks can identify the most impactful AI use cases across underwriting, risk and servicing. Panelists include Dale Glajchen, vice-president and head of commercial loan servicing and syndication at FIS; Emily Bogan, global head of lending origination and credit at FIS; Anish Shah, research director at Chartis Research; and Sharon Kimathi as moderator.

The Chartis report describes a path for commercial lending that focuses on incremental change: using APIs and analytics to make existing environments more visible, connected and adaptable rather than pursuing wholesale replacement of legacy systems.