AI and APIs reshape commercial lending lifecycle

A recent webinar hosted with FIS brought together industry experts Dale Glajchen, Anish Shah and Tim Probst to discuss a shift from siloed lending systems to integrated, AI-driven platforms. The session cited findings from the Chartis Credit Lending Operations 2026 report.

Panelists described unified operating models that combine data, processes, systems, partners and people to speed decisions, improve efficiency and create a more consistent borrower experience.

They identified AI use cases with measurable benefits: credit underwriting and assessment, workflow automation in origination and servicing, ongoing portfolio monitoring and operational decision support. Real-time analytics were presented as a way to speed approval decisions and give continuous visibility into exposures, while AI models can flag early warning signs and automate routine reviews.

Panelists described API-enabled ecosystems as central to a connected lending stack. Exposing core functions and data through APIs lets banks bring in third-party data, fintech partners and internal tools more easily and supports adding analytics and automation on top of legacy systems.

Modernisation, the panel highlighted, requires changes to operating models. Standardising data definitions, improving integration to create a single view of customers and exposures, and setting governance that balances innovation with operational resilience were listed as steps institutions should take. The session was moderated by Sharon Kimathi.

On selecting AI projects, panelists recommended targeting high-volume or high-manual-effort processes with clear data availability and outcomes that affect financial or operational performance. They recommended piloting models in controlled settings, monitoring performance in production and building feedback loops between front-line staff and analytics teams.

Discussion also covered portfolio management and risk oversight. With better data integration and continuous analytics, banks can shift from periodic batch reviews to near-real-time monitoring of credit quality and concentrations, enabling earlier intervention on at-risk loans and supporting more dynamic provisioning and capital planning.

Practical tactics put forward included deploying APIs to expose functionality, creating centralised data stores or federated views to reduce repeated integrations, automating routine tasks to free staff for judgment work, validating models through phased pilots and integrating external partners to access specialised data or services.

Speakers noted the approach described in the Chartis report, where intelligence, automation and connectivity work together, may create clearer audit trails across origination, servicing and secondary-market activities while allowing institutions to evolve existing systems rather than replace them.