As AI agents move deeper into commercial lending, financial institutions must establish clear boundaries around automated decision-making. Determining which processes can be delegated to AI and which require human oversight is becoming an important governance challenge, particularly where decisions carry regulatory, financial and reputational consequences.
In an analysis from nCino, the banking technology provider examines how financial institutions can deploy AI agents across lending workflows without undermining accountability or human judgement. Drawing on Gartner’s Market Guide for Commercial Loan Origination Solutions, the analysis explores the risks of adopting AI without clearly defining its role in decision-making.
AI agents offer opportunities to accelerate loan processing, increase operational capacity and improve quality control. However, these benefits depend on identifying where automation adds value and where human intervention remains necessary. Financial institutions must assess whether individual tasks are suitable for conventional automation, agent-led processes with defined limits or decisions that require direct oversight from experienced staff.
The growing number of technology providers makes this assessment more difficult. Gartner’s Market Guide for Commercial Loan Origination Solutions identifies more than 60 vendors worldwide and describes the market as largely undifferentiated in functionality. As suppliers promote their AI capabilities, financial institutions risk adopting agentic technology without establishing whether it addresses a genuine operational need. Gartner also warns that enthusiasm surrounding generative and agentic AI can exceed the technology’s actual capabilities, complicating procurement and risk assessment.
Poorly governed AI deployment can expose institutions to several risks. Customer relationships may suffer if automation replaces personalised support where it is needed. Systems may also struggle to handle exceptions that fall outside predefined patterns, potentially affecting the quality of lending decisions. More significantly, an accountability gap can emerge when a decision must be justified to regulators or internal credit committees but the reasoning behind it cannot be adequately explained.
Gartner expects fully autonomous systems to remain unlikely in loan origination because of the regulatory stakes involved. Instead, multiagent systems designed to coordinate specific tasks are expected to become more prominent. Explainability remains a concern, with Gartner noting that some vendors give limited attention to explainable AI in their marketing or product plans. Financial institutions cannot transfer responsibility for decisions to technology providers simply because an external model was used to reach an outcome.
Effective oversight also depends on the quality and accessibility of information supporting AI-driven decisions. Integrating products from multiple vendors can fragment data and make it harder to reconstruct how an outcome was reached. An agent operating with incomplete information may produce a confident response that overlooks important details. Gartner recommends a single-platform approach that enables loan officers, underwriters and relationship managers to access relevant information and tools in one place.
A shared platform can support consistent policy application, centralised decision records and a clear audit trail. These controls are particularly important for institutions seeking to demonstrate that AI-assisted decisions comply with internal policies and can withstand regulatory scrutiny.
Across the five stages of the credit lifecycle, client acquisition, underwriting, booking, servicing and loan management, nCino’s analysis identifies three broad approaches to allocating work: automate, guide and tailor. This framework offers a way to match the level of technological autonomy to the complexity and risk of each task.
Automation is best suited to high-volume, rules-based activities with verifiable outcomes. Document classification, data extraction, financial spreading and covenant tracking are examples of processes that can reduce manual workloads while maintaining consistent procedures. However, institutions still need appropriate controls to verify outputs and identify errors before they affect downstream decisions.
Guided workflows allow AI agents to undertake preliminary tasks while human staff retain responsibility for the final decision. Agents can assist with drafting credit memoranda, validating datasets, checking compliance with credit policies and answering questions about internal requirements. Staff can then review the results before a case progresses, preserving oversight while reducing the time spent on routine analysis.
More complex activities should remain human-led, with AI providing supporting information rather than determining the outcome. Deal structuring, pricing exceptions, syndicated credit and relationship decisions can involve nuanced commercial considerations that require experienced judgement. Where decisions carry greater complexity or financial exposure, institutions need to ensure that automated recommendations do not replace meaningful human assessment.
Regardless of the approach, every outcome must be capable of withstanding scrutiny. Financial institutions should assess model governance, transparency and data security before introducing AI agents into operational workflows. They should also prioritise solutions with clearly defined safeguards from the outset, rather than relying on controls added after deployment.
In its analysis of Gartner’s Market Guide for Commercial Loan Origination Solutions, nCino examines how financial institutions can assess AI capabilities while balancing operational efficiency with governance and accountability. The wider challenge for banks is to establish where AI can improve lending processes without compromising explainability, decision quality or regulatory compliance.
Copyright © 2026 FinTech Global
Copyright © 2026 RegTech Analyst
