By Troy Coggiola, Chief Product & Strategy Officer, MeridianLink
Originally published in the ProSight Executive Report
Adopting artificial intelligence is a top priority in banking and lenders are ready to put it to work: 89% of respondents in a recent study by Experian said they expect AI to play a critical role across the lending lifecycle, 84% rank it as a strategic priority over the next two years, and 51% are already implementing AI solutions.
On paper, investment levels signal industry momentum. Yet, most institutions are still early in their AI journey, unsure of how to scale beyond pilots. And while interest and spend continue to rise, only 16% have an enterprise-wide AI roadmap. As a result, deployment remains fragmented across point solutions rather than connected across the lending lifecycle.
The challenge is not AI adoption. It’s execution.
The following sections use document-heavy workflows to illustrate how AI can move from investment to measurable impact in lending.
Where lending loses time and money
Two friction points in the lending process consistently surface across the industry: document collection and review, and the coordination required to move through steps in the workflow.
Despite years of digital transformation and automation, document-heavy processes remain manual, inconsistent, and error-prone. A single requirement, for instance, often gets bogged down in the process. It might be created in one form and interpreted differently as it passes between systems and teams. It frequently reaches the borrower with gaps in clarity. Those gaps trigger repeated cycles of interpretation and rework before the stipulation is fully addressed.
This inefficiency compounds quickly: cycle times lengthen, cost per loan increases, borrower experience degrades, and consistency breaks down as the same requirement is handled differently across the institution.
Many financial institutions introduce AI as a collection of point solutions across the lending lifecycle. Each is designed to improve a specific task but operates without shared context across the broader loan process.
Lending data exists across multiple environments. Workflows across loan origination systems (LOS), point of sale (POS), and third-party tools operate in silos. Borrower requirements are created without full application context or downstream awareness. Missing information is surfaced but unevenly resolved, leading to repeated clarification cycles.
Each improves a slice of the process, but the lending workflow itself remains structurally unchanged.
Foundational shifts to operationalize AI in lending
Bridging the gap between AI investment and lending performance requires moving from point solutions to workflow-level execution. These three capabilities make that possible:
- A unified loan data foundation
Every critical loan element—requirements, documents, income data, credit attributes, and tasks—works better when connected to the loan file and available in context, with the LOS serving as the system of record.
When information lives in PDFs, emails, or siloed systems, AI can still read, classify, and extract it. But without clear relationships across the loan file, interpretation and follow-through can become inconsistent. Normalizing and connecting data across the lifecycle creates a more reliable source of truth, allowing information to be interpreted, reconciled, and applied consistently.
- Event-driven lending workflows
AI in lending performs best when it operates with timely workflow context in concert with broader workflow automation and even human-in-the-loop exception handling, when needed.
Modern lending systems should capture key events as they occur, including condition creation, document submission, review, validation failures, and condition clearance. This allows breakdowns to be identified closer to the moment they happen, rather than after delays and rework have already compounded downstream.
- Embedded intelligence inside the system of record
AI delivers the most value when it operates inside the lending system, not alongside it.
When intelligence is embedded directly into the loan origination workflow, it can access full context, act within process steps, comply with regulatory requirements, and maintain continuity across the lifecycle. This reduces repeated manual effort required to interpret requirements, rewrite requests, or reconcile information across disconnected systems.
What an AI-enabled document workflow looks like
With these capabilities in place, document processing shifts from a reactive bottleneck to a controlled, intelligent workflow.
For example, when a borrower uploads documents such as pay stubs or bank statements, the system can immediately assess quality and completeness, extract relevant data, and map it to loan requirements in real time. Requirements are validated instantly, with clear accept or reject feedback, and approved data flows directly into the loan file without manual re-entry.
Instead of waiting for downstream review cycles, validation happens at the point of entry.
This reduces ambiguity for borrowers, eliminates unnecessary back-and-forth, and significantly lowers the operational burden on lending teams. Most importantly, institutions can reach decisions faster, and with greater confidence and precision, because the friction that typically slows down underwriting and processing has been significantly reduced at the source.
As financial institutions evaluate AI capabilities, the key distinction is no longer who has AI but how deeply it is embedded into the loan origination system.
To deliver real value, it must be grounded in a financial institution’s data, governed by clear rules and auditability, and integrated into the workflows where decisions are made.
The materials available in this article are for informational purposes only and not for the purpose of providing legal advice. You should contact your own advisors with questions regarding the AI in lending content herein. The opinions expressed in this article are the opinions of the individual authors and may not reflect the opinions of MeridianLink, Inc.
