If you look at banking over the last twenty years, a lot has changed. Applications went online, documents became digital and alerts turned real-time. Yet the core challenges haven’t gone away. Lending is still too expensive, too complex, and too fragile. STRATMOR Group estimates that producing a mortgage still costs more than $10,000, with roughly 90 percent of that tied to human labor.
Complexity adds to the burden. Every loan passes through dozens of steps and multiple systems, from pricing engines to fraud checks to closing platforms. None were built to work seamlessly together, so files are handed off repeatedly. The moment something is missing or inconsistent, the process stops. A person has to step in, fix the issue manually and re-run the checks. Months of effort can go into a single file, only for an issue to surface at the very end.
So, while individual steps have been digitized, the whole system fundamentally hasn’t changed. Tools work in isolation, but the handoffs are still human. That is the real bottleneck.
The Evolution of Technology in Lending
For years the industry has been chipping away at this bottleneck with new technology. Each wave brought progress but also revealed its own limitations. None fully connected the process end to end. Even now, the industry needs more than helpers. It needs operators — systems that can reason, plan and execute. That is the shift to agentic artificial intelligence (AI).
Agentic AI moves from reactive helper to proactive operator. Instead of just observing and recommending, agentic AI connects signals across the bank, plans out multi-step work and carries it through using the same systems your staff already use. Simply put — instead of simply suggesting the next step, it can complete the task.
Agentic AI Use Case in Banking
Early uses of agentic AI reveal its potential in eliminating common bottlenecks by introducing live agents in some of the most expensive and error-prone parts of the process, like prefunding quality control and document validation.
Take quality control (QC) as an example. QC has long been one of the most costly and time-consuming parts of lending, because it happens late in the process and requires reviewing hundreds of pages manually. Traditionally, lenders review only a fraction of loan files due to cost and time.
With agentic AI, they can review 100% of files in-flow, reducing the risk of buybacks, penalties and reputational harm while also delivering consistent, standardized results with audit ready transparency.
By shifting QC upstream, agents can reconcile documents against investor guidelines as soon as they’re uploaded, generate the necessary follow-ups automatically and create an audit trail in real time. What once took days of back-and-forth is resolved overnight, leaving staff with a clear picture of what’s complete and freeing underwriters to focus on judgment calls instead of repetitive checks.
The implications are significant. Costs come down as agents predict which documents will be needed and gather conditions proactively. Complexity eases as workflows adapt in real time and discrepancies are resolved before a human ever opens the file. Confidence rises because every loan can be audited instantly, with explanations and verifiable trails that reduce defects, accelerate secondary execution and strengthen investor trust.
From Fragmented to Intelligent Origination
If the last decade was about digitization, the next will be about intelligence. Intelligent origination is the concept of moving beyond digitized steps and point automations toward a unified system that can orchestrate the entire process end-to-end.
QC is just one use case of where this shift begins, but the bigger picture is applying this intelligence across every step of the origination lifecycle — from application to closing and funding. Intelligent origination envisions agentic AI coordinating across systems, anticipating what’s needed and resolving issues proactively. It turns workflows from a series of disconnected handoffs into a continuous flow. The role of people shifts from doing the work to overseeing the work, from reactive problem-solving to proactive confidence at scale.
The shift to agentic AI won’t just change origination, it will redefine how financial institutions operate. The winners will be those who move first, building systems where confidence is manufactured, costs are contained and growth is scalable.
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