Building Trust Through Verified Data
Consider a complex banking scenario like mortgage know your customer (KYC) onboarding, which we’ll use as a running example. This process involves multiple document types, edge cases and a mix of structured and unstructured information.
Instead of blindly handing over the keys to an agent, structure the process in distinct stages.
The first step is making your data consumable for AI models. Using Document AI enables you to separate and classify the files and extract the key data to create a reliable, structured view of customer submissions. You want facts, not generative assumptions. (Document AI techniques develop computer models capable of analyzing documents in a manner similar to human review.)
Key insight: There is absolutely no value in analyzing or reasoning over data you cannot trust.
• Use Document AI to extract facts before any reasoning occurs.
• Remove generative AI functionality from the initial data gathering phase.
• Build a structured, verified baseline of customer submissions.
Bottom line: Ground your automation in verified data extraction to prevent compounding errors downstream.
Read more: The Next Wave of AI in Banking Will Have Nothing to Do with Technology
Set Control Points to Safeguard Your Process
You cannot assume every submitted document is legitimate — in fact it’s critical not to.
Before any further processing happens, you must introduce strict control points. Assess the documents to determine if they have been artificially generated or tampered with. If the evaluation is not perfectly clear, the system should follow an exception path to notify a fraud team for human review.
Next, validate the data deterministically: Ask binary questions:
• Do the bank statements align with the paystubs?
• Do the documents belong to the same person?
• Are the forms complete?
If these checks fail, the process will adapt. The system creates a structured summary highlighting the missing or mismatched data.
The challenge: Agents fail when they attempt to perform deep analysis on incomplete or inconsistent data.
• Implement fraud detection immediately after document extraction.
• Create dedicated exception paths for flagged or suspicious files.
• Apply deterministic checks to ensure data consistency before invoking a large language model.
Bottom line: The process must always produce a structured outcome, whether an application passes verification, fails validation, or triggers a fraud alert.
Read more: How a 160-Year-Old Law Will Regulate AI for National Banks for Years to Come