Introducing LoanWatch Document Intelligence

Borrower files arrive in the many different formats their accounting systems produce. With LoanWatch Document Intelligence, verified data and a reconciled manifest return on your portfolio's own schedule.

A regional-bank analyst opens the third borrower file of the morning. It is an Excel workbook with nine tabs and a dead pivot, its receivables aged into buckets the last borrower would not have recognized. None of it is wrong; it is simply how that accounting system exports its data. The analyst ties the tabs together, keys the figures into the borrowing base, and moves on. It takes 15 minutes, maybe 30 if a total refuses to balance. Then the next file arrives in a different shape, and it starts again.

Multiply that by the 50 to 200 files a regional ABL group handles in a month, and a pattern emerges that has nothing to do with credit judgment. We have written before about the borrowing base reconciliation tax: roughly 40 hours a month for one analyst, and across a five-person team, north of $250,000 a year of skilled judgment spent on spreadsheet mechanics. The quieter cost is accuracy. Field examiners routinely find material errors in most borrower-submitted certificates, and one money-center bank found 90 percent of its borrowing bases held errors before systematic controls were in place. Those errors clear review at the keyboard and surface eight months later in an audit, as a finding or an advance that ran past its limit.

Today we are introducing LoanWatch Document Intelligence, built to remove that failure point from the borrowing base entirely.

What it does

LoanWatch Document Intelligence reads the files an ABL group already receives, in the formats they arrive in, and returns validated data with a delivery manifest that ties every total back to the source documents. The clean CSV and the nine-tab workbook with the dead pivot are handled the same way, as inputs to be parsed and checked. Those 15 minutes were never where judgment belonged, and removing that busywork returns the time to the credit decisions an analyst is there to make.

Two numbers define the standard it meets. Every record matches its source totals to the penny, a delta of $0.00, and more than 20 validation checks run on every file before delivery, covering totals, row counts, aging buckets, duplicates, dates, and cross-source ties. Nothing is installed, and there is no parser library for your team to maintain as borrowers quietly change their exports. An export in a format we have not seen becomes clean, verified data within the first week. There is no quarter-long integration project. Files arrive through a secure channel, and the data and its manifest come back on the cadence you define.

Why it can be trusted

Parsing financial documents with AI is easy to demo and hard to trust, because the failure mode is a confidently wrong number. LoanWatch Document Intelligence is built so that number has nowhere to live. Deterministic code is the source of truth, and AI is the second check rather than the first. The parsing and calculations run in code; AI handles only what code cannot decide on its own, such as classifying an unfamiliar file or drafting a new parser, which a person reviews before it touches a live file. Every AI output must pass a schema check to be accepted, and a failed call falls back to the code's own result. The code produces the numbers, AI checks them, and a person approves each file that ships.

The AI layer is optional. If your organization does not permit AI, leave it off and the parsers run on their own; turn it on only where you want it to augment code that already works without it. When selected, production AI calls run inside AWS through Amazon Bedrock. Customer files never train any model, and customer names never enter the codebase.

It is also ABL-native, which matters more than it sounds. The entity types an ABL deal contains (customer, senior lender, lender, borrower, debtor, and servicer) are first-class citizens of the system, each with the cardinality it carries. Validation understands what an ABL document is supposed to mean, so a rule like "a borrower can never equal its own debtor on the same row" is enforced at zero tolerance instead of being left for a tired human to catch at day's end. It was built in code from the start, by people who knew what an ABL deal looks like.

Where the analyst hours go instead

The hours that used to go to keying go back to credit, where the people you hired for judgment earn their cost. The totals ship with every delivery, so when a participant lender or an internal auditor questions an advance, the answer starts from a manifest whose figures already agree with the source files, and an audit becomes retrieval rather than excavation. The benefit scales with the pain: non-bank and specialty-finance lenders grow without adding back-office headcount, because there is no parser library to maintain and onboarding a new borrower stays a week-one event. For anyone who has carried the reconciliation tax as a fixed cost of doing ABL, the line item shrinks, with no migration required.

Where it fits

LoanWatch Document Intelligence stands on its own. If you need a dependable way to turn the documents your borrowers send into clean, accurate data, it does that and stops there. It is also the cleanest front door to LoanWatch Collateral Intelligence, our platform for automating ineligibles and borrowing base calculations: the same data can feed continuous collateral oversight whenever you are ready, and the first step pays for itself either way.

A first conversation

The fastest way to judge it is to run it on your own files. A first conversation runs about 30 minutes, focused on your file mix and the borrower types that consume the most analyst hours today. Pilots usually begin with a small set of borrowers in the formats responsible for the heaviest manual workload, with verified output delivered in the first week. Reach us at hello@loanwatch.io.