From documents to credit proposal in minutes: AI-powered digital lending with ApPello
Every corporate credit proposal starts with a pile of documents. The client sends financial statements, an annual report, a business plan and the financing offer agreed with the relationship manager, often outside the loan origination system. Then someone reads all of it and retypes every value into the right field, entity by entity. It is slow, error-prone work, and it happens before anyone has made a single credit decision.
With the newest feature of the ApPello Digital Lending Platform, the documents themselves start the process.
Start the credit proposal with AI
Instead of the standard manual start, the user chooses to start the credit proposal with AI and simply uploads the client’s documents. The platform classifies each file automatically. In our demo, these were a business plan, a financial statement and an accepted financing offer.
Extracted, mapped and cross-checked
The AI reads every document, extracts the data and maps it to the entities and fields of the credit proposal: client, facilities, fees, collateral, covenants and conditions, credit analysis and financial statements. The platform then checks the documents for consistency against each other.
The mapping is configuration, not code. Configuration files define the document types, what to look for and how each value maps to the system.
Confidence you can trust
Every value is extracted several times, with targeted extractions from the source. When the results agree, the value is marked high confidence. When they differ, it is flagged for review.
In our demo, 202 fields came back with high confidence, 18 were marked for optional review and 3 conflicts were identified.
People decide, AI does the copying
Resolving conflicts is mandatory. Everything else is optional. For each flagged value, the reviewer sees exactly where it came from, highlighted in the original document.
In the demo, the client’s registration number appeared differently in two documents because of a typo. One click on the correct value, confirmed by the tax number, settled it. Where a field is linked to a codebook, the reviewer picks from the codebook instead of typing.
Every decision on record
Before approval, a visual summary shows the client, the facilities and the connected collateral. A review history records who confirmed or corrected which value, and when.
A credit proposal, already filled in
After approval, the standard credit proposal process starts with the data in place:
the proposal description, loans, fees, conditions and covenants
the collateral structure in the graphical editor
the financial statements in the statement analyzer
the source documents, attached to the process
How much time does it save?
In our live demo, three client documents produced 223 pre-filled fields. AI processing took under a minute. The whole journey from upload to a ready credit proposal, including the human review, took under five minutes.
Longer document packs take longer to process, but the work runs in the background while the analyst does something else. Either way, it is far faster than reading every page and retyping the data by hand.
The result: credit experts spend their time on review and decisions, not on copying data from a PDF into a field. The same approach can be applied to other document-heavy lending processes too.
If you’d like to see what it could do for your bank, book a 15-minute call!