Financial services provider in Paris automates invoice processing with Nanonets AI
A Paris-based financial services provider processing over 20,000 invoices per month across hundreds of different formats could no longer scale manual processing. Invoice images contained junk data and multiple invoices per page, and attempts to standardize invoice formats caused friction in vendor onboarding.
How it works
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Object Detection
The object detection model detects the presence of invoices in images that may contain multiple items.
Tools used
NanonetsNanonets OCR AIAmazon TextractDocparser
Outcome
Nanonets' three-step AI workflow achieved an accuracy score of ~96% for object detection and ~98% for image classification, with fail-safe validation checks at each step before exporting data to internal systems, enabling customers to reduce the cost and turnaround time of accounts payable processing.
What failed first
Prior OCR solutions including Amazon Textract and Docparser were tried but encountered either accuracy issues or a lack of flexibility in handling new document formats.
Results
Time savedmore than 20,000
Volume~96%
Cost replacedreduce the cost of Accounts Payable processing