Kyc aml · Production

Nanonets automates driver license OCR for North America's largest digital identity verification provider

The problem

The client processed over 50,000 ID documents per month using a traditional OCR engine supplemented by 10 manual-review contractors—an expensive, time-consuming, and error-prone arrangement that could not reliably handle the diversity of document formats or poor-quality photograph submissions.

First attempt

AWS Textract and Abby were both evaluated and found to provide insufficient accuracy; while they could extract data from some documents, they jumbled field values in many others and did not meet the client's automation threshold. Maintaining the traditional OCR solution internally was also ruled out as increasingly expensive with minimal accuracy improvement.

Workflow diagram · grounded in source
1
Customer submits ID document
trigger
“each customer has to submit ID documents. The most commonly submitted ID cards are Drivers' Licenses and Passports”
2
Automatic image quality validation
validation
“The solution automatically identified whether the images adhere to specifications like the high resolution, full ID card visible, the upright orientation of the ID card etc. and providing instant feedback to customers”
3
Custom model field extraction
ai_action
“The OCR API could identify the structure of the license, the titles and fields in it.”
4
JSON output for application integration
output
“These fields can be extracted as json outputs so that it could be easily integrated with our client's application”
Reported outcome

Nanonets delivered a custom-trained OCR model with response times under 15 seconds that automatically validates image quality, extracts fields as JSON for direct application integration, and can run on-premises so user data never leaves the client's infrastructure.

Reported metrics
Document processing response timeless than 15 seconds
Monthly documents processedover 50,000
annual ID cards processedover 10 million
Manual review contractors (prior state)10 contractors
Show all 5 reported metrics
document processing response timeless than 15 seconds
monthly documents processedover 50,000
annual ID cards processedover 10 million
manual review contractors (prior state)10 contractors
previous process qualityexpensive, time consuming and error prone
Reported stack
NanonetsAWS TextractAbbydocker containers
Source
https://nanonets.com/customer-success-story/driver-license-verification
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Nanonets delivered a custom-trained OCR model with response times under 15 seconds that automatically validates image quality, extracts fields as JSON for direct application integration, and can run on-premises so use…

What tools did this team use?

Nanonets, AWS Textract, Abby, docker containers.

What results were reported?

Document processing response time: less than 15 seconds; Monthly documents processed: over 50,000; annual ID cards processed: over 10 million; Manual review contractors (prior state): 10 contractors (source-reported, not independently verified).

What failed first in this deployment?

AWS Textract and Abby were both evaluated and found to provide insufficient accuracy; while they could extract data from some documents, they jumbled field values in many others and did not meet the client's automatio…

How is this kyc aml AI workflow structured?

Customer submits ID document → Automatic image quality validation → Custom model field extraction → JSON output for application integration.

Related kyc aml cases