Data extraction
Extract relevant information from the visual part of the document through our OCR algorithms
- Name
- Surname
- Date of Birth
- Country
- Document number
- Optional data
- Personal number
- Sex
- Document class code
- Issuing state code
- Nationality code
- Place of issue
- Place of birth
- Date of issue
- Age of issue
- Years since issue
MRZ Code Reader
MRZ or Machine Readable Zone is a particular area in an identity document (Passports and ID cards) that encloses the document holder’s personal data. Our AI Document Verification solution can easily decodify it.
- Name
- Surname
- Date of Birth
- Country
- Document number
- Nationality code
Data matching
Once information is extracted from visual and MRZ Sensity AI Document Verification will automatically match those data to ensure the document hasn’t been altered.
Data format validation
In the case of a manipulated document, it may happen fraudsters are not accurate in change data. Each data on the ID document has a precise format compliant with the national regulation (caps, font type, number of characters etc). With Sensity’s AI document verification you can detect invalid data format on thousands of documents worldwide.
Recency check
Sensity AI document verification returns and instant rejection if the document has expired or it doesn’t fit with the company expectations.
Authenticity checks
This is one of the most powerful features in our AI document verification suite. For most of the ID formats we are able to extract one of the most significative parts and through computer vision algorithms match them with the same part extraded by the original format issued by the government. If the algorithms give us a high confidence score, we consider that check passed. If the compared parts are not considered identical. The document will be rejected.
Ghost portrait analysis
If the document format presents ghost portrait we can send two warnings in case the ghost portrait is missing (in contradiction with the national format that needs a ghost portrait) or the ghost portrait doesn’t match with the main picture, so it can be considered a spoofing attempt as you can see in the examples below.
Metadata analysis
We investigate the file looking for signals of a potential forged document. This suite include 8 different checks to improve your fraud detection capabilities.
Splicing detection
This approach is entirely based on a deep neural networks able to analyze the document at pixel level, detecting manipulated fields.
- 100% Automated
- 5 seconds response
- 99.2% accuracy rate
