Catching fraudsters at the entry point
Many fraudsters can be caught at the entry point by analyzing metadata of images they upload. We dig into the characteristics of the pictures and define the possibility of fraud before identity verification.
Metadata Analysis
Metadata Analysis defines the time and place when the picture was taken and compares them to the ones when the picture was uploaded
It helps to define:
- Pictures taken in advance
- Possibly fabricated images
Device Fingerprint Analysis
Companies often struggle to identify a device when users hide the IP address or switch among browsers on the same device. Device fingerprint analysis easily identifies individual devices - this is an important step in fraud detection.
It helps to prevent:
- Multi accounting
- Identity theft
- Credit card fraud
Detecting graphic manipulation
One of the popular ways for fraudsters to cheat is using graphic editors. To define whether the picture was edited electronically, we use our unique image authenticity tool. It checks two dimensions: the device features and image structure.
Enhanced signature analysis
We use an extensive database of cameras and software signatures to determine the source of an image or detect traces of modification software with very high accuracy.
It helps to identify:
- Any traces of Photoshop and another graphics software
Pixel analysis
Pixel analysis evaluates the image authenticity in several dimensions, so we can reveal malicious manipulations.
It helps to identify:
- Areas that have been modified in the editing programs
Fraud pattern detection
Skilled fraudsters make sophisticated forgeries, buy real documents on the Internet or gain access to the documents leaked to the DarkNet. We have special mechanisms to detect such cases.
Blocklists
The system checks the documents against the Blacklist. This is the database where we bring all suspicious profiles we ever detected.
It helps to identify:
- The documents from DarkNet
- The real documents bought in the Internet
- The documents of users who were suspected in the fraud
Documents’ Face matching
When fraudsters make forgeries, they often use templates with the same faces. So, there is a chance to find several documents with the same face, but with a different name and country. With our cross-reference and duplicate search tool, we’re able to find this type of fakes.
It helps to identify:
- Fake documents with similar photos
Authenticating the document
Skilled fraudsters use sophisticated digital and manual forgeries. To fight them, we analyse protective elements to ensure the document is official. The system automatically compares stamps, fonts, colored backgrounds, holograms, watermarks, microprints and other security features to our database of 14 000+ documents.
It helps to identify:
- Manual forgeries
- Documents that weren’t issued by state
Payment fraud analysis
Significant group of fraudsters makes money on false chargeback requests. To stop this type of fraud, we do a set of checks using the bank card data, the data received during KYC and liveness checks.
It helps to prevent:
- Illegal chargeback requests
Optical Character Recognition (OCR)
We support documents from 200+ countries and territories, and automatically check those written in Latin, Сyrillic, Semitic and Asian characters.
To verify documents we use our AI technologies: all textual data is extracted, transliterated into Latin characters and saved in the user profiles for your future reference.
MRZ reading
The system captures data from Machine Readable Zone (MRZ) of any Passport, ID, Visa and other documents
Barcode reading
The system decodes various 1D and 2D barcode formats such as PDF417, QR code, and Aztec code
Credit cards reading
The system extracts card number, cardholder’s name and expiration date from a debit/credit card
It helps to prevent:
- Illegal chargeback requests
Liveness detection
Comprehensive checks are very annoying for users. What is more, gesture-based gimmicks like asking a user to blink or speak a random passcode add friction to the experience and are easily fooled by spoofing tactics.
We use our own liveness technology. It’s just one simple action for users, but a fast and secure way to ensure the presence of a user for you.
It helps to prevent attacks using:
- Paper masks with eye & mouth cutouts
- Hollywood masks, wax figures & lifelike dolls
- Impostors, lookalikes & doppelgangers, and more
More about liveness technology
Face-based biometrics
Stolen IDs and impersonation attacks is one more possible way fraudsters choose.
Our AI technologies automatically select the best image from liveness and perform advanced facial scanning. It indicates if this image matches the photo on the ID without forcing the user to take extra action.
We use our own liveness technology. It’s just one simple action for users, but a fast and secure way to ensure the presence of a user for you.
It helps to ensure:
- The person who goes through liveness is an owner of the provided documents
Fuzzy matching
When the compliance team performs AML screening, it often has to struggle with a high number of false match results. The parameters need to be wide enough to take on slight nuances in names, but not too wide to generate false positives.
To meet this challenge, we provide a fuzzy matching tool with a big number of features including: — different spelling of names (e.g. ‘Jon’ instead of ‘John’) — shortened names (e.g. ‘Elizabeth’ matches with ‘Elisa’, ‘Elsa’, ‘Beth’) — abbreviations (e.g. ‘Ltd’ instead of ‘Limited’), and many more It helps to identify: Non-exact matches during AML screening
— different spelling of names (e.g. ‘Jon’ instead of ‘John’)
— shortened names (e.g. ‘Elizabeth’ matches with ‘Elisa’, ‘Elsa’, ‘Beth’)
— abbreviations (e.g. ‘Ltd’ instead of ‘Limited’), and many more
It helps to identify:
- Non-exact matches during AML screening
Customer Lifecycle management
While using Sumsub, you have a single user profile, so you get the user's entire history presented right in front of your eyes. You can see all the checks made in the past:
— what documents have been uploaded
— what checks have been passed and what are their results
— whether the user passed the questionnaire
— who from the team initiated the changes, and so on
It helps to:
- Store all information about user in one place
- Reduces errors during manual review