9 out of 10 edits fail KYC. And it’s not about a “pretty image”
▪ Mustang Service – when approval matters more than blind testing.
› We work with what systems actually check:
→ passing anti-fraud checkers: Onfido, Veriff, Sumsub, etc.
→ German-grade studio equipment
→ two-stage control + metadata cleaning before submission
→ ELA analysis bypass
→ data structure and logical consistency
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BANK AND CRYPTO CHECK DIFFERENTLY
YOU HAVE THREE NAMES AND NONE OF THEM MATCH
In the machine-readable zone your name follows the ICAO standard. In the passport's main field – a national transliteration scheme. In your bank – whatever was typed when the account opened.
Sergei, Sergey, Serhii. Iurii, Yuriy, Yurii. Formally one name, to the system three different people.
Matching runs character by character. A single letter apart creates a mismatch between form, document and payment method. Genuine data, genuine document, result: rejection.
Extra traps: apostrophes in surnames, double given names, and the order of fields in the MRZ – surname comes first there, and that gets mixed up constantly.
Before submission we align the spelling across all three points and match the form to the MRZ version.
Name spelled differently on your card and your passport? Write before submitting, not after the refusal – this takes minutes to fix.
AN ALGORITHM FLAGGED HER PHOTO – SHE SPENT NEARLY SIX MONTHS IN CUSTODY
A fake military ID surfaced in a bank fraud case. Facial recognition returned a social media photo of a 50-year-old Tennessee woman as a possible lead. She was arrested.
The system suggested a face. Humans were supposed to establish whether it belonged to the person behind the crime. The cost of getting that wrong was far more than a rejection screen.
