Remote Fraud Prevention Analyst – KYC/AML Focus

Cybertexex; United States; Remote; USD 12,000 - 24,000; Full time

Benefits offered by this job

Fully remote

Performance-based bonuses

Flexible schedule

Electronic service agreement

Job summary

Cybertexex is a global provider of automated KYC, watchlist screening and behavior analytics solutions. We are expanding our team and hiring an Anti-Fraud Officer to protect the product and its users from fraud while supporting business growth and maintaining positive customer experience.

This is a fully remote position based in Vietnam. You will analyze data to detect and prevent fraud, prepare labeled datasets for ML, and deliver timely fraud analysis that improves both customer satisfaction

Qualifications

• Understanding of KYC/AML processes with at least one year of hands-on experience as Fraud/Risk Analyst, Identity Verification Specialist, KYC/AML Analyst or Trust & Safety Specialist.
• Experience with document verification of identity documents, proof of address, and related KYC documentation.
• Industry experience in fintech, crypto, banking, payment systems, marketplace, etc.
• Knowledge of common fraud patterns and detection tools.
• Understanding of fraud models and ML concepts.

Responsibilities

• Detect and prevent fraud by analyzing suspicious signs in the data.
• Prepare and label fraud datasets for machine learning.
• Deliver high-quality, timely fraud analysis while enhancing customer satisfaction and fraud detection frameworks.

Skills

KYC/AML knowledge

Fraud data analysis

Document verification

Fintech/crypto domain knowledge

Fraud detection tools

ML concepts

Job description

Cybertexex is a global provider of automated KYC, watchlist screening and behavior analytics solutions. We are expanding our team and hiring an Anti-Fraud Officer to protect the product and its users from fraud while supporting business growth and maintaining positive customer experience.

This is a fully remote position based in Vietnam. You will analyze data to detect and prevent fraud, prepare labeled datasets for ML, and deliver timely fraud analysis that improves both customer satisfaction