melvin sanchez

I’m a fraud and risk analytics-focused customer operations professional, currently running advanced fraud and anomaly analysis across “high-volume messaging clusters and CDR datasets” to identify emerging risk patterns and reduce client exposure. I use SQL to extract and analyze structured datasets, evaluating fraud trends, detection performance, and rule effectiveness.

I optimize “rule-based detection mechanisms” to reduce false positives and improve fraud capture precision, and I reclassify fraud categories to strengthen prevention policies. I also generate data-driven risk assessment reports on fraud rate fluctuations and operational impact, then partner with international teams to support blocking decisions and mitigation strategies.

Before this role, I worked as a COE T2 & IRT Urgent Specialist in Trust & Safety Investigations at Uber, analyzing behavioral and transactional data to detect fraud, evaluate enforcement accuracy, and escalate systemic vulnerabilities. Earlier, I handled “inbound and outbound collections” and dispute/irregular payment investigations while producing audit-ready documentation and maintaining compliance with financial regulations.