Odai
Ameera.
Fraud Specialist at Apple transitioning into Data and AI Engineering. I build agentic systems — from AI-powered forensics tools that process thousands of cases, to self-hosted infrastructure where AI agents manage my own digital life. Currently exploring the intersection of agentic AI, personal data sovereignty, and Arabic-first products. If there's a workflow I touch more than twice, I automate it..
About
Fraud Specialist at Apple transitioning into Data and AI Engineering. I build agentic systems — from AI-powered forensics tools that process thousands of cases, to self-hosted infrastructure where AI agents manage my own digital life. Currently exploring the intersection of agentic AI, personal data sovereignty, and Arabic-first products. If there's a workflow I touch more than twice, I automate it..
Projects
Experience
- Prevented an estimated multi-million dollar annual loss by analyzing over 1,200 high-risk fraud cases monthly using pattern recognition on transactional, device, and historical data.
- Decreased exposure to high-volume fraud types by proactively identifying and reporting emerging trends across markets, driving the development of scalable Strategic Data Solutions workflows.
- Maintained a 100% policy compliance rate for all high-risk investigations by producing comprehensive, audit-ready documentation and adhering strictly to risk standards.
- Increased new investigator efficiency by 25% by developing and leading specialized training sessions focused on analysis logic, pattern recognition, and cross-system investigation workflows.
- Resolved complex customer escalations by conducting deep-dive reviews of device/system records, resulting in a significant reduction in repeat issues.
- Collaborated cross-functionally with engineering and logistics teams to address recurring system discrepancies, contributing to operational improvements.
- Investigated multi-airline fraud cases by cross-referencing booking logs, ticketing data, and routing discrepancies
- Coordinated with partner airlines to validate large volumes of booking data and resolve high-impact fraud issues
- Analyzed beneficiary payment datasets to detect irregularities, duplicate accounts, and eligibility fraud
- Contributed to an estimated 10% reduction in aid loss through fraud investigation and field coordination
- Developed new data checks that improved workflow efficiency and audit readiness
