Risk Operations AI Specialist
Listing location: Asia / Australia, Brisbane / Australia, Melbourne / Australia, Sydney / Taiwan, Taipei / Thailand, Bangkok / Hong Kong / Indonesia, JakartaFull-TimeJunior
Salary not disclosed
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Job Details
- Languages
- Bilingual English/Mandarin
- Experience
- 2+ years
- Required Skills
- Written communicationPrompt Engineering
Requirements
- 2+ years in fraud review, risk operations, or KYC/KYB case management
- Hands-on experience with ATO (Account Takeover) detection and investigation
- Comfort working in AI-augmented workflows: you read model outputs, interpret confidence scores, and make decisions based on AI-generated signals rather than raw data alone
- Structured, precise written communication: you can document a root-cause annotation in <3 sentences that an ML engineer can act on
- Bilingual English/Mandarin is required to be able to coordinate with overseas partners and stakeholders
- An operator's mindset toward AI tools: you leverage AI signals to make faster, better decisions — you master the tool rather than compete with it
Responsibilities
- Review cases escalated by ROCA where confidence score falls below the auto-decision threshold, using AI-assisted signals within the unified case review UI (no multi-system switching)
- Make accurate pass / block / escalate decisions on a daily queue spanning KYC Audit, ATO investigation, Reset 2FA, POA Review, and Withdrawal anomaly case types etc.
- Escalate Key User cases (Priority ≥ 75) and high-risk patterns to Senior Agent / Team Lead with clear rationale documentation
- Maintain SLA compliance (<4h manual resolution time) while sustaining decision accuracy ≥ 98%
- Label every reviewed case as TP / TN / FP / FN with structured root-cause annotations — this data directly feeds ROCA's continuous learning pipeline
- Identify recurring FP/FN patterns and route them through the correct feedback path
- Actively contribute to improving Precision (>95%) and Recall (>99%) targets by surfacing systematic signal failures to Data and Model teams
- Participate in regular AI model calibration sessions: review misclassification batches, validate ground truth, challenge decision thresholds with data
- Maintain and update Confluence SOP pages for assigned case types; SOP coverage rate target ≥ 80% (directly impacts ROCA first-pass accuracy)
- Flag SOP coverage gaps when ROCA's reasoning chain reveals unhandled scenarios; draft SOP additions for Team Lead review
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