Risk Manager II, Travel and Expense Reimbursement Audit (TERA)
Amazon (AI roles) · Hyderabad, Telangana, IND
No salary listed. Estimated from 434 salary-disclosed Operations roles on this board: $150K–$226K (interquartile range; estimate, not the employer's figure).
Apply at Amazon (AI roles)Find your warm intro on LinkedInAbout the role
Amazon's Travel & Expense Reimbursement Audit (TERA) team is seeking a Risk Specialist II (L5) who combines deep investigative expertise with a builder's mindset and a relentless focus on operational excellence. This role is pivotal to TERA's automation roadmap — you will investigate complex expense fraud, abuse, and waste (FAW) cases with rigor and depth, independently design and prototype scalable, user-friendly investigation tools using AI, and systematically eliminate non-productive work that drains investigator capacity.
A core mandate of this role is to raise investigation quality while reducing wasted effort — by identifying and eliminating false positives in detection rules, streamlining multi-touch-point processes into efficient single-pass workflows, and building the tooling and frameworks that make every investigator more effective.
This role sits at the intersection of domain expertise and technology — you will be the bridge between what investigators need and what automation can deliver, with a constant eye on removing friction and non-value-add work from the investigation lifecycle.
Key job responsibilities
- Conduct end-to-end deep-dive investigations of complex FAW cases across transactional data, OCR receipts, badge scans, and travel records; identify patterns, perform root-cause analysis, and deliver evidence-based findings.
- Measure and test risk controls to inform program-level risk assessment; identify gaps in methodology and escalate with recommended solutions.
- Serve as the domain SME for your investigation area — stay current on fraud trends, regulatory changes, and industry best practices and apply insights across teams.
- Analyze detection rule outputs to systematically reduce false positives through threshold calibration, exception logic, and contextual filters — ensuring investigators focus on genuine risk signals.
- Define and own investigation quality frameworks and metrics (QC scores, rework rates, disposition accuracy); drive improvement through data-driven feedback loops and root-cause analysis.
- Map investigation workflows to identify redundant handoffs and multiple touch points; re-engineer into streamlined single-pass processes that reduce cycle time and cognitive load.
- Quantify non-productive work and implement prevention mechanisms — automated pre-filters, smart routing, templated outputs — tracking productivity gains against benchmarks.
- Design and build scalable, user-friendly investigation tools, dashboards, and workflow interfaces; own the full lifecycle from pain-point identification to adoption.
- Partner with engineering and product teams to translate investigator needs into technical requirements, roadmap inputs, and production-ready solutions.
- Independently build prototypes and proof-of-concepts using generative AI, LLMs, prompt engineering, and no-code/low-code platforms to solve investigation challenges.
- Support Astra/Virtual Investigator testing, develop and tune Dexter detection rules, and validate SOP Automation outputs to ensure accuracy and consistency.
- Work across engineering, product, data science, and operations teams; maintain SOPs and documentation; mentor peers and deliver risk management training.
About the team
TERA operates within Amazon's Global Risk & Compliance organization, responsible for safeguarding the company's Travel & Expense ecosystem. The team investigates potential fraud, abuse, and waste in employee expense submissions across all geographies.
TERA is in a transformational phase, building AI and automation programs to dramatically scale investigation capacity while improving quality and eliminating inefficiency. - 5+ years of fraud audits/risk investigations experience
- Experience effectively communicating complex concepts through written and verbal communication
- Experience handling confidential information and maintaining professionalism in dealing with senior executives, or experience with end-to-end project management
- Bachelor’s degree in Finance, Accounting, Business, Economics, or a related field.
- Demonstrated ability to manage multiple concurrent investigations with attention to detail and adherence to deadlines.
- Proficiency in data analysis using Excel, AI/ML tools (e.g., generative AI, LLMs, prompt engineering, no-code/low-code AI platforms)
A core mandate of this role is to raise investigation quality while reducing wasted effort — by identifying and eliminating false positives in detection rules, streamlining multi-touch-point processes into efficient single-pass workflows, and building the tooling and frameworks that make every investigator more effective.
This role sits at the intersection of domain expertise and technology — you will be the bridge between what investigators need and what automation can deliver, with a constant eye on removing friction and non-value-add work from the investigation lifecycle.
Key job responsibilities
- Conduct end-to-end deep-dive investigations of complex FAW cases across transactional data, OCR receipts, badge scans, and travel records; identify patterns, perform root-cause analysis, and deliver evidence-based findings.
- Measure and test risk controls to inform program-level risk assessment; identify gaps in methodology and escalate with recommended solutions.
- Serve as the domain SME for your investigation area — stay current on fraud trends, regulatory changes, and industry best practices and apply insights across teams.
- Analyze detection rule outputs to systematically reduce false positives through threshold calibration, exception logic, and contextual filters — ensuring investigators focus on genuine risk signals.
- Define and own investigation quality frameworks and metrics (QC scores, rework rates, disposition accuracy); drive improvement through data-driven feedback loops and root-cause analysis.
- Map investigation workflows to identify redundant handoffs and multiple touch points; re-engineer into streamlined single-pass processes that reduce cycle time and cognitive load.
- Quantify non-productive work and implement prevention mechanisms — automated pre-filters, smart routing, templated outputs — tracking productivity gains against benchmarks.
- Design and build scalable, user-friendly investigation tools, dashboards, and workflow interfaces; own the full lifecycle from pain-point identification to adoption.
- Partner with engineering and product teams to translate investigator needs into technical requirements, roadmap inputs, and production-ready solutions.
- Independently build prototypes and proof-of-concepts using generative AI, LLMs, prompt engineering, and no-code/low-code platforms to solve investigation challenges.
- Support Astra/Virtual Investigator testing, develop and tune Dexter detection rules, and validate SOP Automation outputs to ensure accuracy and consistency.
- Work across engineering, product, data science, and operations teams; maintain SOPs and documentation; mentor peers and deliver risk management training.
About the team
TERA operates within Amazon's Global Risk & Compliance organization, responsible for safeguarding the company's Travel & Expense ecosystem. The team investigates potential fraud, abuse, and waste in employee expense submissions across all geographies.
TERA is in a transformational phase, building AI and automation programs to dramatically scale investigation capacity while improving quality and eliminating inefficiency. - 5+ years of fraud audits/risk investigations experience
- Experience effectively communicating complex concepts through written and verbal communication
- Experience handling confidential information and maintaining professionalism in dealing with senior executives, or experience with end-to-end project management
- Bachelor’s degree in Finance, Accounting, Business, Economics, or a related field.
- Demonstrated ability to manage multiple concurrent investigations with attention to detail and adherence to deadlines.
- Proficiency in data analysis using Excel, AI/ML tools (e.g., generative AI, LLMs, prompt engineering, no-code/low-code AI platforms)
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