AI Fraud & Financial Crime Analyst

Santriya Technologies; United States; Remote; USD 80,000 - 140,000; Full time

Job summary

Santriya Technologies Ltd. seeks an AI Fraud & Financial Crime Analyst in the United States to investigate fraud and financial-crime alerts using AI/ML risk signals and transaction data, while maintaining human accountability for customer-impacting decisions.

You will review alerts and activities, apply AML and fraud procedures, escalate when necessary, and contribute to post-incident reviews. This role requires strong analytical thinking and strict data confidentiality.

Responsibilities

• Review alerts, cases, customer activity, device/payment signals and related evidence.
• Use AI to summarize complex activity, link entities and surface relevant typologies, verifying every material fact in source systems.
• Differentiate model score from evidence and document rationale for closure, restriction or escalation.
• Apply AML, sanctions, fraud, scam and customer-protection procedures relevant to jurisdiction and product.
• Escalate suspicious activity and prepare clear investigation narratives for authorized reporting teams.
• Identify false-positive drivers, emerging fraud patterns and control gaps and feed them to model/rules teams.
• Observe strict confidentiality, access and need-to-know controls.
• Participate in QA, calibration and post-incident review.

Job description

Job Summary

Santriya Technologies is seeking an AI Fraud & Financial Crime Analyst to investigate fraud and financial-crime alerts using AI/ML risk signals, transaction data and documented typologies while retaining human accountability for customer-impacting decisions and escalation.

Product & documentation operating model: You will be assigned one or more company products, client solutions or operational services. The approved product documentation, SOPs, policies, process maps, knowledge articles, release notes, controls, scripts and AI runbooks are the source of truth. Use only approved enterprise AI tools to retrieve, summarize, draft, classify, prioritize or automate work. Validate material AI outputs against current documentation and authoritative system data, record exceptions, and escalated conflicts or missing guidance. Do not invent product features, prices, customer entitlements, engineering limits, regulatory positions or safety instructions.

Key Responsibilities

• Review alerts, cases, customer activity, device/payment signals and related evidence.
• Use AI to summarize complex activity, link entities and surface relevant typologies, verifying every material fact in source systems.
• Differentiate model score from evidence and document rationale for closure, restriction or escalation.
• Apply AML, sanctions, fraud, scam and customer-protection procedures relevant to jurisdiction and product.
• Escalate suspicious activity and prepare clear investigation narratives for authorized reporting teams.
• Identify false-positive drivers, emerging fraud patterns and control gaps and feed them to model/rules teams.
• Observe strict confidentiality, access and need-to-know controls.
• Participate in QA, calibration and post-incident review.

AI-enabled ways of working

• Use approved GenAI copilots, enterprise search/RAG, analytics and workflow agents to reduce repetitive work and improve decision preparation.
• Keep prompts, outputs and automated actions within approved data-access, confidentiality and retention rules.
• Correct inaccurate summaries, classifications or recommendations before they become customer, operational or system records.
• Feed recurring AI errors, knowledge gaps and process friction into product, documentation and control improvement.

Human accountability

AI is a copilot, not the accountable decision-maker. The role holder remains responsible for judgement, quality, privacy, security and appropriate escalation. Credit, fraud blocking, legal/regulatory, financial advice, safety-critical, clinical, employment and other high-impact decisions must follow delegated authority and required human review.

Success Measures

• Investigation quality and timeliness improve
• False positives reduce without missed risk
• Decision rationale is audit-ready
• Emerging threats are fed back into controls