Financial Crime Lead — HitPay

Location: Singapore or remote SEA · Team: Compliance — Financial Crime · Reports to: Group Head of Compliance & Group MLRO · Manages: 2 analysts (TM + Screening · Fraud & Risk) · Type: Full-time \## About HitPay HitPay is a full-stack payments platform serving 15,000+ SMEs across Southeast Asia and beyond, regulated in six jurisdictions (MAS, BSP, BNM, AUSTRAC, FinCEN, NZ DIA). Our financial-crime function is AI-native: automated monitoring does the first pass across the merchant book and surfaces a prioritised action queue with the evidence attached. Humans don't hunt for suspicion; they judge it. \## The role You own everything after detection. The machine produces the queue; your team of two — one seat on transaction-monitoring + screening alerts, one seat on fraud & merchant risk — dispositions it, and you own the outcome: every alert decided within SLA, every escalation defensible, every STR that should be filed prepared and handed cleanly to the MLRO office, every fraud loss that was preventable prevented. You are the single accountable owner for financial-crime operations across both disciplines, and the person the CEO and Group MLRO ask: "are we on top of it?" \## What you'll own \- The daily queue, end-to-end: disposition standards, SLAs, and quality across the AML action queue and the screening-alert worklist. You personally take the hardest cases. \- Both disciplines, one team: manage and develop the TM + Screening analyst and the Fraud & Risk analyst — coverage planning (including leave windows), cross-training so neither queue ever has a single point of failure, and workload balancing between the two seats. \- Transaction monitoring outcomes: investigation standards for AML alerts across the typologies you'd expect in a multi-market merchant book, and the STR case-prep pipeline into the MLRO office — evidence packs that are filing-ready. \- Fraud & merchant-risk outcomes: the card-fraud early-warning response loop — from tiered alerts to concrete action (enhanced monitoring, reserve/payout recommendations, offboarding cases), plus merchant-policy violations and website/storefront risk findings. \- Sanctions/screening adjudication quality: true-match vs false-positive decisioning standards on name-screening alerts, with the audit trail regulators expect. \- The calibration loop: your team's dispositions feed back into detection tuning. You own the feedback — which alerts are noisy, which typologies are missed — and keep precision climbing. \- Typology intelligence: maintain the institutional memory of what financial crime looks like in our book, and push it back into detection coverage and onboarding guardrails. \- Self-serve investigation at the data layer: our case evidence lives in a data warehouse, and the best investigators here query it directly — charge patterns, payout flows, login/device signals, cross-merchant links. You write your own SQL to pressure-test an alert, size a typology, or answer a regulator's question, rather than filing a ticket and waiting. \- Interfaces: escalation authority with Payment Ops for holds/pauses; suspicious-onboarding intake from the KYC Ops Lead; operational-standards input from the Deputy MLRO's policy recommendations; case narratives for regulator correspondence and production orders. \## What you'll bring \- 6–10 years in financial-crime operations at a payments company, PSP, EMI, or bank — with real depth in both AML investigations and fraud risk , not just one. You've dispositioned TM alerts and worked card-fraud/chargeback or merchant-risk cases. \- 2+ years leading or supervising investigators/analysts — you've owned a queue SLA, run coverage through leave and turnover, and coached people to better decisions. \- Hands-on STR/SAR case preparation experience — you know what a filing-ready evidence pack looks like and what an FIU does with it. \- Screening adjudication experience (sanctions/PEP/adverse-media) with a defensible true-match methodology. \- SEA typology fluency: scam proceeds, mule networks, ban evasion/re-onboarding, and merchant-side fraud patterns in SG/MY/PH are your home turf — or you can prove you'd get there in a quarter. \- Technical, hands-on with data — this is a requirement, not a preference: you write your own SQL confidently against a production data warehouse (joins, window functions, cohorting — not just SELECT *), and you reach for a query before you reach for a dashboard. You can trace a merchant's charge/payout history, quantify a pattern across the book, and turn the result into case evidence without an analyst or engineer in the loop. \- AI-forward: at home in a queue a machine triaged, comfortable challenging model output, and you'd rather tune the pipeline than add headcount. Python/scripting for ad-hoc analysis is a plus. \- Decisive under ambiguity: pausing a merchant wrongly costs real money, missing one costs more. You can defend either call in writing. \## Nice to have \- ACAMS / ICA / CFE certification. \- Snowflake (or BigQuery/Redshift) experience — our warehouse is Snowflake. \- Experience with rules-engine or model tuning (thresholds, backtesting, FP-reduction programmes) — you'd be running these yourself here. \- Prior exposure to acquiring-side risk (chargebacks, card-scheme programmes, merchant reserves). \- Experience being the named escalation owner in a regulator inspection or production-order response. \## Why this role is unusual Most financial-crime leads inherit an alert backlog and a headcount ask. Here the detection stack is already built and automated — alerts arrive prioritised with the evidence attached. Your job is the part that actually matters: judgment, standards, and outcomes, with a small team where most institutions would need twenty — and a direct line to the Group MLRO and the CEO.