AML Models Specialist
Listed on 2026-01-03
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Finance & Banking
Financial Compliance, Regulatory Compliance Specialist
Summary
As a member of the Meta Fin Tech Compliance (MFTC) Anti-Money Laundering (AML) Models & Rules Team, you will drive efforts related to designing, developing, validating, and maintaining automated controls (Models, Rules, Triggers) to effectively mitigate inherent AML risks across our Family of Apps and suite of payments products. You will work collaboratively with a dynamic group of cross‑functional stakeholders including, but not limited to, Engineering, Legal, Product, Integrity, and other Compliance functions.
You will leverage Meta’s vast resources to create innovative, scalable, and highly effective AML technical solutions, along with supporting processes and governance to align with regulatory obligations set forth by all relevant Anti‑Money Laundering/Counter‑Terrorist Financing (AML/CTF) laws, protect Meta’s platforms from being abused by bad actors, and continue to enable and foster a safe environment for our users.
Meta Payments Inc. is committed to complying with Anti‑Money Laundering/Counter‑Terrorist Financing (“AML”/“CTF”) obligations imposed by federal, state, foreign, and other applicable laws. These governing laws and regulations may include without limitation the following: the Bank Secrecy Act (“BSA”); the USA PATRIOT Act of 2001 (“Patriot Act”); the rules and regulations overseen by the United States Department of the Treasury’s Office of Foreign Assets Control (“OFAC”);
and other requirements applicable to registered money services businesses (“MSBs”). Meta Payments maintains a risk‑based Policy and Program designed to ensure compliance by Meta Payments and its affiliates with governing law.
1) identifying and understanding inherent AML risks with Meta’s payments products;
2) deriving business and technical requirements to sufficiently and precisely mitigate inherent AML product risks;
3) collaborating effectively with cross‑functional partners to complete the end‑to‑end automated control delivery process.
1) identifying and proposing modifications to address new/emerging patterns, risks, and external factors that may impact the productivity of automated controls;
2) routinely assessing opportunities and proposing changes to enhance control effectiveness and efficiency;
3) executing threshold tuning exercises based on statistical quantitative analysis and supporting testing to ensure controls are operating efficiently and effectively.
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