Financial Crimes Risk Signal Analyst

About the role

The Financial Crimes Risk Signal Analyst specializes in evaluating alerting signals, data inputs, and risk indicators used across fraud and BSA/AML/OFAC detection systems. The role ensures signals are performing as intended, validates data quality, and supports reporting that informs model and rule refinement across financial crime programs.

Responsibilities

• Researches and validates risk signals used in transaction monitoring, fraud scoring, and rule-based alerting.
• Ingests, cleanses, transforms, and validates data from diverse sources to support analytics, dashboards, and enabled models.
• Supports end-to-end life cycle of alerting quality and effectiveness for Financial Crimes Programs.
• Supports data quality dashboards, scorecards, and alerting mechanisms.
• Conducts data quality assessments, contributes to root-cause analysis, and participates in remediation planning.
• Assists in development of KRIs and KPIs such as detection efficiency, signal precision, and false-positive ratios.
• Maintains structured documentation supporting compliance, audit, model, and financial crimes risk governance.
• Collaborates with Compliance, Enterprise Data, and Risk teams to refine signal thresholds, rule logic, and detection coverage.
• Translates complex analytical concepts into accessible insights for varied technical and business audiences.
• Performs other duties as assigned.

Qualifications

• Bachelor’s degree in Analytics, Information Systems, Finance, Criminal Justice, or related field, required.
• 3-5 years’ experience in fraud/AML analytics, risk signal validation, or operational risk monitoring, required.
• 3-5 years’ experience in BSA/AML compliance, fraud and/or case investigation, or experience in quality assurance/control or internal audit, required.
• Experience with data-driven roles involving large datasets, preferred.
• Experience producing governance-grade validation or audit documentation, preferred.
• Exposure to financial crime domains (AML, KYC, fraud), preferred.
• CAMS and/or CAFP certifications, preferred.

Skills

• Strong analytical and conceptual thinking skills, with the ability to assess risk signals, indicators, and detection logic.
• Proficiency with SQL, PowerBI and comfort navigating transaction-level datasets.
• Ability to translate signal performance issues into actionable insights.
• Understanding of AML/fraud regulatory principles and risk detection concepts.
• Familiarity with visualization tools for presenting findings to non-technical stakeholders.
• High attention to detail, with strong documentation and audit readiness standards.
• Knowledge of data quality domains and data lineage principles.
• Ability to develop and maintain dashboards and report for model health and risk indicators.
• Knowledge of regulatory environment(s) and emerging BSA/AML and fraud trends.
• Strong investigative, written, and oral communication skills.
• Strong commitment to ethics, and the ability to understand a variety of issues and perspectives.
• Understanding of the banking industry, including bank partnerships with fintech companies.
• Multitasks effectively and takes action promptly, both independently and in a team environment.
• Handles highly confidential information with appropriate discretion, and works well in a high volume, fast paced environment.