Manager, Financial Crimes Analytics

Summary The Manager, Financial Crimes Analytics is responsible for designing, testing, tuning, and optimizing financial crimes detection models across Anti-Money Laundering (AML), Fraud, Know Your Customer (KYC), and Sanctions programs. As a senior individual contributor, the role provides technical leadership while remaining hands-on in surveillance model development, statistical analysis, threshold calibration, model efficacy testing, and AI/ML optimization. The position partners with Financial Crimes stakeholders, Technology, Model Risk Management, and other business partners to strengthen detection effectiveness, establish consistent testing practices, and support scalable model capabilities.

Essential Duties and Responsibilities ·         Serve as the technical lead for complex surveillance model design, development, testing, and optimization across Financial Crimes domains. ·         Design, code, test, and refine detection scenarios, statistical models, thresholds, segmentation, and risk-scoring methodologies. ·         Lead above-the-line (ATL), below-the-line (BTL), back-testing, and model efficacy assessments to identify coverage gaps and tuning opportunities. ·         Develop and monitor model performance metrics, health indicators, and early-warning processes; investigate performance deterioration and recommend corrective action. ·         Configure, test, tune, and optimize financial crimes detection platforms, including Fiserv Financial Crime Risk Management (FCRM) and LexisNexis Bridger; support integrations with Fiserv Payments Exchange, PEP+, Signature, and KYC platforms such as Fenergo. ·         Apply statistical methods to model development, feature selection, tuning, validation support, and performance evaluation. ·         Establish consistent standards for model testing, tuning, documentation, change control, and reproducible analytical results. ·         Perform hands-on root cause analysis of missed or non-detected activity, false positives, and model/data defects. ·         Partner with Model Risk Management and stakeholders on model governance, independent validation, audit, regulatory, and legal requests. ·         Present model performance findings, technical recommendations, and risk-based tradeoffs to Financial Crimes leadership. Knowledge of ·         Financial Crimes detection and regulatory expectations across AML, Fraud, KYC, and Sanctions. ·         Surveillance model lifecycle, scenario logic, threshold calibration, segmentation, and model risk governance. ·         Statistics, predictive analytics, machine learning, and model performance measurement. ·         Financial crimes and screening platforms such as Fiserv FCRM, LexisNexis Bridger, and Fenergo, as well as Fiserv Payments Exchange, PEP+, and Signature and their related data flows and integrations. ·         Data quality, feature engineering, and analytical testing methodologies. Skill in ·         Advanced SQL, Python, statistical analysis, and model development/testing frameworks. ·         Developing and tuning rules-based, statistical, and AI/ML detection models. ·         ATL/BTL testing, back-testing, sensitivity analysis, and detection efficacy assessment. ·         Analyzing large datasets, engineering/modeling features, and diagnosing model performance issues. ·         Hands-on configuration, scenario/rule tuning, testing, troubleshooting, and performance optimization of Fiserv FCRM, LexisNexis Bridger, and comparable financial crimes tools. ·         Communicating complex analytical findings to technical, business, and governance stakeholders. Ability to ·         Balance hands-on model development with technical leadership and cross-domain priorities. ·         Independently