Manager, Financial Crimes Analytics

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 lead complex model initiatives without direct people-management authority.

• Translate emerging financial crime risks into measurable detection strategies and model enhancements.

• Exercise sound judgment on detection effectiveness, operational impact, and model risk.

• Drive consistent testing standards, continuous tuning, and knowledge sharing across domains.

Education / Previous Experience

• Bachelor’s degree in Data Science, Statistics, Computer Science, Mathematics, Finance, Business, or a related field.

• Six (6) or more years of experience in financial crimes analytics, model development, data science, quantitative analytics, or related work in financial services, FinTech, or consulting.

Licenses / Certifications

• Relevant data science, machine learning, cloud, or financial crimes certifications are a plus but not required.