Job Purpose Statement

The Senior Manager, Transactions & Behaviour Analytics designs, executes and continuously improves the transaction monitoring detection framework across all Loop DFS markets, platforms and product lines. The role owns scenario coverage, rule and threshold optimisation, behavioural analytics and the integration of AI-assisted detection agents into the surveillance estate. The Senior Manager leads a team of Specialists and operates as the technical authority for detection design within the TM&R vertical, working in close partnership with Engineering, Product and 2nd Line counterparts (MLRO, Fraud, Risk & Compliance). 

Key Accountabilities (Duties and Responsibilities)

Detection Design & Rule Optimisation — 40%

• Scenario Coverage: Maintain a documented mapping of detection scenarios to product risk and AML/CFT typologies across consumer wallet, merchant acquiring, card and partner-ecosystem flows in all Loop DFS markets.
• Rule & Threshold Tuning: Design, back-test and govern detection rules and thresholds. Maintain a healthy detection-to-false-positive ratio; document all rule changes for audit.
• Behaviour Analytics: Build and operate behavioural models that surface patterns static rules cannot reach — including AI-assisted detection agents deployed as digital peers within the team.

Team Leadership & Cross-Functional Delivery — 25%

• Team Build & Mentorship: Lead, mentor and develop Specialists in Transactions & Behaviour Analytics across markets. Build technical capability and a strong analytical culture.
• Engineering & Product Partnership: Work with Engineering to operationalise rules and analytics into the production monitoring estate. Work with Product to ensure new products and flows are surveillance-ready at launch.
• Vendor & Tooling Management: Manage transaction monitoring platform vendors and specialist analytics tools, including the digital peer agent estate.

Reporting & Audit Support — 20%

• Reporting: Produce detection-related MI for the Head TM&R and executive forums. Prepare analytical reports for management, audit and regulators when required.
• Audit Support: Support Internal Audit, external audit and Risk & Compliance engagements on all detection-related matters.
• Regulatory Awareness: Maintain current knowledge of FATF Recommendations and AML/CFT regulatory frameworks across Loop DFS markets.

Innovation, Automation & Continuous Improvement — 15%

• Digital Peer Agents: Operate and extend AI-assisted detection agents as a scalable workforce model — extending reach without proportional headcount growth.
• New Data Sources & Techniques: Identify and integrate new internal and external data signals, analytical techniques and detection patterns.
• Performance Optimisation: Drive measurable improvements in detection rate, alert quality and analyst productivity.

Job Specifications

Education & Qualifications

• Bachelor’s degree in a quantitative or analytical discipline (Statistics, Data Science, Computer Science, Mathematics, Economics or related field).
• Professional certification (CAMS, CFE, ICA) preferred.

Experience

• 5+ years in financial services in a transaction monitoring, fraud analytics or financial crime investigations role.
• Strong hands-on data analytics skills — SQL essential; Python or R highly desirable.
• Experience with transaction monitoring platforms (tool-agnostic).
• Solid understanding of AML/CFT typologies and regulatory requirements across Loop DFS markets.
• Experience working with AI/ML detection capabilities is a strong advantage.
• Demonstrated experience writing and modifying SQL queries against large transaction datasets.

Technical Specifications

Financial Crime & Regulatory Knowledge

• Strong understanding of AML/CFT laws, regulations and typologies relevant to mobile money, card acquiring and eCommerce.
• Working knowledge of regulatory frameworks across Loop DFS markets.

Data Analytics & Detection Engineering

• Advanced SQL; proficient Python or R for analytics and rule prototyping.
• Experience configuring and tuning transaction monitoring platforms.
• Comfortable with AI/ML detection approaches and the operational discipline around them.

Scenario & Risk Methodology

• Strong scenario design discipline grounded in typology and product risk.
• Able to balance detection coverage against false-positive load with documented evidence.

Investigations Support

• Able to support case investigations with analytical evidence packs and pattern explanations.