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Technical Financial Crime Manager

Paystack

Posted 5 months agoLagos, Nigeria

Location

Lagos, Nigeria

Job Type

Full-time

Experience

Senior

Category

Legal, Regulatory, Risk and Compliance

Job Description

Technical Financial Crime Manager About Paystack Over the past nine years, Paystack has established itself as a pioneer in African fintech with a mission to help merchants get paid by anyone, anywhere in the world. Processing over $300 million in monthly transactions, our modern payments infrastructure supports tens of thousands of notable corporations, including MTN, Bolt, and Domino’s Pizza. As we enter a phase of accelerated growth, we are seeking a Technical Financial Crime Manager to own, design, and scale our fraud and AML detection capabilities. This role sits at the intersection of data, engineering, and financial crime operations, with end-to-end accountability for ensuring our monitoring systems are technically robust, domain-accurate, and scalable across multiple markets. This is a hands-on technical leadership role. You will define detection logic, guide system design, and directly influence how financial crime risk is identified and managed at Paystack, while also leading and developing high-performing fraud and AML teams. What You’ll Do As the Technical Financial Crime Manager, you will run the day-to-day fraud and AML detection stack; from data and rules to operational outcomes. You will combine deep technical expertise with financial crime domain knowledge to design effective monitoring systems, manage domain specialists, and ensure Paystack remains a safe, trusted payments platform. You will be accountable for: The technical quality and effectiveness of fraud & AML monitoring logic The operating model and performance of Financial Crime Monitoring teams Key Responsibilities Technical Ownership of Detection & Monitoring Define, build, test, and optimise fraud and AML detection rules, scenarios, thresholds, and models used in production systems. Data Analysis, Modelling & Insights Analyse large, complex transactional and behavioural datasets to identify emerging fraud and AML risks across markets. Financial Crime Ove
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Responsibilities

  • Establish feedback loops between investigation outcomes and detection logic to continuously improve signal quality.
  • Maintain structured, auditable documentation of rules, logic, assumptions, and changes.
  • Conduct trend analysis, root cause analysis, and deep dives on losses, typologies, and control gaps.
  • Apply deep understanding of fraud typologies, AML/CTF risks, sanctions, and regulatory expectations to det.
  • Translate complex datasets and domain insights into actionable detection logic embedded in monitoring and alerting platforms.
  • Measure and manage detection performance using quantitative metrics (precision, recall, false positives, alert-to-case conversion, loss metrics)
  • Design and implement statistical models, machine learning approaches, and/or time-series analysis to enhance detection capabilities.
  • Build and own dashboards and reporting frameworks tracking KPIs, SLAs, alert quality, investigator productivity, and risk outcomes.