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

Translating risk, regulatory, and business requirements into scalable detection systems

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.
• Translate complex datasets and domain insights into actionable detection logic embedded in monitoring and alerting platforms.
• Establish feedback loops between investigation outcomes and detection logic to continuously improve signal quality.
• Measure and manage detection performance using quantitative metrics (precision, recall, false positives, alert-to-case conversion, loss metrics).

Maintain structured, auditable documentation of rules, logic, assumptions, and changes.

Data Analysis, Modelling & Insights

• Analyse large, complex transactional and behavioural datasets to identify emerging fraud and AML risks across markets.
• 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.

Conduct trend analysis, root cause analysis, and deep dives on losses, typologies, and control gaps.

Financial Crime Oversight

• Own the end-to-end fraud and AML detection domain, ensuring alignment between prevention, detection, investigation, and remediation.
• Apply deep understanding of fraud typologies, AML/CTF risks, sanctions, and regulatory expectations to detection design.
• Manage the Fraud and AML operational teams (specialists and first-line managers) to ensure adequate coverage, capability and day-to-day execution. 
• Translate regulatory, partner, and audit requirements into scalable technical and operational controls.
• Stay ahead of evolving financial crime patterns, market-specific risks, and regulatory developments across Paystack’s footprint.

Tooling, Automation & Scale

• Partner with Product and Engineering to embed detection logic into core systems and improve monitoring, alerting, and case management tooling.
• Drive automation initiatives to reduce manual effort, improve consistency, and enable scale without compromising control quality.
• Identify and prioritise enhancements to monitoring platforms, workflows, and data pipelines.
• Ensure fraud and AML tooli