Hiring?Get matched with relevant candidates. Pay only when you hire.Request relevant candidates.
P
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
ProGigFinder Press
Prepare your next application.
Generate and review a CV tailored to your chosen role for $2.99. Card checkout is available where mobile money is unsupported. You can apply without buying this tool.
Get my CV for this job: $2.99
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.