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Technical Financial Crime Manager
Paystack
Posted 2 months agoLagos, Nigeria
Location
Lagos, Nigeria
Job Type
Full-time
Experience
Senior
Category
Other
Job Description
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
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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).
- 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.
- 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 tooling evolves in line with transaction growth, new products, and new markets.
- Operational Excellence:
- Build and continuously improve operational processes, SLAs, KPIs, and quality frameworks across Fraud and AML teams.
- Use data and metrics to manage performance, capacity, and outcomes, ensuring teams operate efficiently and effectively.
- Identify gaps, risks, and inefficiencies, leading initiatives to strengthen controls and scale operations sustainably.
- Balance speed, quality, regulatory expectations, and customer experience in day-to-day decision-making.
- Cross-Functional & Executive Collaboration:
- Work closely with Product, Engineering, Data, Risk, Compliance, Legal, and Customer Operations.
- Influence roadmap priorities related to fraud, AML, and financial crime tooling.
- Provide clear updates to senior stakeholders on operational performance, risks, and emerging issues
- Required:
- 7+ years in financial crime roles in payments, fintech, banking, or financial services.
- Strong technical expertise in data analysis, including advanced SQL and experience working with large, complex datasets.
- Expert Python skills, including experience with libraries such as pandas, NumPy, scikit-learn, statsmodels, and/or model pipelines.
- Proven experience designing, building, and tuning risk detection systems (fraud, AML, or similar).
- Solid understanding of statistical modelling, machine learning, and/or time-series forecasting, with experience deploying models into production or operational workflows.
- Ability to translate data insights into operational detection logic used by investigators and automated systems.
- Experience measuring and optimising detection performance using quantitative metrics.
- Strong systems thinking: able to design scalable, maintainable monitoring frameworks rather than one-off rules.:
- Deep understanding of financial crime typologies, fraud patterns, AML/CTF requirements, and regulatory obligations.
- Experience operating within fraud, AML, risk, or compliance functions in payments, fintech, or financial services.
- Proven experience leading and developing teams, including setting direction, coaching, and performance management.
- Ability to balance technical depth with practical operational decision-making.
- Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders.
- Preferred:
- Experience with dbt and modern analytics stacks.
- Experience with version control systems (GitHub).
- Experience with AI-assisted tooling or advanced analytics platforms.
- Familiarity with monitoring platforms, alerting systems, transaction screening, and case management tools.
- Experience working with OLTP (MySQL/PostgreSQL/SQL Server), OLAP (Redshift/BigQuery/Snowflake), and NoSQL (MongoDB) databases.
- Industry certifications such as ACAMS, ICA, CFE, CFCS, or similar.
