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 Dominos 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 Youll 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 Paystacks 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

Support audits partner reviews and regulatory engagements as a subject matter expert.

Who Were Looking For

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.

High ownership mindset and comfort operating in ambiguous high-growth environments.

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.

Why This Role Matters

This role is foundational to Paystacks ability to scale safely. You will define how financial crime detection works at Paystack combining strong technical systems with sound domain judgment. Success in this role directly protects customers merchants partners and the broader financial ecosystem while enabling Paystacks continued growth across Africa and beyond.

This role is open to candidates based in Nigeria Ghana Kenya or South Africa