Manage: Fraud Data Insights and Analytics

Empowering Africa’s tomorrow, together…one story at a time.

Job Summary

This role sits within the Fraud Strategy function and focuses on data-driven fraud prevention, analytics-based insights, and strategic reporting to enhance decision-making, control design, and fraud loss reduction. The purpose is to lead the design, delivery, and continuous improvement of data-driven insights, analytical models, and fraud intelligence reporting for the Business Banking segment.
The role leverages advanced analytics to detect emerging fraud patterns, optimize controls, and inform strategic fraud risk decisions across products, channels, and customer segments. It ensures that the organization maintains a proactive, intelligence-led approach to fraud prevention and detection.

Job Description

Key Accountabilities

Data Analytics & Insights 

• Develop and implement data analytics frameworks to identify, quantify, and monitor fraud risk trends across Business Banking products and digital platforms. 

• Produce actionable fraud insights, dashboards, and MI for senior management and fraud committees. 

• Conduct deep-dive root-cause and loss analytics to support strategic decision-making. 

• Partner with Fraud Operations to identify control weaknesses and recommend data-informed improvements. 

• Design and maintain data models to track key metrics such as fraud losses, recovery rates, and prevention ROI. 

Advanced Analytics & Modelling 

• Apply predictive analytics and machine learning to detect anomalous behaviour, identify emerging fraud threats, and enhance early-warning systems. 

• Collaborate with data science teams to develop, test, and calibrate fraud risk models. 

• Evaluate performance of existing rules and models, ensuring optimal detection vs. false-positive rates. 

• Support digital and product teams with data-driven fraud control design for new initiatives. 

Fraud Intelligence & Reporting 

• Manage end-to-end fraud data collection, validation, and integrity across systems. 

• Build automated fraud reporting pipelines (Power BI, Tableau, or other visualization tools). 

• Translate complex data into clear narratives and visual storytelling for business and executive audiences. 

• Contribute to monthly, quarterly, and ad hoc fraud performance reports to committees and regulators. 

Collaboration & Stakeholder Management 

• Partner with internal stakeholders (Fraud Strategy, Fraud Operations, Business Banking Product Heads, Data & Analytics teams) to ensure a unified data approach. 

• Support governance and risk functions with fraud data for audits, risk assessments, and regulatory submissions. 

• Build strong relationships with Group Data, IT, and Cyber teams to align on data standards and security. 

Continuous Improvement 

• Stay current with industry trends, analytics techniques, and fraud typologies. 

• Lead or participate in data innovation projects – e.g., behavioural biometrics, network analytics, or AI-driven detection. 

• Mentor analysts and develop the team’s analytical capability. 

• Contribute to the strategic fraud roadmap by identifying new opportunities for data-led efficiencies.

Preferred Education 

• Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, or Risk Management. 

• Postgraduate qualification (MSc, MBA, or equivalent) advantageous. 

• CFE (Certified Fraud Examiner) advantageous 

• Data Analytics Certifications (SAS, SQL, Python, R, Power BI) 

• Machine Learning or AI certifications (AWS, Google, Microsoft) 

Preferred Experience 

• 7–10 years’ experience in data analytics, business intelligence, or fraud analytics (preferably in financial services or business banking). 

• Proven track record in data modelling, dashboard design, and analytical problem-solving. 

• Exposure to fraud risk management frameworks, digital banking, and payments ecosystems. 

• Experience using big data environments (e.g., Hadoop, Spark, Databricks) and cloud analytics platforms. 

Knowledge and Skills 

• Analytical Thinking - strong data interpretation, correlation, and pattern recognition skills. 

• Technical Proficiency - expert in SQL, Python/R, BI tools (Power BI, Tableau), and data visualization. 

• Fraud Knowledge - understanding of fraud schemes, typologies, and control environments in Business Banking. 

• Innovation & Problem Solving - ability to use advanced analytics to anticipate and mitigate fraud threats. 

• Communication - converts technical data insights into clear business narratives. 

• Collaboration - works effectively with cross-functional teams (Fraud, Risk, Product, Data). 

• Attention to Detail - ensures accuracy and reliability of data outputs and reports. 

• Business Acumen - understands financial products, customer journeys, and revenue impact of fraud losses. 

Education

Postgraduate Degrees and Professional Qualifications: Mathematics

Absa Bank Limited is an equal opportunity, affirmative action employer. In compliance with the Employment Equity Act 55 of 1998, preference will be given to suitable candidates from designated groups whose appointments will contribute towards achievement of equitable demographic representation of our workforce profile and add to the diversity of the Bank.

Absa Bank Limited reserves the right not to make an appointment to the post as advertised