Analyst: Fraud Strategy

• Analyse fraud data to identify trends, emerging risks, control weaknesses and opportunities to improve fraud prevention and detection.
  
• Support the development, implementation and monitoring of fraud strategies across products, channels and fraud systems.
  
• Monitor application and transaction fraud performance, including fraud losses, customer impact, operational effectiveness and residual risk.
  
• Assess the effectiveness of fraud detection systems, rules and controls.
  
• Support fraud forecasting, reporting, loss tracking and prevention monitoring.
  
• Conduct pre - and post -implementation analysis for strategy changes, including impact assessments, test -and -learn initiatives and benefits tracking.
  
• Perform root -cause analysis on fraud events, control gaps and emerging threats.
  
• Maintain strong data quality, integrity and reconciliation practices.
  
• Translate analytical findings into practical recommendations for business and technical stakeholders.
  
• Contribute to the continuous improvement of fraud strategy, analytics, reporting and system capability.
  

• Effective monitoring and optimisation of application and transaction fraud strategies.
  
• Timely reporting and analysis of fraud performance, losses, preventions, alerts and operational metrics.
  
• Improved visibility of fraud risks, trends, customer impact and control effectiveness.
  
• Accurate, decision -useful management information and governance reporting.
  
• Actionable recommendations that reduce fraud losses, strengthen controls and support risk appetite objectives.
  
• Strong support of strategic fraud initiatives, system enhancements and advanced analytics opportunities.
  

Requirements

• Strong analytical skills with the ability to work with large, complex datasets.
  
• Proficiency in SQL and strong Excel capability.
  
• Understanding of statistical, modelling and analytical techniques.
  
• Ability to communicate complex analytical findings to technical and non -technical audiences.
  
• Exposure to Python, R, SAS, Power BI, Tableau or similar tools.
  
• Understanding of fraud risk, credit risk, customer lifecycle management and financial services products.
  
• Knowledge of fraud strategy monitoring, fraud controls, decisioning systems and performance reporting.
  
• Ability to assess rule effectiveness, alert quality, false positives, missed fraud and operational impacts.
  
• Knowledge of, or ability to quickly learn, application and transaction fraud typologies.
  
• Experience supporting strategy reviews, impact assessments and root -cause analysis.
  

• Strong analytical and problem -solving skills.
  
• Intellectual curiosity and continuous learning mindset.
  
• Action -oriented with strong delivery focus.
  
• Effective stakeholder engagement and collaboration skills.
  
• Strong communication and influencing capability.
  
• Sound judgement and data -driven decision making.
  
• Commercial awareness with a customer -centric approach.
  
• High levels of integrity, resilience, adaptability and self -leadership.
  

• Bachelor's degree in Finance, Economics, Statistics, Mathematics, Data Science, Actuarial Science, Engineering, Computer Science or a related quantitative discipline.
  

• Postgraduate qualification in a quantitative, analytics, risk or business -related field.
  
• Relevant fraud, risk, analytics, data science or business intelligence certifications.
  
• Exposure to SQL, Python, R, SAS, Power BI, Tableau or similar tools.
  

• Experience analysing data using SQL, SAS, Python, R, Excel or similar tools.
  
• Experience working with large datasets, data quality assessments and reconciliations.
  
• Ability to communicate analytical insights clearly to diverse stakeholders.
  
• Ability to manage multiple priorities and deliver high -quality outputs independently.
  

• Experience in fraud risk, credit risk, collections, recoveries, financial crime, customer analytics, decisioning, reporting or business intelligence.
  
• Experience within financial services, retail credit, banking, payments, cards or lending.
  
• Exposure to fraud detection systems, rules engines, transaction monitoring or decisioning platforms.
  
• Experience supporting strategy monitoring, governance reporting and control performance reviews.
  

• High level of confidentiality, integrity and judgement when working with customer, fraud, transactional and business -sensitive data.
  
• Ability to work with ambiguity, investigate data movements, ask the right questions and translate findings into practical fraud strategy recommendations.
  
• Strong attention to detail, with the ability to produce accurate, evidence -based outputs suitable for management, governance and audit -related discussions.
  
• Willingness to continuously build fraud, analytics, systems and business knowledge in line with the evolving WFS fraud strategy agenda.