ANALYST: FRAUD STRATEGY
Main Purpose<\/b>
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To support the development, implementation, monitoring and optimisation of Fraud Strategy across WFS, with a focus on using data, analytics, reporting and business insight to minimise fraud risk and fraud losses while balancing customer experience, operational efficiency and business growth.
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The role supports fraud strategy across application fraud and transaction fraud, including the monitoring of fraud decisioning strategies, fraud rules, fraud detection systems, emerging fraud trends, fraud management information and control effectiveness. The analyst will contribute to the continued strengthening of WFS fraud capability across current platforms and future strategic initiatives, including onboarding fraud optimisation, store card transactional fraud, fraud analytics, governance reporting and future fraud system enhancements.
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KEY RESPONSIBILITIES
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- Analyse fraud data to identify trends, emerging risks, control weaknesses and opportunities to improve fraud prevention and detection.<\/span>
<\/li>- Support the development, implementation and monitoring of fraud strategies across products, channels and fraud systems.
<\/li>- Monitor application and transaction fraud performance, including fraud losses, customer impact, operational effectiveness and residual risk.
<\/li>- Assess the effectiveness of fraud detection systems, rules and controls.
<\/li>- Support fraud forecasting, reporting, loss tracking and prevention monitoring.
<\/li>- Conduct pre\- and post\-implementation analysis for strategy changes, including impact assessments, test\-and\-learn initiatives and benefits tracking.
<\/li>- Perform root\-cause analysis on fraud events, control gaps and emerging threats.
<\/li>- Maintain strong data quality, integrity and reconciliation practices.
<\/li>- Translate analytical findings into practical recommendations for business and technical stakeholders.
<\/li>- Contribute to the continuous improvement of fraud strategy, analytics, reporting and system capability.
<\/li><\/ul>KEY OUTCOMES<\/b>
<\/div>- Effective monitoring and optimisation of application and transaction fraud strategies.<\/span>
<\/li>- Timely reporting and analysis of fraud performance, losses, preventions, alerts and operational metrics.
<\/li>- Improved visibility of fraud risks, trends, customer impact and control effectiveness.
<\/li>- Accurate, decision\-useful management information and governance reporting.
<\/li>- Actionable recommendations that reduce fraud losses, strengthen controls and support risk appetite objectives.
<\/li>- Strong support of strategic fraud initiatives, system enhancements and advanced analytics opportunities.
<\/li><\/ul><\/span>Requirements<\/h3>
TECHNICAL COMPETENCIES<\/b>
<\/div>- Strong analytical skills with the ability to work with large, complex datasets.<\/span>
<\/li>- Proficiency in SQL and strong Excel capability.
<\/li>- Understanding of statistical, modelling and analytical techniques.
<\/li>- Ability to communicate complex analytical findings to technical and non\-technical audiences.
<\/li>- Exposure to Python, R, SAS, Power BI, Tableau or similar tools.
<\/li>- Understanding of fraud risk, credit risk, customer lifecycle management and financial services products.
<\/li>- Knowledge of fraud strategy monitoring, fraud controls, decisioning systems and performance reporting.
<\/li>- Ability to assess rule effectiveness, alert quality, false positives, missed fraud and operational impacts.
<\/li>- Knowledge of, or ability to quickly learn, application and transaction fraud typologies.
<\/li>- Experience supporting strategy reviews, impact assessments and root\-cause analysis.
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<\/div>BEHAVIOURAL COMPETENCIES<\/b>
<\/div>- Strong analytical and problem\-solving skills.<\/span>
<\/li>- Intellectual curiosity and continuous learning mindset.
<\/li>- Action\-oriented with strong delivery focus.
<\/li>- Effective stakeholder engagement and collaboration skills.
<\/li>- Strong communication and influencing capability.
<\/li>- Sound judgement and data\-driven decision making.
<\/li>- Commercial awareness with a customer\-centric approach.
<\/li>- High levels of integrity, resilience, adaptability and self\-leadership.
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<\/div>QUALIFICATIONS <\/b>
<\/b><\/div>Minimum:<\/b>
<\/div>- Bachelor's degree in Finance, Economics, Statistics, Mathematics, Data Science, Actuarial Science, Engineering, Computer Science or a related quantitative discipline.
<\/span><\/li><\/ul>Advantageous:<\/b>
<\/div>- Postgraduate qualification in a quantitative, analytics, risk or business\-related field.<\/span>
<\/li>- Relevant fraud, risk, analytics, data science or business intelligence certifications.
<\/li>- Exposure to SQL, Python, R, SAS, Power BI, Tableau or similar tools.
<\/li><\/ul>Experience
<\/div>Required:
<\/div>- Experience analysing data using SQL, SAS, Python, R, Excel or similar tools.
<\/span><\/li>- Experience working with large datasets, data quality assessments and reconciliations.
<\/li>- Ability to communicate analytical insights clearly to diverse stakeholders.
<\/li>- Ability to manage multiple priorities and deliver high\-quality outputs independently.
<\/li><\/ul>Advantageous:<\/b>
<\/div>- Experience in fraud risk, credit risk, collections, recoveries, financial crime, customer analytics, decisioning, reporting or business intelligence.<\/span>
<\/li>- Experience within financial services, retail credit, banking, payments, cards or lending.
<\/li>- Exposure to fraud detection systems, rules engines, transaction monitoring or decisioning platforms.
<\/li>- Experience supporting strategy monitoring, governance reporting and control performance reviews.
<\/li><\/ul>Previous fraud experience is not essential if the candidate demonstrates strong analytical capability, learning agility and the ability to apply data\-driven thinking to fraud\-related business challenges.
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<\/div>ADDITIONAL REQUIREMENTS<\/b>
<\/div>- High level of confidentiality, integrity and judgement when working with customer, fraud, transactional and business\-sensitive data.
<\/span><\/li>- Ability to work with ambiguity, investigate data movements, ask the right questions and translate findings into practical fraud strategy recommendations.
<\/li>- Strong attention to detail, with the ability to produce accurate, evidence\-based outputs suitable for management, governance and audit\-related discussions.
<\/li>- Willingness to continuously build fraud, analytics, systems and business knowledge in line with the evolving WFS fraud strategy agenda.
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<\/div>Closing Date: 19 June 2026<\/b>
<\/div><\/span> - Ability to work with ambiguity, investigate data movements, ask the right questions and translate findings into practical fraud strategy recommendations.
- Experience within financial services, retail credit, banking, payments, cards or lending.
- Experience working with large datasets, data quality assessments and reconciliations.
- Relevant fraud, risk, analytics, data science or business intelligence certifications.
- Postgraduate qualification in a quantitative, analytics, risk or business\-related field.<\/span>
- Intellectual curiosity and continuous learning mindset.
- Proficiency in SQL and strong Excel capability.
- Timely reporting and analysis of fraud performance, losses, preventions, alerts and operational metrics.
- Support the development, implementation and monitoring of fraud strategies across products, channels and fraud systems.