Specialist: Fraud Detection

We are sourcing for a Specialist: Fraud Detection role.

Key Performance Areas

Fraud Detection Analysis

• Analyse incidents that lead to fraud detection.
• Analyse suspicious behaviour or patterns that might indicate suspected fraud.
• Conduct analysis and review allegations of suspected fraud.
• Conduct thorough research suspected fraud allegations to gather evidence in determining a fraud activity.

Fraud Detection Interventions and Enhancement

• Implement fraud detection systems that are equipped to adapt to new threats as they emerge.
• Drive continuous harvesting of insights from fraud loss events and build fraud intelligence to inform controls and development of new system rules and enhancements.
• Monitor the implementation and effectiveness of proposed system, control and detection enhancements to improve fraud detection.
• Ensure that required systems are available for use, suitable rules are applied, and fraud intervention processes are executed within the required timelines and compliance standards.

Conduct Regular System Audits

• Conduct assessments to identify vulnerabilities and system gaps.
• Ensure that the fraud detection system is functioning effectively.
• Detect control weaknesses and evolving fraud tactics.

Lifestyle Audit

• Data collection: Extraction of financial documents and any other supporting documents.
• Data preparation: Transforming, converting and enriching data.

Policy Review and Implementation

• Develop, review and implement departmental policies and procedures and processes.
• Keep up to date with effective policy and practice execution strategies.
• Develop and implement the appropriate Fraud Control strategy with priorities and objectives for the forthcoming year.

Reporting

• Track and analyse potential fraud trends and performance.
• Collect data from various sources, from external threat intelligence to be used in detailed analysis.
• Analyse data form for detailed fraud patterns.
• Conduct fraud analytics to improve the detection speed and accuracy.
• Extract and share insights and lessons learnt from root cause analysis applied in fraud cases to inform further fraud prevention improvements across a wide range of processes and systems, regulation and policies.
• Compile and communicate accurate and timely reports to track progress made and keep key stakeholders informed of fraud detection.
• Prepare detailed reports on findings and provide recommendations for preventive measures in consultation with Fraud Prevention unit.

Stakeholder Management

• Communicate to relevant stakeholders and fraud teams to alert them to potential threats that could harm company
• Collaborating with other data analysts, you will help build models and rules to prevent fraud while minimising customer impact.
• Coordinate with law enforcement agencies when required.
• Active participation in and contribution to specialist practice forums to share information and insights across the business.

Qualifications and Experience

• Bachelor’s Degree/ Advanced Diploma in Information Technology/ Computer Science/ Information Management/ Data Analysis/ Data Science.
• Association/ Affiliation in one or more of the following professional bodies ACFE/ ICFP or ISACA.
• ACL Certified Data Analyst (ACDA)/ MCSE/ Certified Analytics Professional (CAP)/ Certified Data Management Professional (CDMP) or any other equivalent Data Analytics Certificate will be added advantage.
• Relevant 5 - 7 years’ experience in data analytics, auditing or banking environment inclusive of experience in analysis methodologies, process disciplines, system development lifecycle methodologies and automation and scheduling experience.
• Experience in data mining and the development of procedures automated for management reporting purposes will be an added advantage.
• Experience in the application of data analysis tools such as: MS SQL/ ACL/ Analyst Notebook/ MS Power BI/ AWS Database.

Technical and Behavioral Competencies Required

• Network and alliance.
• Employee engagement.
• Ethics and values.
• Change management.
• Critical and innovative thinking
• Policy conceptualisation and formulation.
• Risk Management.
• Stakeholder development and relations.
• Ability to develop data interrogation techniques and the creation of data cubes.
• Knowledge of database tools, structures and extraction tools.
• Knowledge of data integration and transformation practices.
• Knowledge of data quality practices.
• Knowledge of data mining and analysis.
• Knowledge data analysis tools such as: MS SQL/ ACL/ Analyst Notebook/ MS Power BI/ AWS Database/Tableau.
• Computer Literacy (MS Word, MS Excel, MS Outlook).

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