Technical Fraud Analyst

Flash Group; Wes-Kaap; On-site; ZAR 300 000 - 400 000; Full time

Job summary

A leading data analytics firm in South Africa seeks a Technical Fraud Analyst to utilize advanced statistical techniques and machine learning to combat fraud. The candidate will analyze large datasets, develop predictive models, and work collaboratively to enhance fraud detection systems. Ideal applicants will have a Bachelor's degree in a relevant field and at least two years of experience in fraud or data analysis. Strong programming skills in languages such as R, SQL, Python, and Java are essential, alongside analytical skills and adaptability.

Qualifications

• Minimum two years’ experience in fraud/data-related field.
• Experience working with large datasets and databases is essential.

Responsibilities

• Analyse large datasets to identify patterns and anomalies.
• Research and incorporate new fraud detection techniques.
• Evaluate and improve accuracy of fraud detection systems.
• Ensure data integrity during the analysis process.
• Provide recommendations to enhance fraud prevention measures.
• Implement real-time analytics for fraud detection.
• Collaborate with internal teams on fraud prevention strategies.

Skills

Analytical and problem-solving skills

Programming languages (R, SQL, Python, Java)

Understanding of statistical methods

Familiarity with machine learning and AI

Education

Bachelor’s degree in a related field

Job description

Overview

We are looking for a Technical Fraud Analyst who will leverage advanced statistical techniques, machine learning algorithms, and data analytics to detect and prevent fraudulent activities.

The role will encompass analysing large datasets to identify patterns and anomalies, develop predictive models to anticipate fraudulent behaviour, and work closely with cross-functional teams to implement and refine fraud detection systems.

RESPONSIBILITIES

• Analyse large datasets by conducting thorough analysis of extensive datasets to identify patterns and anomalies indicative of fraudulent activity.
• Research new techniques by staying updated on the latest fraud detection methodologies and incorporate them into the organisation’s practices.
• Evaluate system performance regularly, by assessing and improving accuracy and efficiency of fraud detection systems and models.
• Ensure data integrity and security of the data throughout the analysis process to protect sensitive information.
• Provide insights and recommendations based on data analysis to enhance overfall fraud prevention and detection measures.
• Implement real-time analytics to detect and respond to fraudulent activities as they occur, minimising potential damage.
• Regularly update and refine fraud detection models and techniques based on new data and emerging fraud trends.
• Collaborate with internal teams, to develop and implement comprehensive fraud prevention strategies.
• Develop and implement systems for identifying unusual behaviours or transactions that could indicate fraudulent activity.

MINIMUM REQUIREMENTS

• Bachelor’s degree in a related field such as data science, computer science, statistics, mathematics, or engineering.
• Two (2) years’ experience in fraud/data related field.
• Experience working with large datasets and databases is essential.

REQUIRED KNOWLEDGE / TECHNICAL SKILLS

• Candidates should possess strong analytical and problem-solving skills, experience with programming languages like R, SQL, Python and Java
• Solid understanding of statistical methods and data analysis techniques.
• Familiarity with machine learning and artificial intelligence.

COMPETENCIES / ATTRIBUTES

• Analytical Thinking: Ability to systemically analyse complex data, identify patterns, and trends.
• Attention to Detail: Meticulous focus on data accuracy and precision in identifying discrepancies.
• Problem-Solving Skills: Proficiency in developing innovative solutions to detect and prevent fraud.
• Communication Skills: Effectiveness in conveying complex findings and insights to non-technical stakeholders.
• Adaptability: Flexibility to adjust to new challenges, tools, and techniques in the evolving fraud landscape.
• Critical Thinking: Skill in evaluating situations logically and making informed decisions based on data.
• Ethical Integrity: Commitment to ethical practices and maintaining confidentiality in handling sensitive data.
• Creativity in Developing Solutions: Inventiveness in devising new strategies and methods to combat fraud.
• Time Management Skills: Efficient in managing multiple tasks and meeting deadlines in a fast-paced environment.
• Persistence: Tenacity in exploring data and testing models until effective solutions for fraud detection are found.