Analyst : Card Fraud

Purpose Statement:

Through card analysis, aim to identify anomalies, and provide actionable recommendations that mitigate risk, reduce financial losses, and protect both customers and stakeholders from the impact of card fraudulent behaviour. Extracting, analysing, and linking of data from multiple sources and systems to detect card fraud trends that will influence strategy and the writing of expert card fraud rules. Implementing and operationalising card fraud mitigation and analysing the effectiveness thereof through dashboards and metrics.

Experience

Minimum: 

• At least 3 years experience within a card environment with a focus on card analysis, including application of SQL, Power BI and advanced Excel functions.
• Ideal: 3+ years experience within a card fraud/risk analytical environment, including application of SQL, Power BI and advanced Excel functions. Card Fraud analysis experience. Banking / financial services

Qualifications (Minimum) 

• A relevant tertiary qualification in Information Technology - Computer Science or Statistics

Qualifications 

(Ideal or Preferred) 

• Bachelor's Degree in Information Technology - Computer Science or Information Technology

Knowledge

Minimum:

• Proficiency in card analysis techniques and statistical methods to identify anomalies and patterns indicative of card fraud.
• Knowledge of card fraud detection methods, such as rule-based systems, anomaly detection, and predictive modelling.
• Strong knowledge of SQL for querying databases and extracting relevant data for analysis.
• A basic understanding of machine learning concepts and algorithms, including supervised and unsupervised learning.
• Familiarity with data visualization tools and techniques for creating informative dashboards and reports.
• Ability to identify and address complex card fraud-related challenges and develop effective solutions.
• In-depth knowledge of query and scripting languages to extract, manipulate, and analyse large datasets, uncovering hidden patterns and anomalies indicative of fraudulent behaviour.

Ideal:

Understanding and knowledge of:

• Card Forensics (fraud) Internal MIS database tables/table structures AWS
• Bank products
• Proficiency in a scripting language (e.g., Python) for data manipulation, scripting, and model development.

Skills

• Analytical Skills Numerical Reasoning skills Planning, organising and coordination skills Problem-solving skills Attention to Detail

Conditions of Employment: Clear criminal and credit record