Technical Counter-Fraud Officer Pathway

Module Details

• Foundation – understand how data analytics integrates into fraud investigation and detection workflows, whilst gaining a strong foundation in using Microsoft Excel for investigative work. You will also learn how to prepare and clean your data for analysis and conduct sampling in a professional manner. 
• Analysis – learn how to conduct both broad, high-level analysis to catch inconsistencies that can identify wider patterns of fraud as well as how to drill down into data using outlier analysis to detect more sophisticated, individual level fraud. You will also understand specialised fraud detection methodologies such as Benford Analysis, the Luhn Algorithm, and Relative Size Factoring.
• Reporting –understand how to record your methodologies and findings in an effective, replicable manner. From this, learn how to present your findings in a professional report for wider dissemination to key stakeholders who may not have a strong analytical background.

• Database querying and analysis – understand how databases work and write SQL queries to analyse transaction data, customer records, and account information. Master date and time analysis to detect rapid transactions, dormant accounts, and suspicious timing patterns.
• Advanced fraud detection – build complex filters combining multiple conditions, create automated detection rules that run continuously and use joins to spot patterns across multiple tables.
• Professional documentation and reporting – write clear, reusable queries that others can understand and maintain, document methodologies for audit trails and complete a practical mock investigation detecting fraud in sample datasets.

• Python fundamentals and data handling – learn Python basics, coding syntax, and how to work with lists, dictionaries, CSV files, and Excel data.
• Fraud detection automation – Learn how to use conditions and loops to implement fraud rules. Build reusable checks for duplicate records, unusual amounts, and repeated identifiers.  Automate evidence file organisation and batch-check records against fraud criteria.
• Investigation toolkit project – integrate open-source data and build your own "fraud toolkit" for real-world investigation work.

• Website code analysis – Understand how websites work and are hosted. Analyse the coding behind a website to extract investigative clues, malicious scripts, and credential-stealing techniques which may link multiple fraudulent sites.
• Technology fingerprinting and profiling – Identify content management systems, frameworks, plugins, and themes. Build comprehensive technical profiles of fraudulent websites and compare these profiles across multiple sites to identify common ownership or creation methods
• Open-source intelligence and practical investigation – investigate website registration data, hosting patterns, name servers, and SSL certificates to identify shared fraudulent infrastructure. Conduct a complete investigation from site discovery to building linked website networks.

Module details