Detecting Fraud with Data Analytics

Fraud continues to pose a significant threat to organizations across all sectors, impacting financial performance, reputation, and stakeholder trust. As fraud schemes become more complex and data volumes continue to grow, traditional detection methods alone are no longer sufficient. Organizations increasingly rely on data analytics to identify unusual patterns, detect anomalies, and uncover potential fraud risks proactively.

The Detecting Fraud with Data Analytics course is designed for professionals who have foundational knowledge of fraud, audit, or data concepts and are seeking to enhance their capabilities through analytical approaches.

Within BOOST, this program focuses on equipping participants with the skills to apply data analytics techniques to support fraud detection, prevention, and investigation activities.

The course emphasizes practical understanding of how data can be used to identify red flags, analyze transactions, and support evidence-based conclusions. Participants explore common fraud schemes, relevant data sources, analytical methods, and the interpretation of results within audit, compliance, and investigative contexts.

By combining fraud risk knowledge with applied analytics concepts, this program enables participants to move from reactive detection toward proactive, data-driven fraud risk management.

• Understand the role of data analytics in fraud detection
• Identify common fraud schemes and related data indicators
• Use analytical techniques to detect anomalies and red flags
• Select appropriate data sources for fraud analysis
• Apply basic and intermediate analytics methods in fraud scenarios
• Interpret analytical results to support fraud conclusions
• Integrate data analytics into audit and investigation processes
• Strengthen fraud prevention and detection capabilities