Predictive Modeling for Financial Fraud

Introduction

Financial fraud is one of the most costly and disruptive risks facing organisations today. While reactive detection techniques are still widely used, forward-looking strategies that harness predictive modeling are increasingly proving essential in staying ahead of fraudsters.This GLOMACS training course, *Predictive Modeling for Financial Fraud*, provides participants with the knowledge and tools to build, understand, and evaluate predictive models tailored to fraud risk. It introduces statistical and machine learning methods that help forecast fraudulent behaviour based on historical data, behavioural indicators, and transactional patterns. Delegates will leave with a clear understanding of how to apply predictive analytics within a broader fraud risk framework.

Objectives

By the end of this Predictive Modeling for Financial Fraud training course, participants will be able to:

• Understand the principles and lifecycle of predictive modeling in fraud contexts
• Explore key statistical and machine learning techniques used in fraud prediction
• Select, train, and validate models using appropriate performance metrics
• Interpret model outputs to support risk-based decision making
• Incorporate predictive modeling into an overall financial fraud management strategy

Methodology

This training course is delivered in a structured format through instructor-led sessions. It combines clear theoretical explanations with illustrative examples and model walkthroughs. The training course is accessible to professionals with varying technical backgrounds, providing conceptual clarity without requiring programming or advanced statistical knowledge.

Organisational Impact

Participating organisations will benefit through:

• Improved capability to anticipate and prevent fraud before losses occur
• More effective allocation of investigative and compliance resources
• Enhanced integration of analytics into enterprise fraud risk strategies
• Better alignment with regulatory expectations for proactive monitoring
• Increased trust and transparency in financial systems

Personal Impact

Participants will gain:

• A practical understanding of predictive modeling in the financial fraud domain
• Skills to interpret and evaluate model performance and applicability
• Confidence to participate in model development and implementation discussions
• Broader analytical thinking in the context of financial risk
• A foundation for advancing toward more complex fraud analytics roles