An Interactive 5-Day Training Course

Predictive Modeling for Financial Fraud

Leveraging Statistical and Machine Learning Models to Anticipate and Prevent Financial Fraud

06 - 10 Apr 2026

Dubai

| $5950

20 - 24 Jul 2026

London

| $5950

19 - 23 Oct 2026

Amsterdam

| $5950

30 Nov - 04 Dec 2026

London

| $5950

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.

Key Learning Outcomes

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

Training 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.

Predictive Modeling for Financial Fraud

Who Should Attend?

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

Learning Journey Breakdown

Understanding financial fraud typologies and trends
Limitations of traditional detection and need for prediction
What is predictive modeling? Key concepts and benefits
Overview of data sources and risk indicators
Introduction to the predictive modeling workflow

Data collection and cleansing for fraud modeling
Feature engineering: selecting and creating predictive variables
Dealing with imbalanced data and rare event modeling
Exploratory analysis to understand relationships and anomalies
Data partitioning: training, validation, and test sets

Overview of classification algorithms: logistic regression, decision trees, and more
Introduction to machine learning models for fraud detection
Evaluating model performance: confusion matrix, ROC, AUC
Understanding overfitting and generalisation
Comparing and selecting the right model for your data

Interpreting model outputs and scores
Implementing risk thresholds and decision strategies
Integrating models into operational systems
Monitoring and updating predictive models over time
Governance and model risk management considerations

Aligning predictive modeling with business and regulatory objectives
Developing a fraud risk framework with predictive analytics
Collaborating with technical teams and data scientists
Exploring future trends in AI and predictive modeling
Summary, reflections, and next steps

Ready to Take the Next Step?

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