| Date | Venue | Fees | |
|---|---|---|---|
| 20 - 24 Jul 2026 | London - UK | $ 5,950 | |
| 19 - 23 Oct 2026 | Amsterdam - The Netherlands | $ 5,950 | |
| 30 Nov - 04 Dec 2026 | London - UK | $ 5,950 | |
| 19 - 23 Jul 2027 | London - UK | $ 5,950 | |
| 18 - 22 Oct 2027 | Amsterdam - The Netherlands | $ 5,950 | |
| 29 Nov - 03 Dec 2027 | London - UK | $ 5,950 |
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
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.
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
Who should Attend?
This GLOMACS Predictive Modeling for Financial Fraud training course is ideal for professionals responsible for managing fraud risk, financial data analysis, or predictive analytics, including:
- Fraud and financial crime analysts
- Risk and compliance officers
- Internal auditors and forensic accountants
- Data scientists and business analysts
- Finance and operations managers seeking proactive fraud solutions
Introduction to Predictive Modeling and Financial Fraud
- 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
Preparing Data for Prediction
- 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
Modeling Techniques and Tools
- 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
Model Deployment and Monitoring
- 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
Strategic Application and Future Outlook
- 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
- Upon successful completion of this training course, GLOMACS Certificate will be awarded to the delegates. Continuing Professional Education credits (CPE): In accordance with the standards of the National Registry of CPE Sponsors, one CPE credit is granted per 50 minutes of attendance
Endorsed Education Provider