| Date | Venue | Fees | |
|---|---|---|---|
| 17 - 21 Aug 2026 | Dubai - UAE | $ 5,950 | |
| 05 - 09 Oct 2026 | Amsterdam - The Netherlands | $ 5,950 | |
| 05 - 09 Oct 2026 | Online | $ 4,950 | |
| 14 - 18 Dec 2026 | London - UK | $ 5,950 | |
| 03 - 07 May 2027 | Dubai - UAE | $ 5,950 | |
| 16 - 20 Aug 2027 | Dubai - UAE | $ 5,950 | |
| 04 - 08 Oct 2027 | Amsterdam - The Netherlands | $ 5,950 | |
| 04 - 08 Oct 2027 | Online | $ 4,950 | |
| 13 - 17 Dec 2027 | London - UK | $ 5,950 |
Introduction
In an increasingly data-saturated world, organisations are grappling with growing volumes of transactions, interactions, and digital activity — all of which can mask sophisticated fraudulent behaviour. To uncover hidden risks and patterns, fraud professionals need more than just traditional detection tools — they need the ability to extract meaningful insights from complex datasets.This GLOMACS training course, *Data Mining Techniques for Fraud Analytics*, bridges the gap between raw data and actionable fraud intelligence. It introduces participants to core data mining techniques that can help detect unusual patterns, relationships, and trends that often signal fraudulent activity. Whether you’re looking to enhance your fraud investigation process or build a proactive detection framework, this course offers practical knowledge grounded in real-world applications.
Objectives
By the end of this Data Mining Techniques for Fraud Analytics training course, participants will be able to:
- Understand the key concepts and methodologies of data mining
- Identify types of fraud that can be detected using data mining techniques
- Apply classification, clustering, and association techniques to detect anomalies and fraud patterns
- Evaluate the effectiveness of different data mining models in various fraud scenarios
- Understand data preparation, feature selection, and model evaluation within fraud analytics
Training Methodology
This training course employs a practical, instructor-led delivery style with a focus on structured learning modules. It incorporates visual demonstrations, step-by-step explanations, and walkthroughs of data mining workflows tailored to fraud detection. The content is accessible to both technical and non-technical participants, ensuring foundational concepts are clearly communicated without requiring coding or software development expertise.
Organisational Impact
Organisations that engage with this Data Mining Techniques for Fraud Analytics training course will benefit through:
- Improved ability to detect fraudulent behaviour using data-driven approaches
- Strengthened fraud prevention frameworks through data analysis
- Enhanced return on investment in fraud detection technology and tools
- Reduced operational and reputational risks associated with undetected fraud
- Greater integration of analytics into fraud management strategies
Personal Impact
Participants will gain:
- A solid understanding of data mining concepts and their relevance to fraud analytics
- Practical knowledge of key data mining models used in fraud detection
- Enhanced analytical and problem-solving capabilities
- Confidence in interpreting analytical results to support investigations
- Improved ability to engage with data science teams and contribute to anti-fraud initiatives
Who should Attend?
This GLOMACS Data Mining Techniques for Fraud Analytics training course is designed for professionals working in fraud risk management, auditing, compliance, analytics, and data science roles, including:
- Fraud investigators and analysts
- Internal and external auditors
- Risk and compliance officers
- Data and business analysts
- IT professionals and system architects supporting fraud detection initiatives
- Managers looking to enhance organisational fraud detection capabilities
Introduction to Data Mining and Fraud Analytics
- Understanding the scope of fraud and fraud analytics
- Introduction to data mining: objectives and process
- Types of fraud suitable for data mining approaches
- Key components of a fraud analytics program
- Overview of the CRISP-DM framework
Data Preparation and Exploration
- Identifying and sourcing relevant data for fraud analysis
- Data cleaning, transformation, and integration techniques
- Exploratory data analysis and visualization for anomaly detection
- Feature engineering and selection for fraud indicators
- Handling imbalanced datasets and missing values
Classification and Prediction Models
- Introduction to classification techniques (decision trees, logistic regression, etc.)
- Training and validating predictive models for fraud detection
- Performance evaluation metrics: accuracy, precision, recall, ROC curves
- Overfitting, model tuning, and cross-validation strategies
- Applications of classification in transaction and identity fraud
Clustering and Association Techniques
- Understanding unsupervised learning in fraud analytics
- Clustering methods (K-means, DBSCAN) for behavioral analysis
- Market basket analysis and association rule mining
- Identifying fraudulent patterns through segmentation and link analysis
- Selecting appropriate models for specific fraud cases
Integrating Data Mining into Fraud Strategy
- Building a data mining workflow for fraud detection
- Operationalizing fraud analytics models
- Ensuring model interpretability and business alignment
- Challenges and limitations of data mining in fraud prevention
- Summary and practical steps for implementation
- Upon successful completion of the classroom-based 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
- Upon successful completion of the online training course, a GLOMACS Certificate will be awarded to all delegates. Guided Learning Hours – In accordance with ISO 9001:2015–certified quality management standards, one Guided Learning Hour is awarded for every 60 minutes of attendance.
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