Fraud Analytics Training

This DIXONTECH training provides professionals with the advanced analytical tools and techniques required to detect, prevent, and investigate financial fraud using data analytics. Participants will learn to build fraud detection models, apply predictive analytics, and use digital tools for identifying suspicious patterns and anomalies. The course bridges forensic accounting, risk management, and data science, preparing participants to safeguard their organizations against financial and operational fraud.

TRAINING TOPICS

• Principles of Fraud Detection and Prevention
• Data Analytics and Predictive Modelling for Fraud
• Fraud Risk Assessment and Internal Controls
• Machine Learning and AI in Fraud Detection
• Case Studies and Practical Fraud Investigation Tools

By the end of this training course, delegates will be able to:

• Apply analytics to detect and prevent fraud
• Identify anomalies through data mining and visualization
• Build predictive fraud detection models
• Strengthen internal controls and governance frameworks
• Implement data-driven fraud investigation processes
• Interpret large data sets for hidden fraud risks
• Enhance compliance and fraud reporting accuracy

TRAINING IS TAILORED TO:

• Auditors and forensic accountants
• Compliance and risk management officers
• Data analysts and fraud investigators
• Financial controllers and internal auditors
• Security and investigation professionals
• Regulatory and law enforcement specialists
• Corporate governance and compliance teams

TRAINING METHODOLOGY

The course combines lectures, real-life fraud case studies, and hands-on analytics exercises using Excel and visualization tools. Participants will learn to use statistical techniques, scenario testing, and fraud detection algorithms to identify fraudulent transactions and implement effective control systems.

DAY 1

INTRODUCTION TO FRAUD AND ANALYTICS IN BUSINESS

• Understanding types and patterns of fraud
• Key fraud indicators and red flags
• Fraud triangle: pressure, opportunity, rationalization
• Importance of data analytics in fraud detection
• Fraud prevention frameworks and strategies
• Building a fraud risk management culture
• Case study: identifying early warning signals

DAY 2

DATA COLLECTION, CLEANSING, AND ANALYSIS FOR FRAUD DETECTION

• Sources of data for fraud analytics
• Data integrity, completeness, and accuracy verification
• Using Excel and Power Query for data preparation
• Exploratory data analysis and trend identification
• Identifying outliers and unusual transactions
• Data visualization for anomaly detection
• Practical exercise: cleansing and analyzing datasets

DAY 3

PREDICTIVE ANALYTICS AND MODEL DEVELOPMENT

• Introduction to statistical and predictive modeling
• Logistic regression and classification models
• Correlation and pattern recognition analysis
• Using sampling methods in fraud detection
• Creating dashboards for monitoring risk indicators
• Predictive modeling workflow for fraud scenarios
• Case study: building a simple predictive model

DAY 4

MACHINE LEARNING AND AI IN FRAUD DETECTION

• Role of AI and ML in detecting complex fraud
• Supervised vs. unsupervised learning applications
• Clustering, decision trees, and neural networks
• Automation in continuous fraud monitoring
• Fraud detection with anomaly detection algorithms
• Integrating AI models into financial systems
• Practical session: AI-based fraud detection simulation

DAY 5

FRAUD INVESTIGATION, REPORTING, AND CONTROL FRAMEWORKS

• Investigative process: from suspicion to evidence
• Building audit trails and fraud documentation
• Legal and ethical considerations in investigations
• Implementing governance and internal control policies
• Reporting fraud findings to management and regulators
• Technology-enabled internal audits and compliance
• Capstone project: developing an organization-wide fraud control strategy