Financial Fraud Detection and Big Data Analytics Using Artificial Intelligence

Course Objectives:

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

• Understand the nature and mechanisms of financial fraud and identify the most common fraudulent schemes across industries.
• Leverage Artificial Intelligence (AI) and Machine Learning (ML) techniques to detect anomalies and suspicious financial transactions.
• Apply Big Data Analytics to extract early warning indicators of fraud from large and complex datasets.
• Develop predictive models to proactively prevent and mitigate financial fraud risks.
• Design intelligent internal control systems powered by automation and data-driven insights.

Course Outline

Introduction to Financial Fraud and Intelligent Analysis

• Concepts and types of financial fraud (accounting, banking, cyber, etc.)
• The impact and cost of fraud on organizations
• The role of digital transformation in modern fraud detection
• Overview of Artificial Intelligence and its financial applications

Big Data Analytics in Financial Systems

• Understanding Big Data: types, sources, and structures
• Tools and technologies for financial data analytics (Python, Power BI, Tableau, etc.)
• Data cleaning and preprocessing to detect abnormal patterns
• AI-driven exploratory and descriptive analytics

Artificial Intelligence in Fraud Detection

• Machine Learning algorithms for fraud detection
• Classification models for identifying suspicious transactions
• Anomaly detection and behavioral analytics
• Real-world applications in banking, insurance, and government sectors

Predictive Modeling and Intelligent Systems

• Building predictive fraud detection models with real datasets
• Evaluating model performance (Precision, Recall, ROC Curve)
• Integrating predictive models with internal control systems
• AI-based automation in internal audit and compliance functions

Legal and Governance Frameworks for Anti-Fraud Systems

• International standards for anti-fraud and compliance (ACFE, ISO 37001, COSO)
• Data privacy and ethical considerations in AI-driven analytics
• Governance, transparency, and accountability frameworks
• The future of fraud detection in the era of digital transformation and AI