The Role of Machine Learning in Fraud Auditing

Course Objectives:

• Understand the fundamentals of machine learning and its relevance in fraud auditing.
• Explore various machine learning techniques used to detect fraudulent activities.
• Analyze the integration of machine learning into traditional fraud auditing practices.
• Develop skills in using machine learning tools and techniques to identify potential fraud cases.

Introduction to Fraud Auditing and Machine Learning

• Overview of fraud auditing principles and challenges.
• The importance of data-driven auditing in identifying fraud.
• Introduction to machine learning (ML): key concepts, types of learning (supervised, unsupervised, reinforcement).
• The role of ML in automating fraud detection and analysis.

Data Collection and Preprocessing for Fraud Detection

• Types of data used in fraud auditing: transactional, behavioral, and financial data.
• Data preprocessing: cleaning, normalization, and feature selection.
• Understanding imbalanced datasets and techniques like oversampling and undersampling.
• Overview of key ML algorithms used in fraud detection (e.g., decision trees, logistic regression, random forests).

: Implementing Machine Learning Models in Fraud Auditing

• Building a simple fraud detection model: step-by-step guide using Python or relevant tools.
• Training and testing ML models using historical fraud data.
• Evaluation metrics: precision, recall, F1-score, ROC curves, and confusion matrix.
• Practical examples of fraud detection models in various industries (finance, insurance, e-commerce).

Challenges, Ethical Considerations, and Future Trends

• Common challenges in applying ML to fraud detection: data quality, interpretability, false positives.
• Ethical considerations: bias in ML models, privacy concerns.
• Future trends in fraud auditing: the role of AI, deep learning, and real-time fraud detection.
• Integration of ML with traditional fraud auditing methods.

DATE:
1ST BATCH: 16th – 19th Mar,2026
2ND BATCH: 7th – 10th July,2026

3RD BATCH: 3rd – 6th Nov,2026