Overview What You Will Learn Curriculum Instructor

Course Overview

Are you concerned about the increasing number of frauds and scams in your business and the potential financial losses and reputational damage they could cause? It is crucial to detect and prevent frauds and scams as early as possible to avoid severe consequences. This course is the perfect solution to your problem as it offers a comprehensive and advanced approach to fraud management.  

By taking this course, you will acquire valuable skills and knowledge that will help you detect and prevent frauds and scams more efficiently, thereby saving your business from potential financial losses and reputational damage.

By the end of the course, you will leverage the fraud analysis techniques, fraud detection techniques, etc., to understand the current situation in the financial sector, and you will be proficient enough to tackle the risk management factors that cause the crisis.   

What You Will Learn

  • Understanding Fraudulent Transactions
  • Investigating the Latest Trends and Future Directions in Data Mining for Fraud Detection
  • Applying Data Preprocessing Techniques for Fraud Detection
  • Using Supervised Learning for Fraud Detection
  • Utilizing Unsupervised Learning for Fraud Detection
  • Implementing Ensemble Learning for Fraud Detection
  • Performing Feature Selection Techniques for Fraud Detection
  • Exploring Applications of Data Mining for Fraud Detection

Program Curriculum

  • Types of Fraudulent Transactions
  • Characteristics of Fraudulent Transactions
  • Identifying Fraudulent Transactions using Data Mining Techniques
  • $7 Million Cybersecurity Scholarship by EC-Council

  • Data Cleaning and Transformation
  • Outlier Detection and Sampling
  • Outlier Detection and Sampling – Example in Python
  • Chapter 2 Lab

  • Overview of Supervised Learning Algorithms
  • Logistic Regression
  • Decision Trees
  • Support Vector Machines (SVM)
  • Naive Bayes
  • Neural Networks
  • Hyper Parameter Tuning
  • Supervised Learning for Fraud Detection - Code Example
  • Chapter 3 Lab

  • Overview of Unsupervised Learning Algorithms
  • Clustering Algorithms
  • Anomaly Detection
  • Autoencoders
  • One Class Support Vector Machine (OCSVM)
  • Hyper Parameter Tuning
  • Unsupervised Learning for Fraud Detection - Code Example

  • Overview of Ensemble Learning
  • Bagging (Bootstrap Aggregating)
  • Boosting
  • Stacking (Stacked Generalization)
  • Voting
  • Hyperparameter Tuning
  • Ensemble Learning for Fraud Detection – Code Example
  • Chapter 5 Lab

  • Introduction to Feature Selection
  • Filter Methods for Feature Selection
  • Wrapper Methods for Feature Selection
  • Embedded Methods for Feature Selection
  • Comparison of Feature Selection Techniques
  • Supervised Learning for Fraud Detection – Code Example
  • Chapter 6 Lab

  • Relevant Industries
  • Fraud Detection in Banking and Finance - Code Example
  • Fraud Detection in E-commerce - Code Example
  • Fraud Detection in Healthcare - Code Example
  • Fraud Detection in Insurance - Code Example
  • Chapter 7 Lab

  • Blockchain Technology and Fraud Prevention
  • Emerging Trends in Fraud Detection
  • Latest Trends and Future Direction
Load more modules

Instructor

Angelos Markou

Angelos Markos is an experienced Data Scientist with over 11 years of experience in the field of fraud detection and prevention. With a background in e-commerce and banking, Angelos has worked for several companies and is widely regarded as an expert in data mining for fraud detection and has a proven track record of successfully detecting fraudulent activity and preventing potential losses for businesses and individuals alike. Through Angelo's e-course on Data Mining for Fraud Detection, you can be sure that you'll receive the best possible training from a highly qualified and experienced professional.

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