Fraud Detection Workshop

About

This course will help you  on leveraging data analytics to combat fraud. Participants will explore techniques such as supervised and unsupervised learning, as well as social network analysis, using historical data. The course covers a wide range of fraud applications, including insurance, finance, healthcare, and more. Led by an expert instructor, attendees will gain theoretical insights, technical skills, and practical implementation strategies through real-life case studies. Enroll now to strengthen your fraud detection capabilities

Who Should Attend

Fraud analysts, data miners, and data scientists; consultants working in fraud detection; validators auditing fraud models; and researchers in financial services companies, banks, insurance companies, government institutions, health-care institutions, and consulting firms

Learn How To

• Preprocess data for fraud detection including sampling, missing values, outliers, categorization
• Fraud detection models using supervised analytics such as logistic regression, decision trees, neural networks, ensemble models
• Fraud detection models using unsupervised analytics such as hierarchical clustering, non-hierarchical clustering, k-means, self organizing maps
• Fraud detection models using social network analytics (homophily, featurization, egonets, PageRank, bigraphs, .