KDnuggets Home » News » 2017 » Jan » Courses, Education » New e-learning course: Fraud Analytics using Descriptive, Predictive and Social Network Analytics ( 17:n04 )

New e-learning course: Fraud Analytics using Descriptive, Predictive and Social Network Analytics


 
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This online course teaches how to find fraud patterns from historical data using descriptive analytics, and social network learning.



This Fraud Analytics Booknew e-learning course will show how learning fraud patterns from historical data can be used to fight fraud. To be discussed is the use of descriptive analytics (using an unlabeled data set), predictive analytics (using a labeled data set) and social network learning (using a networked data set). The techniques can be applied across a wide variety of fraud applications, such as insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, counterfeit, etc. The course will provide a mix of both theoretical and technical insights, as well as practical implementation details. The instructor will also extensively report on his recent research insights about the topic. Various real-life case studies and examples will be used for further clarification.

Learn how to

  • preprocess data for fraud detection (sampling, missing values, outliers, categorization, etc.)
  • build fraud detection models using predictive analytics (logistic regression, decision trees, neural networks, ensemble models, random forests, etc.)
  • build fraud detection models using descriptive analytics (peer group analysis, break point analysis, hierarchical clustering, non-hierarchical clustering, k-means, self-organizing maps, etc.)
  • build fraud detection models using social network analytics (homophily, featurization, egonets, PageRank, bigraphs, etc.)
  • use our recently developed GOTCHA! method to detect fraud using bipartite social networks
  • backtest fraud models in terms of data stability, model stability and model calibration
  • use case management and visual analytics to disentangle complex fraud patterns
  • identify the importance of privacy when accessing both internal as well as external data for fraud detection
  • calculate buffer capital to protect a firm in terms of both the expected and unexpected fraud losses it is exposed to
  • evaluate fraud models in terms of their economic impact (TCO, ROI)

The e-learning course consists of more than 20 hours of movies, each approximately 5 minutes on average.  Quizzes are included to facilitate the understanding of the material. Upon registration, you will get an access code which gives you unlimited access to all course material during 1 year. The e-learning course focusses on the concepts and modeling methodologies and not on the SAS software!  To access the course material, you only need a laptop, iPad, iPhone with a web browser. No SAS software is needed.  View course outline.

About the Instructor

Professor Bart Baesens is a professor at KU Leuven (Belgium), and a lecturer at the University of Southampton (United Kingdom).  He has done extensive research on big data & analytics, fraud detection, marketing analytics and credit risk management.  His findings have been published in well-known international journals (e.g. Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, Journal of Machine Learning Research) and presented at international top conferences.  He is author of the books Credit Risk Management: Basic Concepts, Analyticsin a Big Data World, Fraud Analytics using Descriptive, Predictive and Social Network Techniques, and Credit Risk Analytics: Measurement Techniques, Applications, and Examples in SAS.  He teaches E-learning courses on Advanced Analytics in a Big Data World and Credit Risk Modeling.  His research is summarized at www.dataminingapps.com.  He also regularly tutors, advises and provides consulting support to international firms with respect to their big data, analytics and fraud detection strategy.


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