Fraud Analytics

Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages.


Become an expert

Learn how to process data for fraud detection and build fraud detection models using 

  • Predictive analytics (logistic regression, decision trees, neural networks, ensemble models, random forests, etc.)
  • Descriptive analytics (peer group analysis, break point analysis, hierarchical clustering, non-hierarchical clustering, k-means, self-organizing maps, etc.)
  • Social network analytics (homophily, featurization, egonets, PageRank, bigraphs, etc.)
  • Case management and visual analytics to disentangle complex fraud patterns