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Fraud Detection Using Descriptive, Predictive, and Social Network Analytics

Course Details
Code: BFRSUSN
Tuition (USD): $1,700.00 • Classroom (2 days)
Course Details
GSA (USD): $1,541.56 • Classroom (2 days)

A typical organization loses an estimated 5 of its yearly revenue to fraud. This course shows how learning fraud patterns from historical data can be used to fight fraud. The course discusses the use of supervised learning (using a labeled data set), unsupervised learning (using an unlabeled 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, and counterfeiting. The course provides a mix of both theoretical and technical insights, as well as practical implementation details. During the course, the instructor reports extensively on his recent research insights about the topic. Various real-life case studies and examples are presented for further clarification.

Skills Gained

  • preprocess data for fraud detection (sampling, missing values, outliers, categorization, and so on)
  • build fraud detection models using supervised analytics (logistic regression, decision trees, neural networks, ensemble models, and so on)
  • build fraud detection models using unsupervised analytics (hierarchical clustering, non-hierarchical clustering, k-means, self organizing maps, and so on)
  • build fraud detection models using social network analytics (homophily, featurization, egonets, PageRank, bigraphs, and so on).

Who Can Benefit

  • 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, healthcare institutions, and consulting firms

Prerequisites

  • Before attending this course, you should have a basic knowledge of statistics, including descriptive statistics, confidence intervals, and hypothesis testing.

Course Details

Introduction

Fraud Detection

  • the importance of fraud detection
  • defining fraud
  • anomalous behavior
  • fraud cycle
  • types of fraud
  • examples of insurance fraud and credit card fraud
  • key characteristics of successful fraud analytics models
  • fraud detection challenges
  • approaches to fraud detection

Data Preprocessing

  • motivation
  • types of variables
  • sampling
  • visual data exploration
  • missing values
  • outlier detection and treatment
  • standardizing data
  • transforming data
  • coarse classification and grouping of attributes
  • recoding categorical variables
  • segmentation
  • variable selection

Supervised Methods for Fraud Detection

  • target definition
  • linear regression
  • logistic regression
  • decision trees
  • ensemble methods: bagging, boosting, random forests
  • neural networks
  • dealing with skewed class distributions
  • evaluating fraud detection models

Unsupervised Methods for Fraud Detection

  • unsupervised learning
  • clustering approaches: hierarchical clustering, k-means clustering, self-organizing maps
  • peer group analysis
  • break point analysis

Social Networks for Fraud Detection

  • social networks and applications
  • is fraud a social phenomenon?
  • social network components
  • visualizing social networks
  • social network metrics
  • community mining
  • social network based inference (network classifiers and collective inference)
  • from unipartite toward bipartite graphs
  • featurizing a bigraph
  • fraud propagation
  • case study

Fraud Analytics: Putting It All to Work

  • quantitative monitoring: backtesting, benchmarking
  • qualitative monitoring: data quality, model design, documentation, corporate governance
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