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Fraud Detection Using Descriptive, Predictive, and Social Network Analytics

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...

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$2,000 USD GSA  $1,632.24
Course Code BFRSUSN
Duration 2 days
Available Formats Classroom
6693 Reviews star_rate star_rate star_rate star_rate star_half
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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, health-care 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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