TAYLLORCOX > Akademie > SAS > Fraud Detection Using Descriptive, Predictive, and Social Network Analytics

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 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.

Termíny
Target group
Prerequisites

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Target group

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

Course structure

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.

Prerequisites

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