Fraud Detection Using Descriptive, Predictive, and Social Network Analytics

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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.