When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
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...
Read MoreA 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.
