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Elérhetőségeink kapcsolat oldalunkon.
Előadó: Bart Baesens, Ph.D. Professor at KU Leuven (Belgium), and lecturer at the University of Southampton (UK); or Christophe Mues, Ph.D., Professor at the School of Management of the University of Southampton (UK); or Cristian Bravo, Ph.D., Associate Professor, Business Analytics, University of Southampton (UK); or Wouter Verbeke, Ph.D., Assistant Professor, Business Informatics, University of Brussels (Belgium); or Stefan Lessmann, Ph.D., Professor, School of Business and Economics, Humboldt University (Germany)
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.
Ismerje meg hogyan...
- 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).
- 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
- 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.
- Types of variables.
- Visual data exploration.
- Missing values.
- Outlier detection and treatment.
- Standardizing data.
- Transforming data.
- Coarse classification and grouping of attributes.
- Recoding categorical variables.
- 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.
Before attending this course, you should have a basic knowledge of statistics, including descriptive statistics, confidence intervals, and hypothesis testing.
A tanfolyam SAS Enterprise Miner szoftver használatára épül.
Base SAS and SAS Social Network Analytics are also used in this course.