После прохождения этих курсов я уверенно создаю профессиональные визуализации и дашборды.
Shoebur RahmanСпециалист по закупкам, Giz Bangladesh
Описание курса
The Association of Certified Fraud Examiners estimates that fraud costs organizations worldwide $3.7 trillion a year and that a typical company loses five percent of annual revenue due to fraud. Fraud attempts are expected to even increase further in future, making fraud detection highly necessary in most industries. This course will show how learning fraud patterns from historical data can be used to fight fraud. Some techniques from robust statistics and digit analysis are presented to detect unusual observations that are likely associated with fraud. Two main challenges when building a supervised tool for fraud detection are the imbalance or skewness of the data and the various costs for different types of misclassification. We present techniques to solve these issues and focus on artificial and real datasets from a wide variety of fraud applications.
Предварительные требования
Программа
План курса
1
Introduction & Motivation
This chapter will first give a formal definition of fraud. You will then learn how to detect anomalies in the type of payment methods used or the time these payments are made to flag suspicious transactions.
- Introduction & Motivation50 XP
- Imbalanced class distribution100 XP
- Cost of not detecting fraud100 XP
- Time features50 XP
- Circular histogram100 XP
- Suspicious timestamps100 XP
- Frequency features50 XP
- Frequency feature for one account100 XP
- Frequency feature for multiple accounts100 XP
- Recency features50 XP
- Recency feature100 XP
- Comparing frequency & recency100 XP
2
Social network analytics
In the second chapter, you will learn how to use networks to fight fraud. You will visualize networks and use a sociology concept called homophily to detect fraudulent transactions and catch fraudsters.
3
Imbalanced class distributions
Fortunately, fraud occurrences are rare. However, this means that you're working with imbalanced data, which if left as is will bias your detection models. In this chapter, you will tackle imbalance using over and under-sampling methods.
4
Digit analysis and robust statistics
In this final chapter, you will learn about a surprising mathematical law used to detect suspicious occurrences. You will then use robust statistics to make your models even more bulletproof.
R
Fraud Detection in R
Курс
завершён
