Information Technology: Professor Vasarhelyi
Lecture #4 Part 1-2
Data Mining and Detecting Fraudulent Transactions
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Time Stamps:
00:00:22 Exception Prioritization in Continuous Auditing: A Framework and Experimental Evaluation
00:04:21 Data Mining
00:06:27 Data Mining: Confluence of Multiple Discipline
00:10:31 Data Mining Applications
00:12:36 Data Mining Tasks
00:15:45 Exception Prioritization in Continuous Auditing: A Framework and Experimental Evaluation
00:16:22 Fraud
00:18:47 Motivation
00:22:23 Contribution
00:26:18 Reasons for Using Belief Function
00:37:51 What is a Population?
00:41:30 Detecting Fraudulent Transactions
00:59:59 Exceptional Exceptions
01:07:25 Clustering and Process Mining
01:11:02 Process Tracing
Professor Li begins this lecture with a discussion of her research. She goes into detail regarding data mining and fraud. Professor Vasarhelyi then lectures about detecting fraudulent transactions. He proceeds with a discussion of clustering, process mining, and process tracing. This lecture is continued in part two of Lecture 4.
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