BC4013 Data Analytics for Fraud Detection Syllabus:
BC4013 Data Analytics for Fraud Detection Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
Discuss the overall process of how data analytics is applied
Discuss how data analytics can be used to better address and identify risks
Help mitigate risks from fraud and waste for our clients and organizations
UNIT I INTRODUCTION
Introduction: Defining Fraud, Anomalies versus, Fraud, Types of Fraud, Assess the Risk of Fraud, Fraud Detection, Recognizing Fraud, Data Mining versus Data Analysis and Analytics, Data Analytical Software, Anomalies versus Fraud within Data, Fraudulent Data Inclusions and Deletions
UNIT II DATA ANALYSIS CYCLE
The Data Analysis Cycle, Evaluation and Analysis, Obtaining Data Files, Performing the Audit, File Format Types, Preparation for Data Analysis, Arranging and Organizing Data, Statistics and Sampling, Descriptive Statistics, Inferential Statistics
UNIT III DATA ANALYTICAL TESTS
Benford’s Law, Number Duplication Test, Z-Score, Relative Size Factor Test, Same-Same-Same Test, Same-Same-Different Test
UNIT IV ADVANCED DATA ANALYTICAL TESTS
Correlation, Trend Analysis, , GEL-1 and GEL-2 , Skimming and Cash Larceny, Billing schemes and Data Familiarization, Benford’s Law Tests, Relative Size Factor Test, Match Employee Address to Supplier data
UNIT V ELECTRONIC PAYMENTS FRAUD PREVENTION
Payroll Fraud, Expense Reimbursement Schemes, Register disbursement schemes
COURSE OUTCOMES:
CO1:Formulate reasons for using data analysis to detect fraud.
CO2:Explain characteristics and components of the data and assess its completeness.
CO3:Identify known fraud symptoms and use digital analysis to identify unknown fraud symptoms.
CO4:Automate the detection process.
CO5:Verify results and understand how to prosecute fraud
TOTAL: 45 PERIODS
REFERENCES
1. Sunder Gee, “Fraud and Fraud Detection: A Data Analytics Approach”, Wiley, 2014, ISBN: 978-1-118-77965-1
2. Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke, “Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques: A Guide to Data Science for Fraud Detection”, Wiley and SAS Business Series, 2015
3. Han, Kamber, “Data Mining Concepts and Techniques”, 3rd Ed., Morgan Kaufmann Publishers, 2012
4. Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman, “Mining of Massive Datasets”, Cambridge University Press, 2nd Ed., 2014.