JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

Course Objectives:

• 1. Discuss the overall process of how data analytics is applied
• 2. Discuss how data analytics can be used to better address and identify risks
• 3. Help mitigate risks from fraud and waste for our clients and organizations

Course Outcomes:

• 1. Formulate reasons for using data analysis to detect fraud.
• 2. Explain characteristics and components of the data and assess its completeness.
• 3. Identify known fraud symptoms and use digital analysis to identify unknown fraud symptoms.
• 4. Automate the detection process.
• 5. Verify results and understand how to prosecute fraud

UNIT - I

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

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

Payroll Fraud, Expense Reimbursement Schemes, Register disbursement schemes

Text Book:

• 1. Fraud and Fraud Detection: A Data Analytics Approach by Sunder Gee, Wiley R19 M. TECH. CN/CNIS/CYBER SECURITY M. Tech. (CNIS/CN/Cyber Security)