Degree Level Course
Financial Forensics
An introduction to Finance and Accounting, The life cycle of a financial transaction, Areas where AI/ML is used in the Finance Industry, Importance of Model Explainability in the Regulated World, An introduction to solving real world finance problems – Credit Card Fraud Detection, Identity Fraud Detection, Anti Money Laundering Scenarios
Course ID: BSGN3002
Course Credits: 4
Course Type: Elective
Pre-requisites: None
Course structure & Assessments
12 weeks of coursework, weekly online assignments, 2 in-person invigilated quizzes, 1 in-person invigilated end term exam. For details of standard course structure and assessments, visit Academics page.
| WEEK 1 | INTRODUCTION TO ACCOUNTING AND FINANCE: Financial Statements | Uses relevance of financial statements | Decision making in the financial arena | Need for finance and accounting in a business world | WEEK 2 | FRAUD VULNERABILITIES AND FINANCE: Types of fraud | Why do they occur | Where do they occur | Detecting red flags | WEEK 3 | FORENSIC ACCOUNTING - 1: Intro to Forensic Accounting | Source of assignments and referrals for a forensic accountant | Role of the forensic accountant as an expert - identify any conflicts | WEEK 4 | FORENSIC ACCOUNTING - 2: Scope of forensic accounting | Process of forensic accounting - Analysis | WEEK 5 | BENFORD LAW AND IMPLEMENTATION: Benford Law | Using Benford Law in Excel to detect audit fraud | WEEK 6 | AGING ANALYSIS, PARETO AND OUTLIERS: Detecting fraud using Aging Analysis in Excel | Creating a Pareto Chart | Outlier identification | WEEK 7 | FINANCIAL TRANSACTION LIFECYCLE: Transaction Lifecycle | Where is AI/ML used in the present world? | WEEK 8 | ENTITY RESOLUTION: What is an entity in a transaction? | Entity resolution | WEEK 9 | ANOMALY DETECTION - 1: Supervised Anomaly Detection | WEEK 10 | ANOMALY DETECTION - 2: Unsupervised Anomaly Detection | WEEK 11 | ANOMALY DETECTION - 3: Time Based Anomaly Detection | WEEK 12 | Model Explainability | Financial Data Visualization using Tableau |
Prescribed Books
The following are the suggested books for the course:
Robert N Anthony, David F Hawkins and Kenneth A Merchant, Accounting: Text and Cases
Stephen H Penman, Financial Statement Analysis and Security Valuation, Tata McGraw Hill Company, Third Edition.
Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2009). Introduction to algorithms. Mit Press.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (n.d.). An introduction to statistical learning: With applications in R.
About the Instructors
Prof. G Arun Kumar is a faculty in the Department of Management Studies at IIT Madras. His areas of interest are Finance and Corporate Governance issues. He was actively involved in "Evidence based impact assessment of development" and have worked on field based Impact assessment assignments in the peri-urban or rural setting across the country. Some of these assignments include development of micro enterprises, microfinance, migration & remittances, digital literacy, skill development, women empowerment & women led micro enterprises, etc.
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