AKTU Artificial Intelligence in Finance (BMBAFM05) Syllabus PDF – MBA (Business Analytics) 4th Semester
Artificial Intelligence in Finance BMBAFM05
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Syllabus — Artificial Intelligence in Finance
Download PDFBMBAFM05 · 3 credits · L-T-P 3-0-0
Course Objectives
- Introduce students to the core principles and innovations of AI in modern financial services.
- Analyze investor behavior, market reactions, and classical financial theories through AI-driven tools.
- Explore AI applications across insurance, credit scoring, and risk analytics.
- Develop analytical skills for wealth management using AI techniques like algorithmic trading and portfolio optimization.
- Understand ethical, regulatory, and governance challenges in AI-driven finance.
Unit 1: Foundations of AI in Finance and Quantitative Analysis (8 hrs.)
Introduction to AI in Finance, Evolution of FinTech and key components, Metaverse and financial innovation, Decentralized Finance (DeFi): Concepts and Applications, NFTs and asset tokenization in finance, Blockchain's impact on traditional financial systems, Robo-advisors and digital lending
Unit 2: Behavioral Finance, Event Studies, and Financial Theory (8 hrs.)
Introduction to behavioral finance and cognitive biases, Psychology of investors: Herding, anchoring, overconfidence, Prospect theory and loss aversion, Event study methodology: Concept and setup, Application of AI in financial event detection, Measuring abnormal returns from events, Text mining for financial news impact
Unit 3: AI in Insurance and Credit Risk (8 hrs.)
Insurance Fundamentals, The Insurance Value Chain, AI in Product Development and Behavioural Pricing, Personalisation & Customer Experience, Claims Management and Fraud Detection, Credit Scoring Fundamentals, Alternative Credit Scoring Models, Opportunities and Challenges in Credit Risk Assessment
Unit 4: AI in Wealth and Asset Management (8 hrs.)
AI in Wealth and Asset Management, Benefits of AI in Wealth and Asset Management, Market Research and Data Analytics, Investment Strategy and Algorithmic Trading, Robo-Advisory: Concepts and Industry Examples, Portfolio Optimization using Machine Learning, Client Personalization and Behavioral Profiling with AI
Unit 5: Ethics, Risks, and Human-AI Collaboration in Finance (8 hrs.)
Ethical Issues in AI Models, Model Validation and Governance, Cooperation Between Humans and Machines, Regulatory Frameworks and AI Risk Mitigation, Bias and Fairness in Financial AI Models, Explainable AI (XAI) in Financial Decision-Making
Course Outcomes
| CO Code | Course Outcome Statements | Bloom’s K levels |
|---|---|---|
| CO1 | Understand the foundations and disruptions of AI and FinTech in financial services | K2 |
| CO2 | Analyze behavioral finance concepts and apply AI to event-driven market studies | K3, K4 |
| CO3 | Evaluate AI-driven solutions in insurance, credit scoring, and fraud detection | K5 |
| CO4 | Apply machine learning for wealth management, portfolio optimization, and robo-advisory platforms | K4, K6 |
| CO5 | Assess ethical issues, governance frameworks, and human-AI collaboration in financial decision-making | K5 |
Suggested Readings
- Artificial Intelligence in Finance by Yves Hilpisch, Publisher: O'Reilly Media
- The AI Book: The Artificial Intelligence Handbook for Investors, Entrepreneurs and FinTech Visionaries by Ivana Bartoletti, Anne Leslie, and Shân M. Millie, Publisher: Wiley
- Behavioral Finance: Psychology, Decision-Making, and Markets by Lucy Ackert & Richard Deaves, Publisher: Cengage Learning
- Machine Learning for Asset Managers by Marcos López de Prado, Publisher: Cambridge University Press
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