MBA Finance Project Topics — 2025 List
Fresh, practical and high-scoring MBA finance project topics for 2025 — each topic includes a short explanation, why it is a good choice, suggested objectives and possible data sources.
How to use this page
Choose a topic from the lists below, read the short description and the "why choose" note. After selecting a topic, use the sample objectives and data sources provided to shape your project proposal. If you want, copy the topic and ask me to write a research proposal or the full report for you.
What makes a high-scoring topic?
- Clear research question and measurable objectives.
- Available data (primary or reliable secondary sources).
- Appropriate methodology (quantitative, qualitative or mixed).
- Practical relevance and actionable recommendations.
Quick tips
- Start with a short literature scan: 8–12 papers or reports.
- Design your methodology before collecting data.
- Save all data sources and cite them properly.
- Keep visualisations clear and focused on your findings.
Top Trending Topics (with explanations)
1. AI-Based Credit Risk Modelling in Banks
Use machine learning algorithms to predict probability of default, compare traditional logistic regression with tree-based and neural models.
Why choose: High demand for data-driven credit decisions; great for using secondary bank data or simulated datasets.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
2. FinTech Disruption in Indian Banking Sector
Study how fintech players changed lending, payments and customer acquisition—focus on UPI, neo-banks, and lending apps.
Why choose: Timely and wide scope for qualitative + quantitative analysis.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
3. Impact of UPI & Digital Payments on Cashless Economy
Analyze transaction trends, merchant adoption, and cash reduction in specific states or cities.
Why choose: Data-rich topic; easy to find government and NPCI reports as sources.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
4. ESG Investing & Its Impact on Portfolio Performance
Compare ESG-screened portfolios with benchmark portfolios across returns, volatility, and drawdown.
Why choose: ESG is a globally trending research area—ample published reports and fund data available.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
5. Working Capital Management in MSMEs
Empirical study on receivables, inventory and payables management in small firms and effect on profitability.
Why choose: Practical, high-relevance for consultancy-style projects.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
6. Predictive Analytics for Stock Market Forecasting
Build simple predictive models (ARIMA, LSTM, Random Forest) on selected stock indices or sectoral ETFs.
Why choose: Great for students who want to mix coding with finance theory.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
7. Cryptocurrency Regulation in India & Investor Behaviour
Study regulatory milestones, investor sentiment, and fund flows into crypto assets.
Why choose: High interest topic; combine survey + secondary data.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
8. NPAs in Indian Banks: Causes, Trends & Recovery Strategies
Time-series analysis of NPA trends, sectoral concentration and effectiveness of recovery measures.
Why choose: A classic banking topic with strong scoring potential.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
9. Comparative Analysis of Mutual Funds vs ETFs (2020–2025)
Compare returns, costs, liquidity and investor flows between mutual funds and ETFs across categories.
Why choose: Very practical and data-driven, investors and students both benefit.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
10. Impact of RBI Monetary Policy on Inflation & Market Stability
Event-study approach around key policy rate changes to measure market and inflation responses.
Why choose: Good for students wanting macro-finance intersection.
Sample Objectives
- Review literature & define variables.
- Collect dataset & perform exploratory analysis.
- Build models and compare performance.
Suggested Methodology
Quantitative (regression, ML), case study.
Possible Data Sources
RBI, NSE, BSE, NPCI, company filings, Kaggle.
Easy & Quick Project Ideas (good for beginners)
- Financial Ratio Analysis of a Listed Company (5 years)
- Comparative Study of Two Mutual Funds
- Impact of Dividend Policy on Share Price (Event Study)
- Customer Satisfaction in Mobile Banking App
- Working Capital Analysis of a Manufacturing Firm
- Effect of Interest Rate Changes on Housing Loan EMI
Advanced/Research-Oriented Topics
For students who want to go deeper: choose topics with mixed methods, build original models, run robustness checks and include policy implications.
- Time-Varying Beta & Risk Premiums in Emerging Markets.
- High-Frequency Trading Impact on Volatility—An Intraday Study.
- Behavioral Biases & Retail Investor Performance during Volatile Markets.
- Credit Scoring with Alternative Data (SMS, Utility Payments).
- Effectiveness of Government Credit Guarantee Schemes for MSMEs.
How to Structure Your Report (Suggested)
Introduction
Context, problem statement and research questions.
Literature Review
Summarize 8–12 relevant papers/reports.
Methodology
Data, variables, models and tools used.
Analysis & Findings
Present tables, charts and interpretation.
Conclusion & Recommendations
Summarize contributions and practical advice.
References & Appendices
Cite sources and include data tables or survey forms.
Sample Research Proposal (Short)
Title
AI-Based Credit Risk Modelling: A Comparative Study of Machine Learning Algorithms
Objectives
- To compare predictive accuracy of logistic regression, random forest and XGBoost.
- To identify the most important features driving default risk.
- To recommend a model for small-bank implementation.
Methodology
Secondary data from bank loan datasets / Kaggle. Use Python for preprocessing and modeling. Evaluate models using AUC, F1-score and calibration plots.
Expected Outcomes
A ranked comparison of models and a feasibility analysis for deployment in a bank’s underwriting process.
Data Sources & Tools
Public Data
- RBI Database of Indian Economy
- NSE/BSE historical data
- NPCI / UPI transaction reports
- SEBI & company annual reports
Tools
- Excel / Google Sheets for quick analysis
- Python (pandas, sklearn, statsmodels) for modeling
- R (tidyverse) for statistics
- PowerBI / Tableau for visualisations
FAQs
How to choose the right MBA finance project topic?
Pick a topic that balances data availability, your interest, and your time frame. If you have access to company data or internships, prefer company-based projects; otherwise choose data-rich public topics like UPI, mutual funds, or ESG.
How long should the project report be?
Typically 40–80 pages depending on your college guidelines. Focus on clear structure: Introduction, Literature Review, Methodology, Analysis, Findings, Conclusion and Recommendations.
Can I use public data sources for quantitative projects?
Yes. Use RBI, NSE, BSE, SEBI, National Statistical Office, NPCI, CMIE, Yahoo Finance, Quandl, or Kaggle datasets. Always cite.