1. Credit Card Fraud Detection

Goal: Detect fraudulent transactions in real time to minimize losses.

Data Needed:

• Transaction ID
• Timestamp
• Amount
• Merchant category
• Location
• Fraud label (Yes/No) (Dataset: Kaggle Credit Card Fraud Detection)

Steps:

1. Clean missing values and scale features.
2. Handle imbalance using SMOTE or anomaly detection.
3. Train models (Logistic Regression, Random Forest, XGBoost).
4. Deploy fraud probability scoring.

Tools: Python (Scikit-learn, PyCaret), Power BI for monitoring.

Deliverable:

• Real-time fraud detection dashboard with alert system.

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2. Stock Price Movement Analysis

Goal: Analyze historical stock prices to identify patterns and trends.

Data Needed:

• Date
• Open, High, Low, Close prices (OHLC)
• Trading volume (Dataset: Yahoo Finance API)

Steps:

1. Import data via API.
2. Calculate technical indicators (SMA, EMA, RSI, MACD).
3. Visualize candlestick charts.
4. Backtest simple trading strategies.

Tools: Python (yfinance, mplfinance), Tableau.

Deliverable:

• Interactive stock trend dashboard with indicators.

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3. Cryptocurrency Volatility Dashboard

Goal: Monitor and compare volatility of cryptocurrencies in real time.

Data Needed:

• Timestamp
• Crypto pair prices (BTC/USD, ETH/USD, etc.)
• Market cap, volume (Dataset: Binance or CoinGecko API)

Steps:

1. Pull live or historical data.
2. Calculate daily returns & volatility (std dev of returns).
3. Compare assets in charts.

Tools: Python (Pandas, Plotly), Power BI.

Deliverable:

• Volatility heatmap + trend charts per coin.

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4. Loan Default Prediction

Goal: Predict whether a borrower will default on a loan.

Data Needed:

• Loan ID
• Applicant demographics
• Loan amount, interest rate
• Credit score, income
• Default label (Dataset: Lending Club Loan Data)

Steps:

1. Clean and encode categorical data.
2. Feature engineering (Debt-to-Income ratio, credit utilization).
3. Train classification models (Logistic Regression, Random Forest).

Tools: Python, Tableau for visualization.

Deliverable:

• Default risk dashboard with borrower profile scoring.

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5. Portfolio Risk and Return Analysis

Goal: Evaluate investment portfolio performance using risk-adjusted metrics.

Data Needed:

• Asset returns
• Weights in portfolio
• Risk-free rate (Dataset: Yahoo Finance API)

Steps:

1. Calculate portfolio expected return & variance.
2. Compute Sharpe ratio, Beta, Alpha.
3. Visualize efficient frontier.

Tools: Python (NumPy, Pandas, Matplotlib), Excel for quick calcs.

Deliverable:

• Risk-return dashboard with optimization recommendations.

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6. Budget Tracker with Spending Trends

Goal: Track income vs expenses and identify spending habits.

Data Needed:

• Transaction date
• Category (Food, Rent, Utilities, etc.)
• Amount
• Payment method

Steps:

1. Categorize transactions.
2. Calculate monthly spending by category.
3. Compare planned vs actual budgets.

Tools: Power BI, Google Sheets.

Deliverable:

• Monthly budget tracking dashboard with trend lines.

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7. Financial News Sentiment Impact

Goal: Measure how news sentiment affects stock price movements.

Data Needed:

• News headlines
• Publication date/time
• Stock price before/after news (Dataset: Financial News from Kaggle + Yahoo Finance prices)

Steps:

1. Perform sentiment analysis on headlines (VADER/TextBlob).
2. Link sentiment scores with short-term stock returns.
3. Visualize correlation trends.

Tools: Python (NLTK, VADER, Pandas), Tableau.

Deliverable:

• Sentiment vs stock movement dashboard.

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8. Forex Exchange Rate Forecast

Goal: Predict future foreign exchange rates for currency trading.

Data Needed:

• Historical currency pair rates (USD/INR, EUR/USD, etc.) (Dataset: OANDA or Yahoo Finance API)

Steps:

1. Aggregate data into daily or hourly rates.
2. Apply ARIMA/Prophet models.
3. Visualize predicted vs actual trends.

Tools: Python (Prophet, Statsmodels), Power BI.

Deliverable:

• Exchange rate forecasting dashboard with confidence intervals.

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9. Bank Branch Profitability Report

Goal: Evaluate profitability and performance of bank branches.

Data Needed:

• Branch ID
• Revenue from loans, deposits, services
• Operational expenses
• Customer base size

Steps:

1. Aggregate KPIs per branch.
2. Calculate profit margin = (Revenue – Cost) / Revenue.
3. Compare across regions.

Tools: Power BI, SQL.

Deliverable:

• Profitability heatmap + ranking by branch.

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10. ATM Transaction Anomaly Detector

Goal: Detect suspicious ATM transactions to prevent fraud.

Data Needed:

• Transaction ID
• ATM location
• Amount withdrawn
• Time of day
• Card type, account type

Steps:

1. Identify unusual withdrawal amounts/times.
2. Apply anomaly detection algorithms (Isolation Forest, Local Outlier Factor).
3. Flag suspicious transactions in dashboard.

Tools: Python (Scikit-learn), Tableau.

Deliverable:

• Live anomaly detection dashboard with alerts.