Siddhant Academy

End-to-End Data Science Projects in Banking and Finance

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Siddhant Bhattarai
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4 min read

Data science has revolutionized the banking and finance sectors, providing solutions to complex business challenges. Below is a collection of solved end-to-end data science projects tailored to banking and finance. Each project is explained in detail, with alternative approaches for dataset access and potential extensions for practical application.

1. Credit Card Fraud Detection with Isolation Forest and LOF

  • Objective: Identify fraudulent credit card transactions using anomaly detection models like Isolation Forest and Local Outlier Factor (LOF).

  • Steps:

    1. Preprocess transaction data to identify patterns.

    2. Train Isolation Forest and LOF models to detect anomalies.

    3. Evaluate model performance using metrics like Precision and Recall.

  • Dataset: Kaggle Credit Card Fraud Detection Dataset.

  • Alternative: Create synthetic datasets using tools like imbalanced-learn.

2. Loan Eligibility Prediction Using Gradient Boosting

  • Objective: Predict whether a loan applicant is eligible based on factors such as credit score and income.

  • Steps:

    1. Preprocess customer data and handle missing values.

    2. Train a Gradient Boosting Classifier for predictions.

    3. Evaluate model accuracy and fairness across demographic groups.

  • Dataset: Loan Prediction Dataset.

  • Alternative: Simulate applicant data using random generation techniques.

3. Building an End-to-End MLOps Pipeline for Loan Prediction on GCP

  • Objective: Deploy a loan eligibility prediction model using an optimal MLOps pipeline on GCP.

  • Steps:

    1. Train and test the model locally using Python.

    2. Set up an automated CI/CD pipeline using GCP services like Cloud Build.

    3. Deploy the model using Google Cloud Run or Kubernetes.

  • Dataset: Use the same dataset as Project 2.

  • Alternative: Generate data using pandas to simulate loan applications.

4. Predictive Analytics for Working Capital Optimization

  • Objective: Forecast customer and supplier payment timings to optimize working capital.

  • Steps:

    1. Analyze historical payment data to identify trends.

    2. Train regression models to predict payment delays.

    3. Implement optimization algorithms to manage cash flow.

  • Dataset: Public financial datasets like UCI Machine Learning Repository.

  • Alternative: Create synthetic payment schedules for experimentation.

5. Portfolio Optimization Models in R for Financial Risk Management

  • Objective: Build machine learning models to optimize investment portfolios for maximum returns with minimal risk.

  • Steps:

    1. Perform exploratory data analysis on asset price data.

    2. Use Markowitz Portfolio Theory and machine learning for optimization.

    3. Evaluate portfolio performance using Sharpe Ratio.

  • Dataset: Yahoo Finance Stock Data.

  • Alternative: Simulate asset performance data using random walks.

6. Insurance Pricing Forecast Using XGBoost Regressor

  • Objective: Predict insurance premiums using regression techniques like XGBoost.

  • Steps:

    1. Prepare customer and policy data for training.

    2. Train XGBoost models for premium prediction.

    3. Analyze model output to ensure compliance with actuarial standards.

  • Dataset: Public insurance datasets on Kaggle or simulated datasets.

  • Alternative: Create synthetic insurance data with variables like age, coverage, and claim history.

7. Deep Autoencoders for Anomaly Detection in Credit Card Transactions

  • Objective: Detect anomalies in transactional data using deep autoencoders.

  • Steps:

    1. Preprocess transactional data for input to the model.

    2. Train a deep autoencoder to reconstruct normal transactions.

    3. Flag transactions with high reconstruction errors as anomalies.

  • Dataset: Credit Card Fraud Detection Dataset.

  • Alternative: Generate synthetic data using Python libraries like faker.

8. Classification Algorithms for Banking Digital Transformation

  • Objective: Examine digital transformation processes in banking using classification techniques.

  • Steps:

    1. Analyze customer interaction data from traditional and digital channels.

    2. Train classifiers like Logistic Regression and Decision Trees.

    3. Evaluate models to identify trends in digital adoption.

  • Dataset: Banking customer interaction datasets on Kaggle.

  • Alternative: Simulate customer interaction logs.

9. Credit Card Default Prediction Using Machine Learning

  • Objective: Predict the likelihood of credit card default by analyzing customer data.

  • Steps:

    1. Preprocess credit card application and usage data.

    2. Train classification models like Random Forest and Gradient Boosting.

    3. Evaluate models using metrics like Precision, Recall, and F1-score.

  • Dataset: Taiwan Credit Default Dataset.

  • Alternative: Create synthetic datasets with customer attributes.

10. Time Series Analysis for Stock Market Forecasting in R

  • Objective: Predict stock prices using time series models to assist in investment decision-making.

  • Steps:

    1. Analyze historical stock price data for trends and seasonality.

    2. Train time series models like ARIMA and Prophet.

    3. Evaluate predictions using time-series-specific metrics.

  • Dataset: Yahoo Finance Stock Data.

  • Alternative: Generate synthetic stock data using time series generators in R.

Conclusion

These projects provide a comprehensive understanding of how data science can be leveraged to solve complex problems in banking and finance. From anomaly detection to portfolio optimization, these applications demonstrate the transformative power of machine learning in real-world scenarios. For inaccessible datasets, synthetic data offers a viable alternative to get started. Let me know if you need further details on any project!