/Profitability-Analysis-with-Monte-Carlo-Simulation-for-Bank-Loan-Approval

This repo analyzes the profitability of approving a bank loan for two projects by using Monte Carlo Simulation. Based on a Harvard Business Review case study, it considers cash flow uncertainty and investment returns. The analysis simulates various scenarios, offering insights for informed decision-making on the loan's potential profitability.

Profitability Analysis with Monte Carlo Simulation for Bank Loan Approval

GitHub Pages: https://priauwindu.github.io/Profitability-Analysis-with-Monte-Carlo-Simulation-for-Bank-Loan-Approval/

Description:

This repository presents a comprehensive analysis on the profitability of approving a bank loan for funding two distinct projects, based on the case study of Crawford Development Co. and Southeast Bank of Texas from Harvard Business Review. The analysis employs Monte Carlo Simulation, taking into account the inherent uncertainty in cash flows and investment returns associated with the projects. By simulating a large number of scenarios, the analysis provides insights into the potential profitability of the loan and assists in making informed decisions.

Key Features:

  1. Monte Carlo Simulation: The analysis utilizes Monte Carlo Simulation techniques to model the uncertain nature of cash flows and investment returns for the two projects.
  2. Comparative Analysis: The repository offers a comparative analysis between the two projects, allowing for an assessment of their respective profitability and risk profiles.
  3. Sensitivity Analysis: The analysis includes a sensitivity analysis that explores different loan rates to identify the optimal rate that maximizes profitability for the bank.
  4. Visualizations and Reports: The repository provides interactive visualizations and comprehensive reports to facilitate a clear understanding of the analysis results and aid decision-making processes.
  5. Documentation and Examples: Detailed documentation and example code are included to assist users in replicating the analysis and applying it to their own loan approval scenarios.

By leveraging the power of Monte Carlo Simulation and drawing inspiration from the Crawford Development Co. and Southeast Bank of Texas case study, this repository enables financial institutions and analysts to gain valuable insights into the profitability of bank loans, optimize loan rates, and make data-driven decisions regarding loan approvals.

If you find this repository useful, you may replicate the code here to assist your projects/works but dont forget to properly cite this repository in your work.

Thank you!

Putranegara Riauwindu