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https://github.com/dngvmnh/revolving_credit_behavior_modeling

The Revolving Credit Behavior Modeling project analyzes revolving credit to facilitate flexible access to funds within a credit limit, assisting financial institutions in setting accurate pricing strategies by addressing risk factors like inflation and interest rates.
https://github.com/dngvmnh/revolving_credit_behavior_modeling

credit-contracts financial-flexibility interest-rate-risks model-evaluation predictive-modeling random-forest-regressor revolving-credit simulation time-value-of-money withdrawal-patterns

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The Revolving Credit Behavior Modeling project analyzes revolving credit to facilitate flexible access to funds within a credit limit, assisting financial institutions in setting accurate pricing strategies by addressing risk factors like inflation and interest rates.

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README 

        
## DEMO

**1. Graphing ideal interest rate and withdrawals**

![lai](https://github.com/dngvmnh/VM2C_202400000471/assets/133772077/a3c28e3d-d476-4387-bed4-eb96b90987d5)

**2. Predictions made by model developed from RandomForestRegressor**

![du doan random forest regressor](https://github.com/dngvmnh/VM2C_202400000471/assets/133772077/539b5a86-f0bb-438a-be5c-50031d5054ed)

**3. Simulating remainders**

![so du](https://github.com/dngvmnh/VM2C_202400000471/assets/133772077/2e2c5606-c032-43fe-939d-8d7b3e55624c)



## Project description



This project presents a comprehensive analysis and model for revolving credit, which allows borrowers to withdraw funds multiple times within a credit limit and pay interest only on the withdrawn amount. This approach provides businesses and individuals with greater flexibility in cash flow management to meet short-term financial needs that are unpredictable.



## Introduction



The project investigates the implications of revolving credit agreements between companies and banks. It addresses the challenges faced by banks in managing risks related to inflation, interest rates, and customer behavior while ensuring profitability.



## Problem Formulation and Assumptions



Given the complexity of the problem, it was simplified by certain assumptions detailed in specific problem segments. The core issue revolves around managing and modeling the value of money over time, interest rate risks, and the withdrawal patterns of borrowers.



## Solution Approach



- Time Value of Money: The current value of withdrawals is calculated based on market interest rates and contract terms.

- Interest Rate Risks: Scenarios where market interest rates fluctuate and their impact on the bank's profitability are assessed.

- Withdrawal Patterns: A model to predict customer withdrawal behavior is developed using recursive calculation methods.

- Behavior Simulation: Interest rate fluctuations and customer behavior are simulated using a Decision Tree Regressor and an enhanced Random Forest Regressor algorithm.



## Model Evaluation



The effectiveness of this model is assessed by comparing it against real-world scenarios and data. The model helps in predicting the benefits for both the bank and the borrower under various conditions, thus aiding in setting appropriate interest rates.



## Applications



This mathematical model can be utilized by financial institutions to price revolving credit contracts more accurately. It offers flexibility and can be adapted to various contexts beyond the initial assumptions and data used.