credit_risk_assessment_system_simulation

Overview

The project is made to make real-time credit risk assessment based on the (bank) client profiles it is given by leveraging big data processing and machine learning, simulating how a bank might evaluate and manage credit risk for its clients.

Simulated Output Example

How It Works

1. Data Ingestion: The system simulates the ingestion of financial data including credit scores, account balances, and transaction histories for thousands of clients.

2. Data Processing: Leveraging PySpark's distributed computing capabilities, the raw data is cleaned, transformed, and prepared for analysis at scale.

3. Machine Learning Model:

   • A Random Forest Regressor is trained on historical data to predict credit risk scores.
   • The model considers multiple factors including credit score, account balance, and transaction frequency.

4. Risk Scoring:

   • Each client's risk is calculated using an algorithm that normalizes and weights various financial factors.
   • The calculated risk score is then used to categorize clients into risk categories ranging from "Very Low Risk" to "Very High Risk".

5. Dynamic Rate Calculation:

   • Based on the risk assessment, the system dynamically calculates personalized savings APY and lending rates for each client.
   • This ensures competitive rates while managing the bank's overall risk exposure.

6. Real-time Assessment: As new financial data comes in, the system can rapidly reassess a client's credit risk, allowing for up-to-date risk management.

7. Risk Dashboard: A comprehensive dashboard provides bank managers with key metrics including average credit scores, risk distributions, and potential high-risk clients.

Installation

Clone the repository:

git clone https://github.com/dvelkow/credit_risk_data_lake_for_lending

Install the required packages:

pip install -r requirements.txt

Run the main data lake:

python main.py

(It would run with random/mock data, but you can easily connect it to a real database through the main.py file)