This project is about developing a credit scoring model that can leverage, through machine learning algorithms, the credit risk assessment of loan applicants. It will consider applicant details like credit history, income, debt-to-income ratio, and employment status, generate credit scores, and support informed lending decisions.
This project was initiated because enhancing loan approval rates, minimizing credit loss, and correctly managing risk exposure in lending operations are the major problems that banks face. This is a collaborative project in which Data Scientists will be joined by Financial Analysts, Machine Learning Engineers, and Specialists in Risk Management.
Week 1-2: Initial Planning and Requirement Analysis
Define the objectives of the project, scope, and high-level requirements.
Collect all relevant data and necessary resources.
Week 3-5: Data Collection and Preprocessing Phase
Gather data of applicants from various sources; pre-process it.
Identify the relevant features from data for the credit scoring model.
Week 6-8: Model Development and Training Phase
Develop a credit scoring model using machine learning algorithms.
Check the precision and performance of the models on historical data.
Week 9-10: Model Integration and Testing Phase
Integration of the model with existing banking systems and processes for loan approval.
Test and improve the model based on performance metrics and user input.
Week 11-12: Deployment and Monitoring Phase
Model deployment in the banking environment.
Monitoring model performance and making changes as required.
