Credit scores play a vital role in determining whether an individual or business is eligible for a loan or credit. Traditional credit scoring models use a set of predefined rules and statistical models to assess creditworthiness. However, these models can be inaccurate and do not always capture the complex relationships between credit-related variables. Therefore, it is essential to have an automated system in place that can accurately predict credit scores.
The objective of this project is to develop an automated credit score classification system using deep learning and machine learning techniques to predict creditworthiness accurately.
The proposed system will use both deep learning and machine learning algorithms to classify three credit scores i.e Standard, Good and Poor. The system will be trained on a dataset of historical credit scores, which will include features such as credit history, income, employment status, and debt-to-income ratio. The system will employ several techniques to improve the accuracy of credit score classification, including data preprocessing, feature engineering, and model tuning. Deep learning algorithms, such as neural networks, will be used to capture complex relationships between variables, while machine learning algorithms, such as decision trees (random forest method used), logistic regression amd xgboost will be used to identify important features.The system will be tested using a separate test dataset to evaluate its accuracy and generalization performance. The system's performance will be compared to traditional credit scoring models to assess its effectiveness.
The system has achieve high accuracy in credit score classification, which will help financial institutions make more informed lending decisions. The system will also be scalable, meaning it can handle large volumes of credit score data without compromising its accuracy.
The proposed automated credit score classification system will help financial institutions improve their credit scoring models and make more accurate lending decisions. It will be an essential tool for banks, credit unions, and other financial institutions that rely on credit scores to assess creditworthiness.