AUTOMATED CREDIT SCORE BAND CLASSIFICATION USING MULTI DIMENSIONAL FINANCIAL BEHAVIOR DATA

Abstract

Credit scoring plays a vital role in financial institutions for evaluating the creditworthiness of
individuals before granting loans or credit facilities. Traditional credit scoring models rely on
limited financial indicators and manual evaluation methods, which may not effectively
capture complex financial behavior patterns. With the rapid growth of digital financial
systems, large volumes of financial behavior data such as transaction history, spending
patterns, repayment habits, and credit utilization have become available. Analyzing this
multi-dimensional financial behavior data can significantly improve the accuracy of credit
risk assessment. This project proposes an automated credit score band classification system
using machine learning techniques. The system analyzes multiple financial attributes to
classify individuals into predefined credit score bands such as Poor, Fair, Good, and
Excellent. The proposed approach involves data preprocessing, feature engineering, model
training, and performance evaluation using classification algorithms such as Random Forest
and Logistic Regression. The dataset containing financial behavior records is divided into
training and testing sets to evaluate the predictive performance of the model. Experimental
results demonstrate that the machine learning model can effectively classify credit score
bands with high accuracy, improving decision-making efficiency for financial institutions.
The proposed system provides a reliable, automated, and scalable solution for credit risk
assessment and supports faster and more accurate loan approval processes.