AUTOMATED CREDIT SCORE BAND CLASSIFICATION USING MULTI-DIMENSIONAL FINANCIAL BEHAVIOR DATA

Abstract

Credit scoring is a fundamental process used by financial institutions to evaluate the
creditworthiness of individuals and to make informed lending decisions. Traditional credit
scoring systems mainly depend on limited parameters such as past loan repayment history and
basic financial records. However, these methods often fail to capture the complete financial
behavior of individuals, leading to inaccurate or biased decisions. To address these limitations,
this project focuses on the development of an automated credit score band classification system
using multi-dimensional financial behavior data.
The proposed system utilizes a wide range of financial attributes, including income level,
spending patterns, transaction frequency, credit utilization ratio, repayment behavior, and other
relevant indicators. By considering these multiple dimensions, the system provides a more
comprehensive analysis of an individual’s financial profile. The collected data is preprocessed to
handle missing values, remove inconsistencies, and normalize the dataset for better performance
of the model. Machine learning algorithms such as Decision Trees, Random Forest, and Logistic
Regression are employed to classify individuals into different credit score bands, such as low
risk, medium risk, and high risk. These algorithms are trained using historical financial data,
allowing the model to identify hidden patterns and relationships between various features. The
performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-
score to ensure reliable predictions.
The automation of credit score classification reduces manual effort and human intervention,
thereby minimizing errors and bias in the decision-making process. Additionally, it enables faster
processing of large volumes of data, making it highly suitable for modern financial systems. The
system can be integrated into banking and financial platforms to support real-time credit
evaluation and risk management