Authors : Dr V Thangavel
DOI : 10.5281/ZENODO.18430075
Volume : 20
Issue : 2
Year : 2025
Page No : 2-7
Risk analysis is a fundamental component of loan processing in the banking sector, where accurate assessment of borrower creditworthiness is essential for reducing financial risk and maintaining institutional stability. The rapid growth of digital banking systems has led to the generation of large and complex financial datasets, making conventional risk evaluation methods increasingly inadequate. This study proposes a structured risk analysis framework for loan processing based on deep learning techniques to support intelligent and automated decision-making. A dedicated data preprocessing stage is incorporated to improve data quality, where a median filter is employed to manage noise, missing values, and outliers commonly found in banking data. The framework explores the use of multiple deep learning models, including Deep Neural Networks (DNN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks, for modelling loan-related risk factors. By integrating advanced preprocessing methods with deep learning-based analysis, the proposed approach aims to strengthen the reliability, consistency, and scalability of risk assessment processes in modern banking environments.