An Integrated Business Intelligence Framework for Sustainable Risk Mitigation

Abstract

This research paper examines the critical juncture of business intelligence and sustainable risk management in response to the increasing challenges faced by modern businesses. Our study recognizes that organizations must navigate uncertainties while prioritizing sustainability. It focuses on analyzing credit risk data. We present a comprehensive examination of predictive performance using Logistic Regression, Decision Tree, and K-Nearest Neighbors classifiers augmented by the Synthetic Minority Over-sampling Technique (SMOTE) for class rebalancing. The empirical findings presented through detailed tables and figures reveal intricate relationships and patterns within the data. This research also contributes to the broader discourse on responsible business practices by highlighting the integration of business intelligence in sustainable risk mitigation. Moreover, comparative analysis of machine learning algorithms under various resampling techniques further strengthens the framework’s reliability.