A Strategic Business Intelligence Framework for Sustainable Asset Management in Finance

Abstract

Amidst the evolving landscape of finance, integrating sustainability principles into asset management stands as a pivotal pursuit for fostering long-term value creation. This research addresses the symbiotic relationship between business intelligence methodologies and sustainable asset management within the domain of finance. Leveraging advanced machine learning techniques including logistic regression, XGBoost, and CatBoost, this study delves into the exploration of sustainable finance practices and their implications for optimized asset management strategies. The study analyzes and models Asset data, aiming to understand the multifaceted dynamics and interdependencies shaping sustainable asset management decisions. Logistic regression serves as a foundation to model the relationships between variables, while XGBoost and CatBoost handle the complexities of categorical attributes, predicting outcomes related to sustainability metrics and financial performance indicators within the asset portfolio. Through comprehensive analyses and visualizations, this research illuminates critical insights into the influential factors driving sustainable asset management decisions. The findings underscore the significance of leveraging data-driven methodologies to optimize asset management strategies aligned with environmental, social, and governance considerations.