Enhancing ESG Insights Using Machine Learning: A Case Study of Top Performing Banks in Indonesia

This research related to the discover ESG practices by Top Performing Banks in Indonesia based on their Return on Asset (ROA) and Net Interest Margin (NIM). Due to the shifting the interest of the investor and the world condtion. Recently, investors have shown a growing inclination to incorporate ESG (Environmental, Social, and Governance) considerations into their decision-making processes. This trend is driven by a heightened awareness of environmental risks, particularly those linked to sustainability. In response to climate change, there is also a growing public interest in adopting more sustainable practices. Due to the broad and sometimes ambiguous definition of ESG, identifying the specific practices that should be implemented by banks remains challenging. Research on frameworks to guide these actions is still limited. For instance, explores the role of knowledge management in facilitating the integration of ESG factors. This research aims to create an ESG knowledge discovery framework with Natural Language Process (NLP) based on DBSCAN results of several companies which have good financial performance such as Return on Asset (ROA) and Net Interest Margin (NIM). The result will be analyzed by NLP, especially TF-IDF (Term Frequency - Inverse Document Frequency) and visualized by Word Cloud. The priority of Top Performing Bank in Indonesia related to the environment indicator conduct the activity which focus on carbon, green, and water. For social indicator, the main focus are employee, training, and financial. Lastly, for governance indicator, they are most prominent activity to the security, data, and policy. Overall, leading banks in Indonesia tend to prioritize environmental aspects in their operations. Companies with strong environmental initiatives have been shown to positively impact their earnings and financial performance.