A machine learning based approach to identify key drivers for improving corporate’s esg ratings





ESG Investing, ML model, Socially responsible investing, Sustainable investing, Green finance


Investors increasingly non-financial factors as part of their risk analysis process and growth assessments of corporates. Machine learning (ML) models for predicting ESG scores are an extremely useful tool to help investors make more informed decisions on their portfolios. Such a tool with wide-encompassing alternative data can be useful to the investors. The use of such datasets and machine learning models for ESG ratings can continuously improve the accuracy and reliability of those models. Using machine learning algorithms to identify key drivers of ESG ratings is an effective way of improving portfolio performance. Although the current state of ESG ratings is relatively static, data collection and mapping methodologies are evolving. As more data becomes available, the noise in ESG factors will become less important.

This unique document provides a machine learning algorithm for predicting an ESG rating based on a company's financial and non-financial attributes. The financial and non-financial attributes of corporations are extracted from Moody's Orbis and Ratings from S&P. The objective here is to predict the ESG rating of companies where the ESG rating is not easily accessible. At the same time, this approach would allow investors to have a suitable framework for investments based on ESG ratings.  With the latest financial and non-financial disclosure by a corporate an ESG score can be predicted which can be used to identify its riskiness with a corresponding increase/decrease of ESG score.

Author Biographies

Dwijendra Dwivedi, Kracow University of Economics, (Poland)

Professional with 20+ years of subject matter expertise creating right value propositions for analytics and AI. A post-Graduate in Economics from Indira Gandhi Institute of Development and Research and perusing PHD from crackow university of economics Poland. He has presented in more than 20 international conference and published a number of Scopus indexed paper on AI adoption in many areas. Academic interest includes econometrics, climate risk, machine learning and risk management . Author has contributed to more than 6 books by springer and other publishers.

Saurabh Batra, Delhi University, (India)

Data science professional working in analytical consulting with expertise in handling end-to-end data analytics projects across different sectors and markets. He holds an experience of 12+ years and has specialized in the BFSI domain with deep experience in customer analytics. He is a B.tech.(ECE) graduate and holds a degree of Masters in Business Economics from Delhi University. He has delivered data science training and has trained both fresh graduates and lateral experience holders in the past. He has submitted multiple research papers in AI like “Why we need an AI Regulator”," The Impact of loyalty programs on customer satisfaction and customer loyalty for a retail store”, “Impact of women leadership and ESG Ratings and in organizations: A time series segmention study from india”, “Risk Scorecards using alternative sources of data for credit risk applications”, “Deep learning models for customer  journey analytics in Banking”, “Machine Learning Model to predict ESG Score” and has presented in some international conferences.

Yogesh Kumar Pathak, Indian Institute of Management Lucknow (India)

Yogesh Kumar Pathak is a Risk Management Professional with 18+ years of corporate experience. He has advised several institutions, including banks, regulatory agencies, corporates on risk governance and implementation. He speaks on risk topics on frequent basis with industry leaders from banking, insurance, telcos, and government institutions. He is an engineering graduate from Indian Institute of Technology (IIT) Bombay and holds post-graduate diploma in management from Indian Institute of Management (IIM) Lucknow. Currently, he is leading Risk Management Practice for SAS Institute for Middle East, Africa, Turkiye and Central Asia.


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How to Cite

Dwivedi, D., Batra, S., & Pathak, Y. K. (2023). A machine learning based approach to identify key drivers for improving corporate’s esg ratings. Journal of Law and Sustainable Development, 11(1), e0242. https://doi.org/10.37497/sdgs.v11i1.242