Environmental, Social, and Governance (ESG) datasets are frequently plagued by significant data gaps, leading to inconsistencies in ESG ratings due to varying imputation methods. This study addresses the missing data issues in ESG datasets using machine learning techniques, comparing K-Nearest Neighbors, Gradient Boosting, Multiple Imputation by Chained Equations (MICE) and Neural Networks. We focus on quantifying the risk induced by data anomalies and provide tools to assess the impacts of this risk on the variability of the scores. By introducing prediction uncertainty using methods such as Predictive Mean Matching and Local Residual Draw, in order to assign confidence measures to individual predictions, we provide a nuanced understanding of prediction uncertainty. Empirical analyses show that these methods improve imputation accuracy and quantify uncertainty, which is required for reliable ESG scoring in banking and finance.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Could artificial intelligence (AI) help companies meet growing expectations for environmental, social and governance (ESG) reporting? Certainly, over the past couple of years, ESG issues have soared in importance for corporate stakeholders, with increasing demands from investors, employees and customers. According to S&P Global, in 2022 corporate boards and government leaders "will face rising pressure to demonstrate that they are adequately equipped to understand and oversee ESG issues -- from climate change to human rights to social unrest."

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Could artificial intelligence (AI) help companies meet growing expectations for environmental, social and governance (ESG) reporting? Certainly, over the past couple of years, ESG issues have soared in importance for corporate stakeholders, with increasing demands from investors, employees and customers. According to S&P Global, in 2022 corporate boards and government leaders "will face rising pressure to demonstrate that they are adequately equipped to understand and oversee ESG issues -- from climate change to human rights to social unrest." ESG investing, in particular, has been a big part of this boom: Bloomberg Intelligence found that ESG assets are on track to exceed $50 trillion by 2025, representing more than a third of the projected $140.5 trillion in total global assets under management.

In 2021, our world is driven by data. As the push for ESG measurement and disclosure grows, this truism is extending into the space as never before. From radical innovations in tech to systems change and social impact, several key ideas are emerging to make sense of collecting, managing and reporting ESG data. Below are the top trends, as identified by speakers at GreenBiz Group's GreenFin 21. As the ESG ecosystem expands worldwide, demands for ESG data collection, analysis and disclosure are growing in number and depth.

Welcome to FINTECH Circle's AI Webinar Series focused on artificial intelligence and the future of AI in finance. As part of FINTECH Circle's book range we have released The AI Book, published by Wiley in June 2020. In this webinar FINTECH Circle's CEO Susanne Chishti will be joined by leading AI experts from Moody's to discuss how AI and innovative technologies empower ESG goals and which innovative finance tools are available to benefit small and large corporations. The webinar will focus on: - How can AI, Fintech and related technologies enhance ESG data, processes, frameworks and governance - Where are opportunities for ESG data and tools innovation alongside industry 4.0 - What's Moody's role in developing AI and Fintech innovation Our invited guests are: - Martina Macpherson, Senior Vice President, ESG & Engagement Strategy, Moody's - James Henley, Associate Managing Director, Moody's Investors Service - Ashit Talukder, Head of AI/ML, Moody's We look forward to having you join us for an inspirational conversation!

We have witnessed a permanent shift in the role that data and technology are playing in investment decision-making. Idea generation techniques that had mainly been seen as emerging or experimental are now increasingly being adopted as mainstream. However, one of the biggest challenges for asset managers is how to incorporate, assimilate and integrate many of these techniques into the daily investment processes of the various investment teams. Regardless of the approach taken, data and how it is integrated and analyzed is going to play an increasingly pivotal role across all investment strategies. I will touch upon some key themes in this blog, but will go into more detail in a series to follow.

With the growth in sustainable investing, there's been a surge in data on environmental, social and governance (ESG) factors over the past few years. Demand for ESG data is rising as asset managers look to incorporate ESG factors such as low-carbon emissions or gender diversity on boards into their investment analysis and decision-making processes. Fund managers, including BlackRock and Vanguard, are offering sustainable funds and exchange-traded funds (ETFs) based on sustainable indexes to capture assets from millennials and women. But the uptake has moved beyond specialty funds and has spread to pension funds, particularly in Europe, looking for long-term returns, reported Bloomberg Intelligence in April. "The financial cost of environmental, social and governance (ESG) performance and better disclosure is spurring uptake," wrote Bloomberg Intelligence in "Sustainable Investing Grows on Pensions, Millennials."