The world is witnessing a seismic shift in the investment sphere, where environmental, social, and governance (ESG) considerations are no longer just buzzwords. And thus, investments in ESG assets and funds are witnessing significant growth every year.

ESG integration is becoming a defining feature of business models worldwide, but evaluating a company’s sustainability performance is still a significant challenge for investors.

ESG as the New Norm: Industry-Specific Strategies

Several companies across industries are gradually integrating ESG practices into their business models. But it’s worth noting that this integration hugely differs depending on many factors like the industry, region, size etc.

For instance, it is sensible, when multinational consumer goods companies set ambitious sustainability goals to reduce their environmental footprint by reducing waste and improving resource efficiency or healthcare companies prioritize medical waste management and patient data security. In the same context technology and logistics companies can focus on promoting sustainable transportation to decrease the world’s dependence on fossil fuels. BFSI companies can prioritize responsible investing, while telecom companies can stress reducing carbon emissions.

This trend of ESG integration, adopting sustainable practices, is on a rise and these companies are becoming widely recognized by various sustainability benchmarks, leading to their increased demand among investors and in turn, their stock prices. But evaluating their ESG performance and investing in these, is a complex story altogether. AI and other advanced technologies can aid investors in navigating this tricky terrain.

The Inconsistency Challenge

Globally, various ESG ratings and disclosures provide valuable information to investors to make informed decisions, but inconsistencies and fragmentation across regions impede comparisons of companies and industries. The differences in reporting requirements and data collection sources, such as annual reports, CSR reports, company websites, and regulatory filings, contribute to disparities in ESG metrics. Also, with numerous ESG indexes being followed across the world, the lack of standardization and transparency in their methodology, further complicates the assessment of ESG data. Investors need to adopt a nuanced approach to interpret and compare ESG information effectively.

Bridging the gap: AI & other technologies for accurate ESG analysis

The use of advanced technologies such as blockchain, Artificial Intelligence (AI), Internet of Things (IoT), and Natural Language Processing (NLP) are paving the way for optimal ESG investment decision-making. Among these, AI has the potential to be a game-changer, enabling better ESG data analysis, bridging regional disclosure gaps, and a lot more. It is helping sift through critical data and assess long-term risks and benefits for sustainable investment, complementing the ESG approach.

AI and machine algorithms can accurately measure ESG factors and even reveal public sentiment towards a particular company, product, or issue by performing sentiment analysis on vast unstructured text from news and social media. Investors can utilize this information for making better investment decisions. AI can also estimate indirect emissions by analysing data from satellite imagery and other sources and presenting an accurate picture of a company’s environmental impact, and its carbon footprint. NLP techniques can help forecast future ESG performance. This space is evolving for better times!

Despite their potential benefits, these technologies have significant limitations such as dependency on a company’s self-reported information and historical data for future predictions. Inaccuracies and biases may arise due to incomplete or biased data and programmed algorithms. Technology may also not be able to accurately interpret unstructured qualitative data analysis, such as the evaluation of a company’s governance practices requiring a deep understanding of the company’s ESG practices.

So, what is the way out? AI and technology offer promising solutions to overcome these challenges, and there are actionable steps that investors and companies can take to implement these solutions. By leveraging these technologies, companies can demonstrate their commitment to ESG principles and provide investors with reliable information. Here are a few solutions to combat the inconsistency challenge better:

  1. Define a common ESG framework and metrics to address inconsistent ESG reporting.
  2. Integrate AI and other advanced technologies to automate the collection and analysis of ESG data, reducing human error and bias.
  3. Follow globally accepted robust data governance policies to ensure the accuracy of data used by these technologies.
  4. Mandate companies to make their ESG data publicly available and partner with third-party ESG data providers for independent validation.

Staying ahead of the curve

An accurate ESG evaluation also requires advanced technologies and human expertise, together, to assist in detailed analysis. Companies across various industries are already implementing ESG practices and utilizing innovative reporting and analytics solutions. From integrating ML and natural language processing (NLP) to improve ESG analysis to developing an AI-driven model that uses NLP and sentiment analysis, organizations are showcasing the diverse applications of AI and ML in ESG investing, and how these technologies can provide more accurate and transparent ESG analysis for investors.

A meaningful collaboration among companies, investors, and regulators is going to be vital for aligning ESG investing with sustainable objectives. Significant investments in innovative ESG reporting and analytics solutions will also be needed to stay ahead in the battle.

About the Author

Soman Dubey is Chief Technology Officer, Fintech, APAC of Accolite Digital.

This article was originally published in CXOtoday.

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