In recent years, Environmental, Social and Governance (ESG) has become more than a buzzword, it has become a central topic across industries and integral to demonstrating climate and risk-minded values to key stakeholders. Since the pandemic, ESG investment has soared, rising from $5 billion in 2018 to more than $50 billion in 2020 and to nearly $70 billion in 2021.

As a framework designed to be integrated into an organisation’s strategy to create enterprise value, ESG has become an increasingly important element in the investment analysis process.

With an increased global focus on ESG issues, these factors have become a way to evaluate companies on non-financial matters, particularly for investors to gauge risk management and growth opportunities. Beyond an organisation’s carbon footprint, the ESG standards provide an overall understanding of a business.

Why do organisations care about ESG?

As more organisations and their investors recognise the value of incorporating ESG as a central component of overall business strategy, executives should prioritise meeting stakeholders’ increasing expectations, managing sustainability-related risks, and capturing business opportunities.

Even through inflation and supply chain disruption brought forth by the pandemic, many corporate studies have demonstrated a positive relationship between ESG and financial performance as investors have felt an urgency to monitor their risk management strategies.

Disclosing ESG actions increases stakeholder engagement, loyalty and competitive advantage, ultimately delivering long-term value beyond financial measures.

Research from analyst firm Gartner suggests 85% of investors considered ESG factors in their investments in 2020, demonstrating the level of importance financial stakeholders have begun placing on ESG. According to the research, the key reason for investors placing ESG high on the priority list is to reduce investment risks. They do this by considering an array of factors, including regulator intervention, competitive positioning, supply chain reliability, corporate reputation, consumer preferences and business ethics, among others.

However, there remain many challenges when it comes to quantifiably measuring ESG, as well as business impacts on society and its stakeholders.

For instance, ESG metrics are not standardised globally, making it difficult to make objective assessments. No ESG data repository or management process can filter and analyse company portfolios and extract insightful information. This poses data quality, traceability and accessibility challenges for companies. As companies disclose more and more ESG data, ESG scores still need to be approached with a certain degree of scepticism.

The solution

This is where Artificial Intelligence (AI) comes in. AI could help overcome challenges brought on by data processing and the incorrect disclosure of information by automating data collection and indicators to compute key factors automatically, using Computer Vision (CV) and Natural Language Processing (NLP).

AI data analytics could then add a degree of accountability to company data, helping investors identify risks and opportunities and increasing transparency. By leveraging algorithms and reducing human bias, AI and data analytics has the ability to produce more reliable and accurate ESG performance reports.

We are still in the early stages of applying data analytics to ESG data, with more work still to be done in order to make it a viable solution, in turn increasing confidence levels in the ESG rating system. In future, the automation of ESG reporting and scoring will provide a method of real-time analysis to understand a company’s social impact, clearly identifying success factors, risks and opportunities.

What does applied AI look like?

• Automate data collection – With Computer Vision (CV) and Natural Language Processing (NLP), you can transform unstructured and messy data into valuable data, classify each under a theme and further create the key ESG indicators automatically.
• Data Analytics will start adding a degree of accountability, quality and transparency – Using alternative data that may exist outside of a company’s formal ESG, using NLP advanced extraction capabilities can assist organisations with identifying risks and opportunities for ESG investments. Using NLP & Machine Learning to transform textual data into numerical data and Sentiment Analysis techniques to extract the tonality of articles will produce valuable potential predictive indicators for assessing long-term risks.
• Forecasting, optimisation and IoT capabilities – Utilising IoT sensors connected and real-time data streaming capabilities and alerts, the energy consumption of an organisation’s assets can be monitored and optimised.
• Intelligent monitoring and stress testing – To ensure the impact of your project is embedded into your business, AI could be used to create an internal ESG Rating model. To drive the decision-making process, an end-to-end approach can be built to assist an AI-driven scoring system that has the potential to address all the important aspects of ESG: Metrics and Targets, Strategy, Risk Management and Governance. Automating your ESG reporting in real-time analysis could help you understand the factors that determine your ESG score without having to wait for the next report.

The focus on ESG is only increasing, and AI and data analytics for both companies and investors is an integral part of their sustainability journey.