With rising ESG investments, investor demand for AI-ML-based ESG data analytics solutions is increasing.
The World Economic Forum in Davos, which concluded recently, revolved around the core theme of sustainability. We are in a phase where the regulators worldwide rule for nonfinancial or sustainability reporting in coordination with financial reporting or a grade of integrated reporting.
Organizations worldwide use leading standards such as Corporate Sustainability Reporting Directive (CSRD), Global Reporting Initiative (GRI), Sustainability Accounting Standards Board (SASB), International Integrated Reporting Council (IIRC) and a few more for sustainability reporting.
Unlike financial reporting, which has predefined parameters in balance sheet and income statements such as assets, liabilities, operating income, net profit or EBITDA, there is no uniform reporting mechanism in environmental, social and governance (ESG) reporting. The reporters play with the language; as a result, the ESG data from companies worldwide are not uniform and are jumbled up.
Today, investors and analysts must go through many company documents to source ESG information and interpret them, which is cumbersome and may not result in sound analysis. They ideally prefer to access ESG information instantly, similar to financial data in simple forms such as pie charts or bar diagrams on a Bloomberg terminal.
Every ESG-focused investor, therefore, is often worried if they are putting their money in the right firm with a proper sustainability or governance matrix.
Stock exchanges worldwide have rich data on annual nonfinancial reports of listed companies. The subscribers of the stock exchanges would be interested in an instant analysis of the unstructured ESG data in these reports, enabling them to make informed decisions.
Similarly, banks and financial institutions (FIs) lend to companies based on their sustainability goals. They devise loan strategies through clear targets and timelines mandated by their clients in their decarbonisation goals. Many financial institutions are pulling out their investments from highly polluting sectors and insisting that their clients migrate to green energy. These financial institutions are looking at solutions that will quickly segregate the borrowers or prospects with the right sustainable strategies from those lacking.
There is a significant demand from banks, FIs and investors worldwide for the understanding or availability of intelligently gathered data in the ESG/sustainability or the integrated reporting space.
The good news is that today, high-end AI-based document analytics solutions have come to the global market that derives information from structured and unstructured data and metadata. The data could be from printed documents, handwritten notes, PDF documents, contracts, websites, word documents, pictures, scanned images, IoT sensor data and more.
The SaaS-based solutions can be available on a subscription base, targeting a more comprehensive audience range. They have high-impact engines trained in data mining in the ESG space, with a rapidly learning AI that keeps on learning and upgrading itself, crunching complex data in the ESG space.
The analytics can capture critical and essential information from the documents related to a company’s ESG contribution. This data could be everything from the consumption of water, land, raw material consumption and power consumption—it could be the company’s pollution or its biodiversity or employee data. It could be data on the board compositions, board meetings and management discussions.
For banks, FIs, stock exchanges, research organizations and investment communities, such as PE/VC funds, brokerage houses, family offices or retail investors, intelligent information can be extracted and analyzed from raw ESG data, which will help in making the right investment decision or improving strategy. It could enable a thoughtful interpretation of their investments’ performance and maybe even predict which would be the top performers or the laggards.
As per reports, the global intelligent document processing market, including AI analytics, was valued at $1.1 billion in 2022 and is estimated to reach $5.2 billion by 2027, at a CAGR of 37.5%.
To find the best results, here are five key attributes to look for in an AI-based ESG data analytics solution:
1. AI-ML solutions should be simple and SaaS-based. The advantages SaaS-based solutions possess include—they should be scalable, cost-effective, auto-updated and secure, with data accessibility from anywhere. Non-SaaS-based solutions may require in-house systems, have increased maintenance costs and limited data accessibility and scalability.
2. Extracting information from any document should be flexible based on standards and nonstandards. Organizations’ sustainability and governance reports often run in multiple formats, including text, tables, graphics, infographics, etc. AI should be intelligent enough to understand and extract data from it. While AI is not 100% foolproof all the time, it can learn, unlearn and become more accurate on the move.
3. It should have search engines trained in rapid data mining, machine learning and upgradation.
4. The solution provider should be reliable and have a proven track record of handling data sets of all types with deep domain expertise.
5. The solution has to be budget-friendly to suit the needs of a large section of target groups.
Sustainability is the need of the hour to save planet Earth. Nations, companies and regulators are announcing policies on sustainability and governance, and investors and banks are investing in growing companies with strong ESG roots. ESG data analytics used to be an issue. However, AI technology enables complex ESG data to be available in understandable, readable formats. This will benefit all stakeholders, including investors and banks, in making crucial ESG-related investment decisions.
