Digital infrastructure is hugely impacting sustainability. The internet accounts for about 3.7% of total global greenhouse gas emissions. But it is also the driving engine of sustainability if positioned at the center of the strategy.
A form of sustainable investing, environmental, social, and governance (ESG) investing, is considered for both financial returns as well as for the overall impact of the investment. With the growing focus on ESG, organizations are building more ethical portfolios to drive investment and meet shifting consumer demands.
However, with ESG funds outperforming other funds, investors, wealth advisors, and asset allocators are facing major challenges with sustainable investing-
- Lack of standardization of ESG policies by companies
- Inability to effectively monitor and disclose ESG data
- Lack of automation in collecting the data, leading to unstructured ESG databanks
Employing an automated AI-driven approach to this sustainable investing will provide organizations with closures for all three of these gaps, thereby ensuring smarter investment decisions.
Challenges To Overcome
ESG data is more qualitative than quantitative. A disproportionate focus on ESG-aligned policies and strategies skews data away from what’s most useful. For asset managers, this makes judging the alignment of reporting ESG entities and investors' priorities an excessively subjective task. The inaccuracy of conventional ESG data, along with a lack of intra- organizational data sets and instructed recording systems, is adding to the burden. Organizational investment managers are under a lot of pressure to perform. By employing a data-driven approach to ESG factors, organizations can more easily access and benchmark their operations, define their future goals, and reinvent themselves.
ESG data analysis still remains a challenge for investors. Due to the staunch sustainable protocol, organizations are facing the need to standardize and disclose their ESG data reports and scores. Further, large enterprises are self-reporting their supposed green practices, increasing the risk of investors being exposed to greenwashing. However, these generalized ESG scores do not provide clarity to investors, as there arise chances of large discrepancies between how the ESG ratings are perceived by different companies.
A lack of standardized data makes assessing companies’ ESG policies challenging. Analysts are now turning to AI to uncover the information required. Advances in AI are enabling organizations well as investors to encompass a variety of complex tasks at incredible speeds and volumes. It is revolutionizing how enterprises work with data.
How can AI Transform the Process?
To analyze unstructured data and make critical decisions, organizations need to embrace tactical and calculating techniques. A significant part of AI (artificial intelligence) in ESG comes from sentiment analysis algorithms. These calculations enable computers to examine the tone of the data and employ AI and Natural Language Processing (NLP) techniques, to analyze heaps of data seamlessly.
Filtering manually gathered information can be error-prone, time- consuming, as well as costly. With artificial intelligence solutions, investment researchers can smoothly identify ESG risk by digging vast amounts of qualitative and unstructured data through algorithms that can identify, extract, and measure ESG information.
AI algorithms that support real-time ESG analysis can also assist in identifying early warnings and provide timely indications of accurately recognized trends in organizational activities. This provides clear pictures to the investors to evaluate the companies' performance and future risk.
By developing AI models with a standardized framework, businesses can present the findings in custom reports and visualizations to the investors.
These ESG ratings and reports can then be used for risk management, portfolio construction, thought leadership, and benchmarking products to attract sustainable investments.
Revisiting the ESG Data Problem
Until 2015, ESG data was generated and aggregated subjectively by research analysts. This led to a plethora of issues like potential analyst bias, making it difficult to frequently update the ESG scores of the organization.
This led to little correlation among ESG data providers.
ESG investing is continuing to face similar obstacles, leading to data remaining noisy and incomplete. To fully standardize, integrate, and make data transparent, organizations need to employ subjective and qualitative aspects of digitization. Considering the rising number of ESG data sources, they not only add value to security selection but increase the cumulative value of the sources being used. To integrate ESG information into alternative databases, it is vital to take into consideration the underlying elements and employ measures to calculate the similar factors provided by the data.
One underlying reason for this discrepancy is the unstructured form of ESG data. ESG data has less structure as some fields are purely based on the data mining of unstructured records. This increases the subjectivity required by data researchers to transform the same ESG factor into a quantifiable metric. There are varying amounts of noise introduced into the factors due to the parameters used for mapping text or records to a quantified metric. With the evolution of data collection and mapping methodologies, the amount of noise in ESG factors is expected to recede.
Implementation of Artificial Intelligence
Machine learning is being employed to address the challenges
in ESG data, particularly the integration of the data into more stable and comprehensive databases. Advances in natural language processing (NLP), deep learning, and machine learning offer strategies that make it possible to incorporate similar fields from different datasets and reduce noise while retaining all the information.
The plethora of unstructured data accumulated by public companies over long periods can now be treated to make it possible to mine enterprise insights, regulatory filings, and integral undertakings to compose a score for every ESG factor.
NLP algorithms contain the ability to read, categorize, and extract positive and negative sentiments to build an array of potential predictive indicators. Investors can employ these algorithms to dig into a broad category of underlying data to see how the organization is exposed to specific ESG factors.
Organizations are now integrating alternative datasets and employing AI- driven operations such as machine learning and NLP (Natural Language Processing) to generate more flexible and up-to-date information. Big names are integrating NLP to analyze different sources of alternative data to derive ESG ratings on more than 20,000 enterprises worldwide. These data driven ESG signals employ self-learning quantitative models to filter data from unbiased sources with more frequency and granularity for real- time analysis. NLP can also be employed to identify and extract entity graphs to auto-extract data with specific elements from the ESG database.
Data augmentation holds the potential to increase the diversity of data in training models without accumulating new data. It can assist in filling the gaps in data, thereby taking another step toward standardization of the ESG data metrics.
Key Highlights
- Integrating real-time data is key to fulfilling ESG commitments.
- With AI (Artificial intelligence), investors can collect and analyze data to identify and account for environmental, social, and governance risks and opportunities.
- AI can enable sustainable investors to filter through mountains of data that bear essential data for ESG investing.
- Corporations can build strong ESG programs underpinned by data.
Conclusion
Integrating and maintaining ESG practices is crucial for the long-term survival of businesses. With a growing number of investors investing positively, investment managers are feeling the stress of quantifying ESG models in their portfolios. The absence of data is making it challenging for them to evaluate the associated long-term sustainability risk.
Artificial Intelligence is the problem solver for this ESG data problem. With AI applications, businesses can search and process large volumes of data, capture ESG-relevant information, and offer findings in a visual format.
AI-driven ESG models will offer investors a standardized framework to evaluate the company's ESG impact and predict trends and future risks. It will assist the investors in getting quality ESG data quicker to make socially responsive and environment-friendly investments.
By enforcing the AI framework, businesses can operate with reliable data. AI will facilitate the required transparency and integration in ESD data to become more profound. AI can become a critical factor in enabling investors and risk managers to analyze ESG data, collected in both structured and unstructured formats, and extract relevant information.
This blog was originally published on SG Analytics blog page
