Posted on : January 24th 2022
As financial institutions embrace new investment decision-making and interacting methods with corporations, buy-side investors adjust their decision models accordingly. The result is an increase in ESG (or “Environmental, Social and governance”) investments, research, and benchmarking to deal with the new corporate responsibility culture.
ESG derives its roots from Socially Responsible Investments (SRI) based primarily on ethical and moral criteria. However, unlike SRI, ESG has very real and calculable financial implications critical for a complete investment strategy. From its roots after the “Who Cares Wins” documents, all corporations have been striving to implement CSR (Corporate Social Responsibility) initiatives to push up their ESG benchmarking scores as it becomes an essential component of their investor relations and valuation.
However, ESG benchmarking has been primarily qualitative with little uniformity in tracking and rating of corporations. Additionally, the data sits in dense reports and news, making it hard to track and easily consume information from an analyst's perspective.
Moreover, benchmarking itself has little transparency. Most firms give scores rather than data, and the same corporation can receive diametrically opposite results from a different index or investment research firm. While the European Union (EU) has recently taken steps towards creating a common ESG taxonomy, it is yet to be adopted and tested for multiple industry use cases.
Meanwhile, amendments to the Markets in Financial Instruments (MiFID) II directive on ESG requires firms to consider their clients' ESG preference when investing, pushing the need for accurate and timely ESG benchmarking to be essential. However, without an efficient, structured ESG data pipeline, firms are left venerable under these new regulations.
Despite these challenges, ESG investing alone consists of approximately a quarter of all professionally managed assets or $30 trillion. According to a recent J.P.Morgan study titled 'Why COVID-19 Could Prove to Be a Major Turning Point for ESG Investing', the market shows no signs of letting up either.
With all the growth in ESG investing and its clear financial implications, firms are pressured to track ESG at scale. As a result, the market needs tools and partners that can bring down the average analyst reviews and structure data for a transparent metric-driven ESG benchmark. Advancements in Artificial Intelligence/Machine Learning (AI/ML) modules, Natural language processing (NLP), and Robotic Process Automation (RPA) accelerate creating data and metrics from sustainability reports for analyst consumptions.
Additionally, ML models can be trained to understand positive and negative ESG concepts. This level of structuring can bring scalability to benchmarking processes. More importantly, a focus is required on the Intent of companies, being able to measure their statistic and score the Impact of those statistics from an ESG product perspective.
There is often a disconnect between the availability of data and the ability to harness it for quick, actionable insights. In addition, conventional analytical methods cannot consistently identify problems and issues that might impact investment data operations.
The Straive Data Platform (SDP) has been designed to serve industry use cases spanning text, public data, and vision intelligence. Straive’s integrated data platform is customized to be an end-to-end ESG data engine, collecting data from news, annual reports, sustainability (CSR) reports, non-governmental organization (NGO) reports, and other unstructured sources. The data is then transformed, structured with ESG concepts, and extracted, bringing meaning and insights to aggregated documents/news.
For example, using the SDP, our clients can not only pull data points like carbon emission reduction, green building certifications, and social responsibility metrics but also derive interpretive data like policy stances, strategic goals, and domain-specific initiatives. All this can then be overlaid with scoring mechanisms, measuring the intensity of ESG concepts for easier consumption.
Straive can customize its technology platform to seamlessly integrate with any workflow, inside or on top of existing data ecosystems. An additional integrated workflow layer allows customers to work directly on the platform or as an unstructured data lake. We aim to accelerate the transformation to bring more transparency, accuracy, and uniformity in investment decision workflow.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
Our solutioning team is eager to know about your challenge and how we can help.