Environmental, Social and Governance (ESG) data collection and reporting has increasingly become a corporate priority over the last few years. Over 96% of S&P 500 companies published sustainability reports in 2021,
according to research from the Governance and Accountability Institute.
This need to examine and publish ESG data has been driven by several factors. Consumers are increasingly showing their preference for companies with positive ESG policies and reporting; employees believe environmental, social and governance metrics are important
factors in choosing an employer; and now government bodies and regulators are starting to weigh in.
Many government bodies and regulators either have, or are considering, mandatory ESG data collection and ESG data reporting requirements for corporations under their jurisdictions. In December, 2022, the European Banking Authority (EBA) published its
roadmap for sustainable finance. The roadmap – a collection of standards and rules aimed at better integrating ESG risk considerations into the banking sector – is set to come into effect in stages over the next three years.
In anticipation of this roadmap, many European banks had already started to build out or revamp their ESG data platforms to accommodate changes around green financing. A main focus for these organisations is how to flexibly incorporate the many new data
sources, types of data and formats that they will have to ingest and analyse under the EBA’s roadmap of changes.
What is ESG data?
ESG data comes from two primary sources – ‘inside-out’ and ‘outside-in’. Inside-out data is supplied by companies, used for analysis, and usually lags about 6-12 months due to annual ESG-related disclosures. Outside-in data however is more regularly updated,
sometimes in real time, and comes from the vast array of data that banks have access to from financial and company data from their customers. This data has more just-in-time impact and can prevent collateral damage through supplier relationships that were
not clearly identified as risks or simply the collation of multiple diverse data sources, e.g. satellite images of fields with water level information meshed up with commercial transportation data.
The volume and variety of ESG data makes collection and analysis difficult and comes down to the variety, velocity and volume of it within an organisation.
Unlike most financial datasets which are mostly numerical, ESG metrics can include both structured and unstructured datasets – as pointed out above not only emails or media reports. If a company wants to analyse satellite data to derive their own climate
dataset, they may even need to analyse videos. These are only a few examples of the variables in this type of data, so it is more important than ever to employ a data model that can support many different types of data.
The velocity of ESG data collection and analyses also increases exponentially as organisations embrace the idea of integrating this data in real-time. For example, loan due diligence used to depend on quarterly ESG data, but as customers demand faster load
approvals, financial institutions are increasingly going to have to rely on real-time data.
Given both the increase in variety and velocity of ESG data, it corresponds that there will be an increase in the sheer volume of data requiring storage and analysis.
On top of this, there are also no universally applicable ESG standards, leaving companies to deal with multiple different standards, with different data requirements depending on where they operate.
Getting to grips with ESG data in real time
An important part of utilising ESG data is that it accurately reflects what is happening at the time of use. For this reason, companies are increasingly incorporating real-time data into their ESG analysis, reporting and scoring. This requires harnessing
technologies such as cloud computing, AI, and machine learning to instantly parse breaking news stories for ESG-related data on their investments or incorporating up-to-the-minute satellite data into reports on a firm's environmental impact for example.
By using real-time data platforms, asset and fund managers can calculate accurate ESG scores to aid in investment decisions or risk calculations. Commercial operators can also ensure that their diligence covers their supply chain and in-direct production
facilities at third parties.
Lending, metrics and data going green
Looking at the roadmap coming from the EBA, one really interesting area of ESG data use concerns loans with environmental sustainability features, or green lending. These loans, sometimes called energy efficient or green mortgages, are typically given to
retail or SME clients to make energy efficient improvements to their properties, think solar panels or funding renewable energy work.
This will mean that banks have to re-work their scoring criteria for green loans to account for changes to risk adjusted performance indicators, impacting the acceptance performance of loans and mortgages, as well as taking into account already originated
loans that could be impacted.
This makes a significant impact on the loan origination process and data systems supporting the process. Banks should think about how they are going to manage evolving or unforeseen changes, capture different data attributes for the same product or loan,
incorporate new data types and formats, find insights from data explosion and meet the demands of customers and the competition.
To make sure that they stay one step ahead of regulatory changes, and we are likely to see many over the next few years, it is important to make sure that you have an infrastructure that can ingest many, varied types of data, consolidate data from siloed
data sets, enable easy searches of the data and simply create customised views of data so it can be used across the business.