By Rebecca Sparrow, Head of Sustainable Finance at Delta Capita
Financial services firms are looking to move away from generic environmental, social and governance (ESG) goals and mission statements toward clear strategic plans that embed into their governance and risk management frameworks. But they are hampered by inconsistent data, poor data coverage and rigid ESG methods that fail to represent true business activity.
Artificial intelligence can come to their aid by providing more tailored and dynamic ESG analysis. AI engines allow for improved ESG data coverage by aggregating, indexing and unifying semantics. This allows companies to surface more relevant ESG data and tailor searches to their business concerns and or clients’ strategies. The result is a more proactive ESG strategy, enabling companies to stay ahead of negative press, and benchmark against peers.
Challenges with embedding ESG
To embed ESG into governance and risk frameworks, companies need to get a clearer understanding of their ESG exposures through clearly executable and reportable metrics and targets. Most financial firms are playing catch up in this area.
Requirements are growing for financial firms to conduct in-depth sustainability due diligence across their investments, clients, underwriting processes, and supply chains. Without an accurate understanding of their customers’ sustainability profiles, they may inadvertently pick up a “dirty” investment or deal.
This reputational risk leads some to look for a quick fix, but the challenge is a lack of standardisation of data across providers, and insufficient data coverage when choosing one provider.
As yet, there are no commonly accepted aggregators of ESG data, nor a standard method for aggregation. In the EU, the Environmental Taxonomy is live and the Social Taxonomy is being defined. But a commonly agreed model to assess a company’s adherence to sustainable development legislation is still not standardised.
Bloomberg recently reported that more than 600 standards, frameworks, data providers, ratings, and rankings are used to measure ESG-related risks.
This fragmentation results in firms having to conduct a broad and manual search for multiple ESG factors across all their relationships. This means juggling multiple sources, aggregating data manually, and conducting extra research where ESG scores appear superficial.
Bridging the rift between scores and reality
The Bloomberg article also showed how the Ukraine invasion has shone a harsh spotlight on these pain points. Although ESG scoring providers have cut Russia’s rating since the Ukraine invasion, the country retained clearance for ESG investing. Companies that had invested heavily in Russia also kept their high ESG scores. This highlighted a major discrepancy between ESG ratings and live activities.
Determining environmental, social and governance factors is complicated and subject to change. Existing market solutions are slow to consider live information, and assume a one size fits all approach. The result is that firms are susceptible to mislabelling investments and reputational damage.
Furthermore, capturing all the uses for ESG in one score is unhelpful. Firms still need to conduct bespoke, skilled due diligence and apply custom ESG criteria. And it needs to be cost-effective.
Given these challenges, financial firms need to consider outsourcing their ESG research, to avoid overstretching their risk management and decision-making functions.
How Delta Capita can help
Delta Capita’s ESG thought leadership, exceptional due diligence capabilities, and leading technology solutions combine to offer a relevant and effective solution that is ahead of the rest of the ESG sector.
Our AI assisted ESG solution provides ongoing monitoring of the tailored ESG points financial firms need to keep up with the changing environment. It also enables repeatable analysis against sustainability goals.
Delta Capita’s solution condenses weeks of research into automated searches that your teams can surface on demand. You can use it to research 400 billion web sources and interpret the sentiment of any ESG-related commentary. It’s cost-effective too, as automation saves 70% of time spent on manual data analysis.
Our solution improves your data coverage by aggregating, indexing and unifying semantics, all in a single platform. This moves you ahead of the curve, and enables you to spot early indicators of up-and-coming ESG trends or issues.