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Sustainability concerns are making noise in a post-COVID world, but the difficulties involved in extracting and analysing the non-financial information to facilitate informed decision making in ESG investing and sustainable finance present a challenge. Artificial intelligence algorithms offer a solution by improving the efficiency and effectiveness of the data extraction processes, but the challenges of ESG reporting implies the element of human judgement is not going away soon. This article takes a closer look at this intersection of artificial intelligence and sustainable finance.

With the discourse on sustainable finance and ESG investing accelerating following Covid-19, the discussion surrounding the challenges to extract and analyse non-financial material data has also picked pace. This is where artificial intelligence, a branch of computer technology that leverages data to automate complex tasks, holds tremendous potential to improve the efficiency and effectiveness of data mining and analysis. Artificial intelligence (AI) has seen significant impetus in recent years as part of the 4^th Industrial Revolution to leverage digital mechanisms for socio-economic development; and it is only opportune to utilize its merits to deepen sustainable finance and ESG investing.

But how does AI revolutionize the way financial companies deal with data?

First, AI can make data extraction more efficient. Reporting frameworks for sustainability or non-financial data vary from each other, both in content and in their applicability per jurisdictions. And since most disclosures are unaudited, the consistency of the reported data varies between companies, further complicating the extraction process and making intra-sectoral comparisons unreliable. Moreover, a lot of non-financial information is often found on third-party sources or unstructured data sources, which adds to the time taken to identify and compile the information. AI technologies can help scrape these multiple sources of information for keywords and data, thus extracting granular information at a far rapid pace as compared to human analysts. That would also aid the comparability and consistency of the extracted information. Once the algorithm’s process is set, the AI bot can repeat it multiple times at fast speeds, despite the sources having large volumes of irrelevant information.

A part of this pertains to small companies, be it small-caps in ESG investing or smaller-issuers in sustainable finance fundraising. Most large companies anyway publish sustainability reports as per the industry’s reporting frameworks, often to boost their branding as a corporate citizen. However, the small companies who often do not report material non-financial information present a challenge; and this is where the ability of AI to quickly extract relevant data and keywords from third-party sources becomes even more relevant. Why so? Because as the interest around ESG investing and sustainable finance deepens, the financial sector will have to expand beyond the large-caps and large issuers to the smaller companies, just like small-cap funds are launched in traditional investing once the investor demand pushes the valuations of the large-caps to the roof or their fundraising needs are met.

Next, AI can make data analysis more effective. It is capable of qualitative and quantitative analysis. Qualitative because it can correlate the financial data with the captured non-financial information to help the investing decision process, can make informed investment decisions based on this analysis. Quantitative because it can power quants-based funds and indices entirely; AI is anyway being used heavily by the hedge fund industry for quants-based strategies.

Last, AI can enable profitable investing decisions. A secret sauce of making profitable investments is timing! Using AI will not only help the financial sector screen companies for non-financial information faster with more accuracy than fatigued human analysts. This combination of speed and accuracy would lead to better timing in investments, thus translating into improved profitability!

Despite its merits, there is a need to exercise caution.

First of all, technology cannot be used in isolation. Given the inconsistencies between the data reported, disclosure methods and sources of information, we cannot just rely on technology. Some human intervention is still needed for judgement and analysis in order to understand the accuracy of the information. An element of engagement with the company reports is also needed to dig deeper and reaffirm the methodology behind the reported data. Perhaps as AI technologies evolves further, the human need will be eliminated. But we are still some time away from that.

A follow-up argument to this human vs. technology debate also connects with cost. The US and European financial sectors have seen significant mid-office (analyst-level) work offshored to emerging markets like India, Philippines and Sri Lanka which offer a cost advantage in terms of the analyst workforce. This needs to be compared with the probable cost-saving the sector can gain by using AI technology for data extraction instead of human analysts, however this will make sense only with extremely large universe. This arithmetic cannot be forgotten for sure.

Second, AI technologies that exist today for ESG data extraction are still unable to solve the issue of greenwashing, an eternal criticism the ESG space faces given the inconsistencies in reporting frameworks and the voluntary nature of the disclosures. Most non-financial reporting are unaudited, fuelling further the fire of greenwashing. AI technologies that can tackle this issue will be the real game-changers in this business.

Next, AI algorithms are strengthened as more and more historical data is inputted for that algorithm to learn from (called machine learning in technology parlance), which reduces its error-rate overtime. Since ESG and sustainable finance is still evolving in most geographies, there may be a dearth of historical data; hence the output of the algorithm would have an error-rate. That also necessitates human supervision. Also, simply depending on historical data may not suffice.

Also, many regulatory changes (ergo regulatory risks) on sustainability are still anticipated. That necessitates the fund managers to take a forward-looking view as well, not just use technology to scrape historical data.

Finally, there is still a talent gap at the intersection of technology, statistics, finance, sustainability, and social sciences, all being critical components to build a sustainable future. Even many Board members are still not fully conversant with ESG guidelines. That makes the human intervention tough as it is.

But despite these immediate headwinds in depending excessively on AI technology, the long-term advantages offered by AI offer a clear rationale to weave it within our sustainable finance and ESG investing initiatives. As the abilities of automation and algorithms improve, the day may not be far when AI even becomes capable to not only identify and extract the relevant information, but also audit the material information and deliver critical insights without any human intervention. And with the post-COVID economic slowdown reorienting the focus of companies and governments towards lean-cost solutions, the long-term cost advantages offered by AI might also offer an economic rationale to do so.

Image Source: https://unsplash.com/photos/0NRkVddA2fw, photo by Guillaume Périgois.

While the EU Taxonomy augurs well for a green future, concerns around the availability and measurement of the data needed for disclosures coupled with the confusion to issuers and investors as it is yet another framework in a marketplace already crowded with disclosure frameworks must be assuaged. That will hasten better alignment with the Taxonomy and help create a greener Europe in the long-term

The recent update surrounding the EU Taxonomy should augur well for the progress towards a green future. But for issuers and investors who must traverse that journey, it also raises certain concerns around the companies’ sustainability reporting practices and the ESG investments that uses sustainability disclosures as the basis for their investing decisions.

Let us take a step back to see quickly what the EU Taxonomy strives for. It enables companies and institutional investors operating in the EU or the UK to classify projects to be able to define the environmental performance of those economic activities. As per the Taxonomy, each project needs to be classified into one of the six broad economic activities namely climate change mitigation, adaptation, pollution, circular economy, water, and biodiversity, while ensuring no major damage to the other five. The projects must also comply with the basic safeguards and EU’s technical screening criterion. The Taxonomy obliges issuers to disclose their turnover/costs and investors to disclose the extent of their investments that are in line with this classification. The broad motive is to align projects better towards a low carbon future and enable better decision-making for economic activities.

How does this impact sustainability reporting and ESG investing?

While some details are awaited before the taxonomy becomes mandatory from next year, it might be opportune to see how sustainability reporting practices and ESG-oriented investing might get impacted.

The first and foremost challenge for issuers is about the reporting frameworks. An issue with sustainability reporting globally are the varied standards and frameworks, and the ensuing confusion on what to adopt whilst balancing transparency and compliance costs. The EU taxonomy may add to that. One difference worth mentioning here is the taxonomy is a mandatory disclosure while most other frameworks are not. Mandatory compliance might position the taxonomy as a more generally accepted non-financial reporting standard, with the others layering over it. For instance, EU companies that want to report further on climate risks may add TCFD or CDP disclosures to their EU taxonomy reporting on green projects. Voluntary nature of other frameworks often results in inconsistencies in the disclosures across companies, making sectoral comparisons tough for financiers. This may be partly rectified if the taxonomy’s methodology is followed consistently across. Of course, a lot depends on item-wise comparison of the reporting requirements between the taxonomy and other frameworks, to understand how much of it overlaps.

The other is about data. The taxonomy encourages more disclosure, reducing greenwashing by the issuers and investors. But more disclosure implies more data. The challenge is whether granular data required to assess a project are available and accurate; and if not, the cost to prepare such taxonomy relevant data could be a daunting exercise. As it is, revenue/cost segmentation by business-lines is hardly standardised across companies, and segmentation on green and non-green basis may be unavailable in most cases. Mid to small sized firms may find limited resource and ability to compute it. Moreover, the taxonomy involves metrics very few companies might be reporting currently, say biodiversity loss or noise pollution, which are tough for companies and investors to compute. Such companies may need the help of ESG research and consulting firms, who have the expertise to collect or estimate a firms’ performance on focused metrics and can assist in filling the gaps in disclosure, which will naturally come at a price. In any case, disclosure per taxonomy-criteria will be necessary for companies so that they do not end up being excluded from the investment universe for the lack of proper reporting.

ESG-oriented funds must disclose their policies on how they integrate sustainability risks into their investing decisions and how they look at the sustainability impacts. This transparency might help further filtering of greenwashing tactics. However, ESG rating models are not standardised and each fund or rating agency prefers its proprietary models, making ratings incomparable for the same company. Thus, the taxonomy may force them to adapt their ESG-data questionnaires and processes in a way that brings more consistency in the way the data is used and modelled, improving comparability. Again, matching the taxonomy’s criteria to the model’s variables might require specialized consulting assistance.

While ESG-oriented investing assets have grown steadily to over $40 trillion, mostly centred in the developed markets, surveys show millennials are evincing interest in sustainable and values-based investing, implying adherence to frameworks like the EU taxonomy might bode well for companies and investors who are mindful of the growth drivers.

While the taxonomy adds compliances and a hunt for granular data, it clarifies eligible activities so that capital flows can be reoriented to hasten the long-term vision of a green Europe. Greenwashing and inconsistent disclosures were making this road longer. Private capital is only going to come into more demand to meet the SDGs, since public spending, aid and philanthropy remains woefully short. Strengthening the sustainability yardsticks for capital, which the taxonomy aspires for, may be an apt thing in such circumstances. If implemented effectively, the taxonomy might supersede or streamline other disclosures, easing compliance in the long-term. With the Commission expected to publish a report by December 2021 on the provisions that would be required to extend the scope of the Taxonomy to social objectives, the direction in which EU policymakers are thinking is clear. Those adapting their data processes and ESG questionnaires quickly, be it with external assistance or internally, might enjoy the first-mover advantage in Europe’s rapidly evolving responsible investing landscape!