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Greenomy: At the Forefront of Utilising NLP to Automate Compliance
The ambitious climate targets that have been set across the globe, including the European Green Deal and the Paris Agreement, are concrete illustrations of both global and regional aspirations towards combatting climate change. A core part of this transition is the greening of the global economy, which demands an influx of capital to be funnelled into sustainable economic activities at an unprecedented momentum.
In order to facilitate this redirection of finance, regulators are drafting legislation to set out the conditions an economic activity must meet in order to be considered sustainable. The most far-reaching piece of legislation pursuing this objective is the EU Taxonomy, a sustainable finance regulation which offers a structure to direct capital towards more sustainable projects. Other relevant pieces of legislation include the upcoming CSRD and the SFDR.
Nonetheless, organising the data required by these regulations is arduous and complex, since it is scattered and has not been collected, organised, coordinated, nor tied back directly to the regulations. This thereby leads to a delay in the implementation and effectiveness of the regulation. Hence, a major aspect of complying with these regulations is to identify and collate the relevant pieces of information from large sets of unstructured data. This is where AI can step in. Artificial Intelligence, and Natural Language Processing (NLP) in particular, can accelerate this process for affected financial market participants.
Natural Language Processing (NLP)
Natural Language Processing is the area of Artificial Intelligence that deals with the interpretation of human language - that is the speech data or text data that is either spoken or written in natural language.
Those that typically work with NLP are computer scientists, linguists, and data scientists, who construct algorithms and specific programs which comprehend human language and then carry out various functions with it, e.g., summarise it, respond to it, or re-elaborate existing text.
NLP is particularly useful at generating insights from unstructured data sources, therefore making it an excellent tool to generate insights from sustainable finance data which is mostly found in unstructured forms.
How NLP adds automation to the Greenomy portal
NLP contributes to the Greenomy solution in various ways, namely: to navigate legal texts easily, automate compliance, detect EU Taxonomy-eligible activities and follow regulatory trends.
- Navigate legal texts easily
Sustainable finance regulations are notorious for overlapping with each other and frequently referring to further legislation, e.g., the EU Taxonomy frequently refers to other EU regulations and directives, as well as legal clauses in domestic legislation. This network of relevant legislation can be difficult for financial market participants (FMPs) to navigate. Entity Detection and relationship extraction are NLP to identify these further legal texts from the source text and build a knowledge graph to navigate regulations with ease, with automatic updates as regulations evolve. Furthermore, as new deadlines are released and their approaching time draws nearer, the solution is consistently updated with these to ensure that FMPs are compliant with regulations and meet deadlines.
- Automate your compliance
A core component of the EU Taxonomy is the set of technical screening criteria that assess if an economic activity can be considered sustainable. Going through these criteria manually is incredibly time-consuming, with some customers reporting that it took them several weeks to do so manually. However, NLP is able to rapidly process millions of unstructured documents, such as annual reports, sustainability reports, life cycle assessments, energy performance certificates, car documents to locate relevant data, match them to these specific technical screening criteria and assess EU Taxonomy compliance.
- Detect your EU Taxonomy-eligible activities
The EU Taxonomy is essentially an encyclopaedia that provides a thorough explanation of different economic activities that are considered environmentally sustainable, yet understanding which, if any, apply to financial market participants can be confusing and time-consuming. NLP models can be utilised to scan through volumes of documents that are drafted in human language and automatically identify evidence of these activities, which both reduces processing time and boosts accuracy in comparison to a manual approach.
- Follow regulatory trends
Finally, sustainable finance is infamous for its constantly changing nature since it is so broad and ongoing research leads to new findings to be incorporated into legislation. Two core examples of this include the change of the NFRD to the CSRD in January 2023, which will entail an increased scope of the legislation and more comprehensive sustainability rules, and the expansion of the scope of the EU Taxonomy to include the new ‘taxo4 objectives’ later this year, i.e., pollution, water, circular economy, and biodiversity. NLP techniques, such as topic modelling help to track the emergence of new sustainability topics from information resources, such as articles and news outlets which empower FMPs to remain one step ahead in sustainable finance knowledge.
NLP is a breakthrough branch of AI that is facilitating the smooth and efficient implementation of sustainable finance regulation around the globe. NLP’s value lies in its ability to rapidly absorb and summarise the maze of sustainable finance legal documents, thereby empowering practitioners to navigate the landscape much faster. This thereby accelerates the transition to a greener economy by speeding up the pace at which climate goals can be achieved.
The new ESG disclosure requirements can be challenging and keeping up with the EU Sustainable Finance regulations is a hurdle for financial market participants. To tackle this, at Greenomy we use NLP to digitalise these requirements into different portals so that we can support you, as an asset manager, investment manager, company, or financial institution, along your ESG data journey.
If you would like to discuss how to tackle your own reporting requirements, you can book a free consultation here.