How AI and Analytics Help Firms Meet ESG Data Challenges

The deployment of artificial intelligence (AI) and sophisticated analytics will be critical for firms that want to meet increasingly onerous ESG regulatory obligations. This was one of the key messages to emerge from participants at a recent conference on the subject.

Among the top priorities discussed was the need to collect, analyse, and use data from diverse and often unstructured sources to support strategic decisions., which highlighted ongoing challenges in ESG data quality and comparability. As new laws and frameworks emerge, particularly in the EU, and as expectations from stakeholders, customers, and employees continue to rise, the pressure to act is intensifying.

The recent ESG Data & Tech Summit in London brought together top ESG specialists from the capital markets with data and technology solution providers to delve into how financial institutions can navigate this evolving ESG regulatory and market landscape. In the event’s keynote presentation, experts from SoftServe and the audience discussed the necessary data and technology strategies that organisations must adopt to tackle regulatory, data management, and reporting challenges on the path to net-zero.

Sustainable finance

Europe is at the forefront of integrating environmental, social, and governance (ESG) factors into corporate and financial sectors as part of the EU’s sustainable finance initiatives. In an already dynamic and multifaceted regulatory landscape within the financial services industry (FSI), discerning nuanced discrepancies has therefore become an even more intricate task. Participants emphasised the formidable challenge posed by the continually shifting regulatory environment, particularly access to and the integration of new technologies capable of delivering these results.

Key regulations, such as the Corporate Sustainability Reporting Directive (CSRD) and the Sustainable Finance Disclosure Regulation (SFDR), require around 50,000 companies to disclose their ESG impacts. This year, the EU is advancing its sustainable finance agenda with the implementation of the SFDR and the EU taxonomy to steer capital towards sustainable activities, further increasing the demand for reliable ESG data intelligence.

The Corporate Sustainability Due Diligence Directive (CSDDD) mandates environmental and human rights due diligence across global operations, with continuing application dates between 2027 and 2029. Furthermore, the European Commission is proposing regulations for ESG rating services to enhance transparency and reliability, aiming for finalisation in 2024 and application in 2025. These efforts will likely drive further adoption of ESG data science and AI-powered analytics to manage the volume and complexity of reporting.

ESG data readiness

Due to the extensive scope of these regulations and the limited timeframe for implementation, companies are encouraged to initiate preparations now. This entails evaluating their existing sustainability reporting processes and making essential adaptations to ensure compliance with the updated standards. These are some of the key challenges:

• Access to high-quality ESG data: Challenges arise in accessing accurate and consistent ESG data across operations, particularly when it comes to maintaining data quality and ensuring comparability across markets and sectors.
• Lack of standardisation: The absence of standardised and globalised ESG disclosure obligations hinders comparability across companies and sectors, highlighting the need for consistent taxonomies and disclosure standards.
• Embedding ESG into financial data: To consider ESG factors in investment decisions and risk management requires new methods to integrate ESG data with traditional financial analysis and risk models.
• No single source of truth: Financial organisations are having technical difficulties in collecting data from various data providers.
• Complexity of regulatory framework: The ever-changing mix of regulations and reporting frameworks for ESG creates difficulties for financial institutions.

Top ESG data and technology concerns identified at the summit

Most participants identified sourcing data as their biggest concern, particularly the assessment of non-standard and unstructured ESG data within organisations. This was followed by issues with data quality, timeliness, and integration. When asked about the biggest ESG technology challenge, the top response was combining and analysing structured and unstructured data, with the integration of ESG goals and performance into risk management close behind.

Implementing technology for ESG-driven approaches

Building ESG data intelligence is now central to developing resilient, insight-driven investment strategies. Emerging technologies and data analytics are transforming how financial institutions approach ESG, helping them optimise resources and make more informed decisions. AI plays a key role in this shift by standardising ESG data and analysing large volumes of structured and unstructured information using techniques like sentiment analysis and natural language processing (NLP).

This not only helps to identify trends and early warnings but also provides investors with a clearer understanding of companies' performance and potential risks. Banks and asset managers can use machine learning and NLP to make sense of unstructured ESG data, extracting actionable insights from regulatory filings and corporate documents to generate comprehensive ESG scores.

Customised solutions

To demonstrate how this could work in practise, SoftServe showcased its ESG data platform at the summit, unveiling innovative AI-driven solutions tailored for asset managers. The ESG ecosystem solution streamlines data gathering by integrating structured and unstructured data through MDPs, scrapers, and APIs. Leveraging AI, Gen AI, ML models, and ESG-focused data science, the platform translates ESG rating changes into actionable price and risk values for investments.

The platform utilises NLP models to extract and analyse ESG-related information, while IDP technology converts complex, unstructured documents into structured, usable ESG data. This proven solution supports and strengthens real-time decision-making, empowering asset managers with enhanced efficiency and insights.

It is an approach that can be easily customised to work with organisations’ existing technology infrastructure, by enhancing current capabilities with minimum disruption. It can integrate the deployment of powerful AI tools with the installed base, as our expert engineering teams work alongside internal resources to deliver optimum outcomes. Speak to us about an initial rapid IT assessment to discover how this could work for your organisation.