Machine learning practices into ESG strategy

By Mohammad Reza Mousavi

Mohammad Reza Mousavi is an Advisory Intern at PKF Malta, an audit and consultancy firm, and has over 25 years’ experience in accounting, taxation, financial and consultancy services

Machine learning, a subset of AI, enables computers to learn from data and predict outcomes without explicit programming. It encompasses supervised (labelled data for prediction), unsupervised (finding patterns in unlabelled data), and reinforcement (learning through actions and rewards) learning.

It powers diverse fields like language processing, computer vision, and autonomous driving, continually advancing with more complex algorithms and larger datasets. As businesses and industries embrace its potential, machine learning drives innovation, from personalized recommendations to medical diagnoses, revolutionizing how we interact with technology and data.

On the other hand, ESG stands for Environmental, Social, and Governance. It's a framework used to assess the sustainability and ethical practices of a company or organization. ESG criteria are used by investors, stakeholders, and analysts to evaluate how well a company manages its impact on the environment, its relationships with employees, communities, and other stakeholders, and the quality of its governance and leadership.

Environmental factors include things like a company's carbon emissions, water usage, and waste management. Social factors encompass issues like diversity, employee treatment, and community engagement. Governance involves assessing the company's leadership, ethics, transparency, and overall management practices.

The integration of machine learning (ML) with ESG strategies holds the promise of reshaping corporate sustainability practices. ML, with its ability to process and analyze vast amounts of data, has indeed revolutionized business operations, providing insights that were previously unattainable. Concurrently, ESG considerations have become integral to corporate strategies, reflecting the growing acknowledgment of businesses' societal and environmental responsibilities. This essay delves into the intersection of ML and ESG strategies, exploring the potential benefits and strategies for amalgamation.

ESG data, crucial for measuring a company's sustainable performance, often suffers from data collection inefficiencies and inaccuracies, as mentioned in the provided text. Traditional methods like surveys are labor and time- intensive and subject to human errors.  ML addresses these issues by efficiently collating and processing diverse datasets from sources such as social media, news articles, and financial reports.

ML's impact is particularly pronounced in the realm of investment decisions, where ESG factors play an increasingly influential role. As stated in one of the provided texts, investors recognize ESG indicators as valuable predictors of a company's long-term sustainability.  Machine learning algorithms scrutinize multifaceted ESG data, revealing hidden patterns and correlations. This capability empowers investors to make informed decisions aligned with their values and financial interests.  Additionally, research from MSCI demonstrates that companies with strong ESG performance tend to experience lower volatility, suggesting that integrating ML-enhanced ESG insights could lead to more stable investment returns.

Predictive modeling, facilitated by ML, further amplifies the impact of ESG strategies. By analyzing historical ESG data and external variables, ML algorithms predict a company's future performance in ESG domains.  For instance, a company aiming to reduce its carbon footprint can utilize ML to identify areas with the greatest potential for improvement.  The resulting data-driven insights inform strategic decision-making, aligning business operations with sustainability objectives.  Research by Harvard Business Review affirms that predictive modeling through ML enhances the efficacy of ESG strategies, aiding in identifying high-impact sustainability interventions.

Moreover, ML can bolster transparency and accountability, crucial facets of effective ESG reporting. Advanced analytical capabilities of ML algorithms ensure ESG data's accuracy and reliability, building trust among stakeholders.  Additionally, ML facilitates real-time monitoring of ESG metrics, enabling companies to promptly identify deviations from established targets.

PKF Malta, a trailblazer in ESG initiatives, has taken the proactive initiative to orchestrate a series of ESG conferences.  These conferences have been strategically structured to navigate the path towards fostering a more sustainable local economy.  The E and S facets of ESG were already comprehensively addressed on April 12, 2023 and June 27, 2023, respectively, while the forthcoming months will witness the focused deliberation on the G aspect of ESG.

The core intention driving these conferences is the creation of a dynamic and interactive platform that facilitates substantial dialogues. This platform serves as a catalyst for stakeholders to not only identify potential solutions for the future but also to engage with the imminent social issues and challenges. This engagement occurs within the broader context of both macro and micro perspectives.

Diversity is paramount in our selection of speakers, who represent a spectrum of fields. These include prominent figures from the business sector, esteemed scholars, perceptive sociologists, and influential policymakers.  Among the distinguished speakers were Dr Michael Falzon, the Minister for Social Policy and Children’s Rights, and the esteemed Honorable Claudette Buttigieg, the shadow minister overseeing Civil Liberties, Social Dialogue, and Diabetes.

PKF Malta is also leveraging its membership with PKF International to further learn and contribute within the field of ESG reporting, auditing and advisory services. PKF Malta also boasts that one of its staff members forms part of the PKF international ESG Steering committee. PKF International is setting forth a holistic approach with respect to its members’ service offering which will enable PKF members to offer their clients a global outlook when it comes to ESG, covering most of their requirements. This is especially useful with multinational companies which would be required to report non-financial data in different jurisdictions in a comprehensive manner.  PKF International’s bringing together experts in different fields of ESG pillars from within PKF members to create a database of information and subject matter experts. There are also efforts underway to identify software and subscription based online ESG tools for all its members.  With this system, member firms can then take on clients with different and complex needs, having the knowledge that they can reach out to subject matter experts from within other member firms.  When it comes to ESG services, collaboration is key and PKF Malta has already collaborated with members of other PKF firms when it comes to ESG.

In closing, combining machine learning and ESG strategies holds great promise for companies wanting to bolster sustainability. ML's precision with data, predictive modeling, and transparency aligns seamlessly with multifaceted ESG concerns. This union empowers better decisions, engaged stakeholders, and sustainable operations. As tech advances, their synergy will drive positive change for a more sustainable future.