The world is changing, and so is the way we do business. Environmental, social, and governance (ESG) programmes have gone from merely “nice to have” to an essential part of corporate strategy and operations. This growth is fuelled by various trends such as climate change, income inequality, demands for transparent governance, and an increased desire among consumers to support brands that positively impact their communities and the environment.
For most business leaders, ESG is a top priority with a growing urgency. A new global survey reveals Singapore as one of three countries that ranks ESG efforts as the number one organisational priority, with 87% of executives believing that delaying or scaling back on sustainability goals damages company value.
Another study shows that public companies in the Asia Pacific region are catching up to their global counterparts by integrating ESG measures into executive incentives, as Singapore takes the lead for sustainability reporting globally.
Analysts have earmarked the finance sector as a key player in the propelling shift towards a sustainable economy and a more stable future. The UN-led Net-Zero Banking Alliance — which includes regional institutions such as DBS Bank, OCBC O39 , UOB U11 and Maybank — is yet another indication that the financial industry is getting organised to play a vital role in achieving climate targets.
As highly regulated institutions, financial organisations are well aware of the scrutiny they face if commitments and actions are misaligned. The recent fines assigned to a Deutsche Bank subsidiary were the first to demonstrate these possible consequences.
In effect, the need for ESG embedding across all core financial processes and steep improvement in quality and auditability of reporting demands a significant uptake of investment in ESG modelling strategies, including ESG data selection and integration, ESG risk methodologies definition, supported by the right mix of robust analytics foundations setting, agile development and data science.
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Embedding ESG across core financial processes
ESG metrics measure several extra-financial risks, whether they pertain to a company, a specific project, or an end-consumer. A core area of focus is CO2 emissions, understanding and considering its direct link to climate change.
However, an organisation’s ESG focus can run across several other areas, such as regulatory compliance, energy management, business conduct and ethics, workplace health and safety, supply chain sustainability, diversity, equity and inclusion. Each of these areas will have metrics to measure performance and disclosure requirements.
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Investors use such metrics to assess company performance and accountability on their ESG commitment. They also largely influence decisions made by banks and insurance companies to take financing and insurance decisions in their search for improved sustainable impact. While ESG historically emerged in the asset management space, it is now an industry-wide transformation, impacting all financial processes.
One of the major challenges faced by financial players that want to further ESG goals is having to juggle more than a dozen data providers on top of their traditional financial data sources. This data deluge comes in thicker and faster by the minute, combining that of some large providers (such as MSCI, S&P Global, and Sustainalytics) with data issued by smaller organisations focused on specific topics for a differentiating edge to be enriched by their reviews, alternative data, and risk assessment methodologies.
Developing an effective ESG framework for reporting and decision-making requires careful consideration of various factors by financial players. This includes selecting appropriate large providers and niche organisations, analysing public data, incorporating internal insights and proprietary signals from unstructured data, and creating alignment on modelling and outputs.
Here are the three key tenets to keep in mind:
- Define metrics associated with each type of ESG dimension, starting with climate-related and environmental risk drivers. This can be a daunting undertaking for all financial institutions seeking to embrace ESG for their entire scope of activity, leading to financial players having to navigate their multitude of possible data sources and make complex choices on how and when to use them.
- Select ESG data sources and build ESG models — an impossible process in silos. There’s no “one-size-fits-all” approach to ESG, so they must have approaches that allow them to develop analytics or models specific to certain activities or processes while fostering consistency and reuse across business lines.
- All stakeholders must collaborate for ESG to be successfully embedded across an organisation’s key processes. This includes aligning ESG experts, core teams, data scientists, and risk teams across model development to ensure justification and explanation to business teams and validation from the risk team. Collaboration also supports the need to evolve methodologies over time as understanding of ESG matures and regulation structures itself.
The above is far from a given, and financial institutions face their shades of challenges as they aim to address these. Banks, insurance companies and asset managers all share the struggle of aligning methodologies across the diversity of asset classes and activities they run.
As simple as it sounds, on the investment side, having consistency in the approach between equity and credit — even if issued by the same company — can have significant implications on business management. Having the capacity to easily test and back-test ESG models collaboratively, understand the implications of a change in a provider methodology, and expose it for quick turnaround and decision-making becomes critical to success.
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As another example, if financial teams want to do an ESG-refined credit assessment of different manufacturing companies, they will need to leverage various data sources, structured and alternative to ensure they fully assess specific risks and opportunities of each business model. They will also need to ensure that everything they do is consistent across the board and considers constantly evolving dynamics, with the capacity to manage risks in an aggregated manner and answer any questions from stakeholders and regulators.
To progress ESG goals, financial organisations should be looking at actively leveraging collaborative analytics practices through a transversal platform approach to guarantee alignment, scale and business empowerment.
Collaborative analytics and data practices empower ESG efficiency
To put effective frameworks in place for ESG, it is necessary to involve all stakeholders. This involves bringing together ESG experts, credit modellers, traders, financial engineers, and operational process owners in the same environment to build the right approaches and ESG models jointly. Financial services organisations may also seek to enrich their core ESG data with additional data sources for specific dimensions. For example, can satellite images be used to assess flooding risks in an environment with extreme weather events?
The capabilities to blend, test, and complete data coverage through machine learning, reverse engineering, and quick review over time will be paramount to winning the ESG race. When building their ESG initiatives, financial companies must consider approaches that allow them to develop analytics or models specific to certain activities or processes and ensure consistency and reuse across business lines.
Conversations on ESG and sustainability will continue to grow, and financial services will face increased pressure to meet new regulatory requirements. In the race to achieve targets, forward-thinking organisations will be those that not only accelerate the changes to ESG data frameworks but also build a community of professionals to drive this change.
James Ang is the senior vice president for Apac at Dataiku
