No better sector is better placed to drive the transition to a sustainable economy and a stable future than the finance sector itself. ESG integration in the financial sector will pave a path for all stakeholders being considered in the decision making process, which will result in real-world impact benefiting everyone.”
This quote, from an EY report titled “How ESG Integration in the Financial Sector Can Help Build Resilient Systems of the Future” speaks to the notion that the integration of Environmental, Social, and Governance (ESG) criteria goes beyond just smart investing and, instead, transcends all financial processes — from investment and insurance to credit scoring and risk identification.
Organizations, though, face many challenges on the road to successfully embedding ESG across key financial processes, including but not limited to:
- Developing the right analytics and models
- Blending traditional financial data with ESG metrics
- Ensuring ESG fuels all financial decisions and products with the right impact
- Lack of understanding of the key role alternative data can play
- Finding a collaborative data science and analytics approach and platform for ESG data analysis and modeling
At its core, ESG is all about ensuring that financial players do not make their decisions based solely on financial KPIs, but also by leveraging extra-financial indicators which are significant revealers of the reality of business models and their impacts. While ESG historically emerged in the asset management space, it’s now clear that the entire financial sector can benefit (especially given the last 10 years of acute consciousness of the materiality of climate change impacts, the emergence of binding regulations, and the rise in demand from consumers).
Combating the Data and Modeling Challenges Associated With ESG: A Collaborative Approach
First, defining metrics associated with climate-related and environmental risk drivers (and then blending that data) can be a daunting undertaking for all financial institutions seeking to embrace ESG for their entire scope of activity. A direct consequence is that financial players have to navigate in a thick jungle of possible data sources (internal vs. external, raw vs. transformed, traditional vs. alternative, and so on) and have to make complex choices on how and when to use them.
Further, organizations need to select ESG data sources and build ESG models, a process that will be deemed impossible in silos. There’s no “one-size-fits-all” approach when it comes to ESG, so they must have approaches which allow them to both develop analytics or models specific to certain activities or processes, while fostering consistency and reuse across business lines.
Finally, for ESG to be successfully embedded across an organization’s key processes, collaboration among all stakeholders is required. For example, ESG experts, core teams, data scientists, and risk teams must be aligned across model development, ensuring explainability for all business teams and validation from the risk team.
How, though, can organizations move past understanding the risks and opportunities associated with ESG and begin to accelerate this data-driven transformation in practice? It really comes down to having an agile analytics and data science platform approach. With Dataiku, for example, organizations can embrace all three dimensions of ESG (instead of prioritizing one over the other), foster collaboration between teams and profiles (such as business teams working with data scientists on ESG-specific initiatives), and operationalizing outputs. It is important to note that everything we have described here is in progress across and just as relevant for other industries. As pressure from the public increases, other sectors will have to meet acute ESG convictions.