Why ESG and Collaborative Data Science Can’t Be Ignored by Financial Institutions

Dataiku, the most complete platform for AI, shares why embedding ESG data into every process is critical for financial organizations who want to remain competitive
Coal Plant Smoke From The Chimney. Ecological Pollution. Aerial
Ecological Pollution: Aerial drone view of smoke emitted from a coal plant.

Over the past 15 years, evaluating Environmental, Social, and Governance — or ESG — criteria has gradually evolved from being a niche activity to becoming a major trend for the entire financial industry. The growing regulation in the space combined with the constant pressure from investors and end customers and proven materiality of climate change are acting as powerful catalysts of a deep transformation of the financial industry, impacting all players: banks, asset managers, insurers, and others.

While the topic is now at the forefront of all financial players’ strategic agenda, successfully embedding ESG in all key financial processes is far from being a given. Developing the right analytics and models, blending traditional financial data with ESG metrics, and ensuring ESG fuels all financial decisions and products with the right impact are among the many challenges companies have to overcome. Environmental Social And Governance

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 that 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.

Esg Data Sources And Build Esg ModelsHow, 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. To read more about how to accelerate ESG embedding across the financial services value chain, check out this ebook

Copy Of Dku Logo Rgb TealDataiku is the industry-recognized platform democratizing access to data and enabling enterprises to build their own path to AI in a human-centric way. Don’t just take our word for it — see what some of the top analyst firms (including Gartner and Forrester) are saying about Dataiku.


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