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Operationalising ESG - Data & Automation

ESG is a broad church, covering not only the sustainability credentials of the client, but also their suppliers (so called Scope 3 emissions). The scope of information that needs to be captured, validated, and fed into the ESG rating necessitates a comprehensive data strategy. The first step is to define a ESG policy, usually an institution-specific amalgamation of key standards like TCFD and IISB. 

Once the policy is agreed, the next step is to determine how the data will be captured. There are several options here: 

  1. Re-use existing KYC data where there is an overlap for things like Nature of Business, Industry Codes, Board of Directors, etc. 

  2. Reach out to the market for public data on larger corporate clients 

  3. Outreach to the client either directly, or through self-serve client portals 

When selecting a market data provider (or multiple providers), it is important that minimum criteria of quality and granularity are maintained. This means sourcing data that is verified and quantitative, rather than subjective, qualitative assessments of ESG performance. For this reason, larger Financial Institutions will tend to forego reliance on external rating providers (which are often the same firms as data providers), in favour of generating their own data-backed, traceable rating. This is due to the opacity of many rating methodologies, and the huge rating score variance between different providers for the same entity.  

Where market data is not yet available (i.e., smaller private companies), the process becomes one of outreach; how best do we request data from clients and fulfil the ESG requirements.   

As with data, the process of ESG has the potential to be labour-intensive and slow. Completed manually, ESG requires a lot of personnel. Even if a financial institution were willing to hire a team of (pricey!) ESG specialists to complete the due diligence, they will struggle to get them. ESG specialists are thin on the ground. 

For reasons of cost, resource availability, and most importantly client experience, successfully implementing ESG depends upon automating processes where possible and appropriate. We cannot create an operational issue while solving a compliance one, making automation a must-have up front. 

As touched on above, data can be automated by re-using data and leveraging external data providers. The provenance of data is also crucial. We don’t want to pull in information of questionable quality of source.  

From a process perspective, automation extends to the automated screening of Controversial Activities. Like Adverse Media for AML, Controversial Activity looks at the news stories around the client to determine the presence of incidents that would affect the ESG rating. These can be automatically resolved where they are low relevancy or materiality, saving on manual effort. 

Ratings themselves can be automatically calculated, with the option of conditional reviews for unusual clients or tricky segments (e.g., uranium mining or ammunition manufacture). Overall client reviews can similarly be automated, where well-performing clients are automatically accepted, and only unusual or poorly rated clients escalated for review.  

By embedding automation in the ESG process, financial institutions can deliver regulatory compliance and a deep client understanding without sacrificing customer experience and lead times. 


Sample Scenario

The below scenario is an amalgamation of several client challenges and experiences.

Anorak Finance (an assumed name) were an early adopter of ESG, defining an ESG policy in 2021 and rolling it out as part of their standard onboarding process. However, they quickly realised that they faced an operational issue when it came to ESG – they were achieving compliance, but the due diligence effort was damaging their lead times and ultimately the customer experience. Staffing costs were also rising as more entity types and regulations came into scope

To address this, Anorak took three main actions. Firstly, ESG was ‘brought in from the cold’ and made part of existing CLM processes, rather than a standalone activity. This brought efficiencies by re-using teams. Secondly, they brought in market data providers which drastically cut down the effort to complete due diligence on larger corporates and publicly listed companies. Finally, processes in screening, risk, and approvals were automated for highly-rated and low-risk clients so that teams could focus on more complex customers.

By automating in a diverse fashion through a mixture of services, internal processes, and operating model, Anorak were able to address their OpEx challenges and transform the customer experience.


Key takeaways: 

  • Get on top of evolving regulations and forge a policy according to the firm’s risk appetite and market perspective
  • Go beyond compliance – lead from the top with a clear communication strategy and action plan 
  • Consider ESG as an opportunity for differentiation and growth, not simply a cost or compliance burden 
  • Invest in your teams for long-term benefits and returns 
  • You don’t necessarily have to reinvent the wheel – engraining ESG across the CLM journey can ensure compliance without negative impact to the customer 
  • Re-use existing data and leverage the existing data provides that you trust 
  • Leverage technology for rating calculations and client reviews, so you can focus on the high risk clients 


This blog was written in collaboration with Aurora.

Read about how Fenergo's ESG Compliance Software solution can help your organisation.

About the Author

Daragh has 10 years’ experience in Fintech, ranging from tech start-ups to some of the biggest banks in the world. After six years at Fenergo working across professional services and product, Daragh now works to collaborate with and deliver value to our clients as part of our strategy team, helping them to address compliance challenges.

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