A vibrant fintech ecosystem is emerging to solve some of the most complex Environmental, Social and Governance (ESG) data challenges. In this article, Jean-Philippe Hecquet provides six fintech case studies, covering Artificial Intelligence, blockchain, satellite imagery, big data and robo-advisors.
Part 1 – ESG Data Landscape: Overview & Challenges
Standard ESG data providers have significantly strengthened their offer and increased their scale over recent years. However, they are still struggling to solve key data challenges and no single provider can currently provide a robust ‘one-stop-shop’ ESG solution. The most sophisticated institutional investors typically have to implement a multi provider approach leveraging a mix of standard ESG data providers (e.g. MSCI[1], Sustainalytics[2], etc.), complemented by specialised providers (e.g. Carbone 4[3], Trucost[4], Beyond Ratings[5], etc.), fintechs (e.g. Four Twenty Seven[6], Carbon Delta[7], Truvalue Labs[8], etc.) and consultants.
In particular, as a result of the low trust in company-reported information, a fintech ecosystem has emerged which aims to go beyond company-reported data sources. To do so, these fintechs are harnessing an arsenal of new technologies: big data based on asset-level information (facilities, power plants, etc.), natural language processing (NLP), the Internet of Things (IoT), satellite imagery, blockchain, and robo-advisors.
If properly integrated, these new technologies and alternative data sets could give an investment firm a significant competitive edge. Although the proliferation of data providers can make an investor’s operating model even more complex, this can be mitigated by outsourcing the data management to banks. In fact, the custodian arms of banks are ideally placed in the investment value chain to provide the required infrastructure. As such the model of custodians is evolving from the ‘safe-keeping of assets’ to the ‘safe-keeping of data’, grounded in a multi provider approach. A race is taking place amongst them to evolve their capabilities and integrate this fintech and data ecosystem. Naturally, immense gains can be expected for the winner.
A thirst for higher ESG data quality
The BNP Paribas ESG Global Survey 2019[9] highlighted that ESG data remains the biggest obstacle to ESG integration for investors, well ahead of costs, a lack of advanced analytical skills and the risk of greenwashing. At 66% across all respondents, the evidence of this ESG data challenge in 2019 is even greater than reported in our 2017 survey[10]. This suggests that investors have become more sophisticated in their ESG integration, demanding a solution to the current data challenges they face.
Part 2 – The Emergence of new ESG Data Technologies, illustrated through six case-studies
- Beyond company-reported information: The rise of asset-level data
- Climate scenario analysis through big data: New ways of modelling climate change
- Implementing the UN SDGs: An emerging blockchain platform
- Real-time ESG market sentiment analysis using artificial intelligence (AI)
- Another way of using AI: Quantifying alignment to the UN SDGs
- Channelling retail investors’ funds towards sustainable investments with robo-advisors
[1] https://www.msci.com/esg-ratings
[2] https://www.sustainalytics.com/
[5] https://beyond-ratings.com/
[7] https://www.carbon-delta.com/
[8] https://www.truvaluelabs.com/
[9] The ESG Global Survey 2019: Asset Owners and Managers Determine their ESG Integration Strategies. BNP Paribas and Longitude Research
[10] Great Expectations for ESG: What’s Next for Asset Owners and Managers? BNP Paribas and Longitude Research
