Global asset manager (AuM: USD700bn+): Natural language processing for ESG investing

  •  30

    data fields analysed

  • 10

    alternative datasets sourced

  • 4

    NLP engines built

  • Director of ESG Integration and Research of a global institutional asset management firm
  • The client wanted an in-house ESG scoring framework that was fully transparent, able to provide real-time updates and customisable to the current portfolio
  • Scope included analysing textual data sourced from news/company publications to create sentiment scores based on UN SDGs
  • Deployed a three-member team – 1 lead data scientist, 1 NLP specialist and 1 data engineer – to build, test, deploy and fine-tune the NLP engines
  • Built a classification model to identify SDG statements within company reports
  • Build a bespoke sentiment analysis model for news data
  • Created a bespoke UI for portfolio managers


  • A proprietary in-house ESG scoring framework based on NLP engines – transparent, able to provide real-time updates and scalable
  • Algorithms to power NLP engines with overall accuracy of 85% and full ownership of intellectual property
  • 70%+ operational cost savings by leveraging a skilled data science team working from an offshore delivery centre
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