Use case: AI-enhanced thematic portfolio construction (ETF selection)


A leading Swiss private bank needed to automate and enhance the selection of ETFs aligned with specific investment themes, previous attempts failed due to biased data (greenwashing, languages, …).


Compute purity scores between an ETF (and its constituents) and a defined theme based on news, patents, and web information.


  • Python
  • Natural Language Processing
  • Web scrapping and data mining


A front-end interface enables the selected themes and ETFs, computing the purity scores as well as enabling the deep-dive and interpret the results. Our solutions analyze millions of information and address bias in multiple ways to ensure reliable and accurate insights.


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