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

Context:

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, …).

Objective:

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

Technologies:

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

Solution:

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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