For Urgentum(UK).
Urgentem, is the award-winning provider of emissions data and climate risk analytics to the finance industry. Urgentem is acquired by Intercontinental Exchange
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Urgentem, is the award-winning provider of emissions data and climate risk analytics to the finance industry. Urgentem is acquired by Intercontinental Exchange
֍Our product is called Element 6. I contributed to the NLP module of Element6.
֍ USP of our product - The government of different countries are implementing strict
environmental policies for companies. As result, asset managers (Target customers) are
looking at ways to evaluate and optimize their portfolio of companies on an
environmental basis.
֍ Our product takes a portfolio of companies as Input and analyzes the everyday news
data for these companies and declares an Environmental score.
֍ Based on the Environmental score of these companies Asset managers will optimize
their portfolios essentially to decrease losses.
֍ Well-experienced in integrating climate risk factors in quantitative risk models.
֍ Statistical modeling and Deep-Learning Models.
֍ We achieved it using topic-specific sentiment analysis. A combination of text
classification on environmental topics and sentiment analysis.
֍ For text classification, we worked on different deep learning techniques and finalized on
Keyword model, ESG BERT, and BERT model (with our training dataset)
֍ We had 26 environment labels, and we classified each sentence of the news data into
different topics.
֍ For the sentiment analysis model: The zero-shot classification model (BART) was
implemented as a pre-trained model from Facebook.
֍ The results are stored in MongoDB (NoSQL).
֍ We had a news vendor called Datanews.io for news feed on different companies.
֍ News Feed --> Preprocessing --> Sentence wise Topic classification model -->
Sentiment Analysis Model.
֍ All the classified results are stored in MongoDB and retrieved back from the DB whenever
required.
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