Here are 10 examples of how financial firms are currently using Amenity's NLP solutions to turn complex documents and narrative content into structured data sets that can be processed, visualized, and analyzed.
Natural language processing (NLP) turns complex documents and narrative content into structured data sets that can be processed, visualized, and analyzed. Here are 10 examples of how financial firms are currently using Amenity's NLP solutions.
Companies seeking to responsibly enhance their decision frameworks use Amenity's ESG model to incorporate real-time, objective, and transparent insights into company-reported statements and filings.
Analysts and investors leverage Amenity's suite of NLP solutions to generate new ideas by expanding their investable universe, improving decision making with objective analytical tools,and building robust investment theses by interrogating financial text data through differentiated methods.
Fundamental and quantitative investors alike use Amenity's Key Drivers as unique lenses into the fundamental forces driving equity valuations. Hundreds of underlying event types are aggregated into dimensions that are immediately relevant to equity analysis and portfolio management.
Investment allocators use Amenity’s NLP solutions to analyze RFPs, DDQs, pitch-books, meeting notes and other manager-supplied materials. The extracted information (based on proprietary taxonomies) allows them to better rate and score investment managers.
Portfolio managers and risk analysts use Amenity's Key Drivers and ESG model to discern, attribute, and manage exposure to the behavioral dimensions of the investment universe, alongside traditional fundamentals.
Institutional investors use Amenity's NLP solutions to analyze broker and internal research by leveraging Amenity's data science team ("Text AI") or by directly building their own models that prioritize, classify, and extract valuable content with our end-to-end development platform.
Companies partner with Amenity to aggregate, score,and manage emerging risks with detailed taxonomies, which can be customized for investors, insurance underwriters, HR professionals, and beyond.
Quantitative investors use Amenity's Signal model to analyze documents and derive best-in-class sentiment scores (based on our core fundamental taxonomy) to deploy in long-short and long-only strategies.
Companies exposed to outliers and anomalies use Amenity's alerting capabilities and time-series analytics as early detection mechanisms and trend trackers. We have extensive experience customizing to specific use cases, including voice-of-the-customer, sales enablement, monitoring for real-time exchanges, and overnight sector updates for traders and specialists.
Research providers and investors use Amenity's in-depth tagging capabilities to classify text data—at scale— across themes, and sectors to improve discoverability, entity targeting, and content classification.
This communication does not represent investment advice. Transcript text provided by S&P Global Market Intelligence.
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