Artificial Intelligence: Using AI to discover insights from earnings calls
Summarizers by Bloomberg, AlphaSense, TiredBanker, and more are changing how financial data is analyzed and interpreted.
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Today we highlight the following:
AI: How AI in earnings call summarizations is revolutionizing financial analysis
CURATED UPDATES: LLMs and other Machine Models; AI Applications in Finance; Infrastructure & Middleware
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AI: The Rise of AI in Earnings Call Summarization
AI is being used to summarize earnings calls for financial investors, with different offerings from Bloomberg, AlphaSense, TiredBanker, and Aviso. This is improving how financial data can be analyzed and interpreted, helping tap into the conversational data that makes a difference for those making investment decisions.
Bloomberg recently announced the incorporation of an AI-powered earnings call summary tool into their Terminal platform. This tool translates financial earnings conversations into key bullet points, which helps users find strategic points like revenue guidance, capital allocation, and market demand from corporate discussions. Industry insiders note the tool's effectiveness in synthesizing trends across companies and distilling contentious points, shortening the time to get to insights.
For instance, Bloomberg shared their AI analysis of a JPMorgan transcript:
AI-analysis of this transcript helped answer questions such as “What drove 2023’s record net income at JPMorgan?”, and “Why is JPMorgan expanding its workforce when Citigroup Inc. is cutting 20,000 staff?”.
Bloomberg is one of several AI-powered earnings call analysis tools entering the market. See AlphaSense’s Smart Summaries, which uses natural language processing to isolate key events in earnings calls quickly. Another example is TiredBanker, which employs advanced AI models like GPT-3.5/4 to offer summaries of a decade’s worth of earnings reports and transcripts for S&P 500 companies. We are fans of being able to get historical data and test whether AI summaries have really added value for price prediction.
Or, see Aviso's AI platform, which uses natural language processing (NLP) and generative AI to derive sentiment shifts and future trends from earnings calls. These insights, often overlooked in standard financial metrics, can be hidden indicators of future company performance. The structure of management language and the delivery of particular messages can carry as much information as the financial substance.
One advantage of these AI-driven tools is their potential to mitigate human error in financial decision-making, potentially improving the quality of analysis. As the tools improve, we would expect near universal adoption across financial services — both to improve investment outcomes, as well as get competitor information and manage large businesses. The next step, of course, is the direct management of target corporations by AI financial analysts, but we are getting a bit too far ahead of ourselves.
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Curated Updates
Here are the rest of the updates hitting our radar.
Machine Models
⭐ Insurity analytics accelerates data-driven decision-making for P&C carriers - Fintech Finance News
AI Applications in Finance
⭐ Central banks face “black box” effect in AI - Central Banking
Cautious optimism for AI applications in banking - The Banker
Infrastructure & Middleware
⭐ Powder simplifies scaling client acquisition process - Fintech Futures
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Contributors: Lex, Laurence, Matt, Farhad, Daniel, Danny, Michiel, Bo