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Jan 15, 2025 | 
insight.factset.com | FactSet Insight |Danielle L. Karr |Yogendra Miraje
Given potential for more M&A activity under the new administration and Congress, investment banks that thread AI tools into their workflows can scale their capacity for deals. Specifically, the right AI tools will enable junior bankers to streamline and complete their core activities materially faster. They can apply the reclaimed time toward additional pitches or client-management tasks for senior bankers.
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Nov 13, 2024 | 
insight.factset.com | Danielle L. Karr |FactSet Insight |Yogendra Miraje |Yuri Malitsky
Given the amount of data, sources, formats, systems, and compliance elements that asset management firms are working with, logging research can sometimes feel like the investment-world equivalent of orchestrating global shipping logistics at peak season. Lots of people are involved, a substantial volume of content must be tracked and kept up to date, there are numerous places to store it, and the flow is constant. It’s not uncommon to grapple with those challenges.
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Oct 8, 2024 | 
insight.factset.com | FactSet Insight |Yogendra Miraje |Yuri Malitsky |Lucy Tancredi
To drive business growth, data-management teams at financial institutions are working with substantially more data than in past years. There are material benefits, challenges, and costs that factor into the trend, making it a popular topic of discussion. The purpose of this article is to highlight the operational elements that data teams are sorting out. They need to simplify complexities and prioritize tradeoffs as they drive toward a single connected data pipeline from disconnected data silos.
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Jun 17, 2024 | 
insight.factset.com | Lucy Tancredi |FactSet Insight |Yogendra Miraje |Ivo Kolarov
AI Strategies Series: 7 Ways to Overcome Hallucinations June 17, 2024 Hallucinations are perhaps the most important limitation of generative AI to stem. This article—the second in our six-part series... By Lucy Tancredi | Data Science and AI Read more June 17, 2024 AI Strategies Series: 7 Ways to Overcome Hallucinations Hallucinations are perhaps the most important limitation of generative AI to stem.
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Apr 29, 2024 | 
insight.factset.com | FactSet Insight |Yogendra Miraje |Lucy Tancredi
Data Science and AI By FactSet Insight | April 29, 2024 It's the double whammy of peak earnings season: How to be everywhere at once (earnings calls) and how to more quickly analyze this quarter's information (transcripts). Transcript Assistant, powered by FactSet Mercury, both summarizes key elements in earnings transcripts and enables you to search them by typing your own questions (not just a keyword search).
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Apr 25, 2024 | 
insight.factset.com | FactSet Insight |Yogendra Miraje |Lucy Tancredi
It's the double whammy of peak earnings season: How to be everywhere at once (earnings calls) and how to more quickly analyze this quarter's information (transcripts). Transcript Assistant, powered by FactSet Mercury, both summarizes key elements in earnings transcripts and enables you to search them by typing your own questions (not just a keyword search). It also visualizes call sentiments and provides summaries of the management/analyst Q&A discussions.
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Feb 27, 2024 | 
insight.factset.com | FactSet Insight |Lucy Tancredi |Yogendra Miraje
We sat down with Seth Stephens-Davidowitz to discuss his research and perspectives on data ahead of his May 1 keynote address at the FactSet FOCUS event in Miami. Seth has an expansive view into the role and power of data. He earned a PhD in Economics from Harvard and has worked as a data scientist at Google, a visiting lecturer at the Wharton School of the University of Pennsylvania, and a contributing op-ed writer for the New York Times.
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Feb 21, 2024 | 
insight.factset.com | Lucy Tancredi |Yogendra Miraje
So far in our AI strategies series we’ve discussed LLM capabilities and limitations, hallucinations, and lack of explainability. Now let’s turn to part four, a discussion of why LLMs provide inconsistent responses to the same prompt and generate outdated answers. Inconsistent responses to the exact same prompt happen because Large Language Models are intentionally built with some allowance for variability. In a previous installment of this series, we looked at an ”.
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Feb 14, 2024 | 
insight.factset.com | Lucy Tancredi |Yogendra Miraje
Given the potential for generative AI hallucinations, users understandably want source links or references to show where a Large Language Model (LLM) finds its answers. In this third installment of our six-part series, we discuss the challenge of generative AI’s lack of explainability—specifically, the inability of LLMs to cite sources—and a solution called RAG for developers to implement explainable output.
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Feb 13, 2024 | 
insight.factset.com | Yogendra Miraje |Yuri Malitsky |Lucy Tancredi
The emergence of generative AI has amplified the importance of reliable data in factual, data-centric decision-making processes. With instruction-tuned Large Language Models (all LLM references in this article are instruction-tuned LLMs) such as ChatGPT on the scene, interaction with machines has advanced significantly and opened new possibilities for conversing with data in natural language—similar to talking with a colleague.
