AI Exchanges: The Role of Data

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Sep 30, 2025

Neema Raphael, Chief Data Officer at Goldman Sachs, brings a wealth of knowledge on data strategy. She shares her journey from software engineer to leading data efforts, emphasizing the transformative potential of generative AI. Neema discusses the critical need for cultural shifts towards probabilistic models in finance and addresses the myth of data scarcity, arguing for the role of synthetic and proprietary data. She also highlights the importance of data cleaning and linking in unleashing AI's true business value, while addressing AI's impacts and her personal uses of technology.

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ANECDOTE

Copter Saved The Firm And Sparked Data Use

Neema Raphael described building the Copter database after the 2008 crisis to measure exposure to Lehman Brothers.
That project turned data from 'business exhaust' into a platform traders and quants used to generate new ideas.

INSIGHT

Generative AI Is A Step Change

Generative AI continues the shift from rule-based to example-based computing but enables creation of images, audio, and language.
That generative step feels like a step change compared with past machine learning advances.

INSIGHT

Embrace Probabilistic Outputs

Generative models are probabilistic and can produce helpful but non-deterministic outputs that are not always traceable.
Organizations must shift mindset from expecting repeatable deterministic outputs to accepting probabilistic extrapolations.

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Goldman Sachs’ Neema Raphael, the firm’s chief data officer and head of data engineering, discusses the role of data in enabling or possibly stalling AI’s progress with George Lee, co-head of the Goldman Sachs Global Institute, and Allison Nathan, senior strategist in Goldman Sachs Research.

This episode was recorded on September 25, 2025.

The opinions and views expressed herein are as of the date of publication, subject to change without notice, and may not necessarily reflect the institutional views of Goldman Sachs or its affiliates. The material provided is intended for informational purposes only, and does not constitute investment advice, a recommendation from any Goldman Sachs entity to take any particular action, or an offer or solicitation to purchase or sell any securities or financial products. This material may contain forward-looking statements. Past performance is not indicative of future results. Neither Goldman Sachs nor any of its affiliates make any representations or warranties, express or implied, as to the accuracy or completeness of the statements or information contained herein and disclaim any liability whatsoever for reliance on such information for any purpose. Each name of a third-party organization mentioned is the property of the company to which it relates, is used here strictly for informational and identification purposes only and is not used to imply any ownership or license rights between any such company and Goldman Sachs.

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