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Carmen Recio and Sergio Gago, Moody’s Analytics

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September 18, 2023
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40:20
min
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Overview

Carmen Recio, an applied researcher in quantum computing focusing on the financial services industry, and Sergio Gago, the managing director of both AI and quantum computing at Moody's, are interviewed by Yuval Boger.

Read the full transcript or listen to the episode here.

Here are the key points:

  1. Roles and Responsibilities: Carmen leads the Moody's Analytics team and focuses on applying quantum computing to financial services. Sergio, who initially kickstarted the quantum computing team at Moody's, now oversees both AI and quantum computing efforts.
  2. Impact of AI on Quantum Computing: Sergio notes that AI technologies like ChatGPT have been taking attention and resources away from quantum computing. He emphasizes that while both technologies are important, they serve different purposes and should not be conflated.
  3. Moody's and Quantum Computing: Moody's is interested in quantum computing because it promises to improve their data analytics capabilities. They are exploring its applications in machine learning, simulation, and optimization, among other areas.
  4. Quantum Computing Risks: Carmen highlights that Moody's is also considering the risks associated with quantum computing, particularly its potential to break current cryptographic systems. They are working on a post-quantum crypto strategy.
  5. Community Engagement: Both Carmen and Sergio mention the importance of staying close to the academic and research communities to keep up with the latest developments in quantum computing.
  6. Thought Leadership: Moody's aims to be a thought leader in the space, helping their clients understand the potential and risks of quantum computing. They are developing training materials and conducting research to fill knowledge gaps in the financial industry.
  7. Customer Perspective: Sergio notes that while some financial institutions are investing in quantum computing, many are still in a "wait and see" mode. He argues that this approach is not sufficient and that companies should start preparing now.
  8. Explainability Challenge: Sergio points out that one of the challenges with quantum computing, similar to AI, is the issue of explainability. Regulatory requirements often demand that models be fully explainable, which could be a hurdle for quantum computing applications.
  9. Future of Quantum Computing: Carmen stresses that it's not about when quantum computing will become mainstream, but rather about being prepared when it does. She emphasizes the steep learning curve and the need for companies to start understanding its impact now.
  10. Advice for Mid-Sized Banks: Sergio suggests that mid-sized banks, which may not have the resources to invest heavily in quantum computing, could benefit from "algorithm-as-a-service" solutions. Moody's is working on such offerings to help these banks leverage quantum computing without needing in-depth expertise.
  11. Economists and Quantum Computing: Carmen believes that economists should focus on identifying their computational bottlenecks and leave the technical details to experts. She suggests that quantum computing should be abstracted away from end-users like economists, who should specify their business needs instead.
  12. Unbiased Study: Sergio mentions that Moody's conducted a study to understand the penetration of quantum computing in the financial industry. Unlike studies from companies that have a vested interest in quantum, Moody's aims to provide an unbiased view.

The interview provides a comprehensive look at how Moody's is approaching the integration of quantum computing into their services, the challenges they foresee, and their advice for other organizations considering a similar path.

Read the full transcript or listen to the episode here.

Listen to the podcast or read the transcript

Guests

Carmen Recio
Quantum team lead, Moody’s Analytics
Sergio Gago
AI, Machine Learning and Quantum Managing Director, Moody's Analytics
Listen to the podcast or read the transcript

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