Advancing state-of-the-art science and AI in financial services (AIM263)

Scaling AI at Capital One: A Journey of Transformation

Introduction

  • James Montgomery, Director of Data Science at Capital One, discusses the company's approach to AI at scale and its integration of cutting-edge AI research in the finance industry.
  • Provides a brief overview of his background and the transformation he has witnessed at Capital One over the past 10 years.

Capital One and Machine Learning

  • Capital One is a relatively young bank (about 30 years old) that has evolved from a credit card company to a large retail and commercial bank with over 100 million customers.
  • Leverages machine learning in various areas:
    • Adversarial use cases: Fraud detection, cybersecurity, anti-money laundering
    • Marketing and customer acquisition
    • Optimizing customer and employee experiences

Lessons Learned: The Human Component

  • Enabling cross-functional partnerships is crucial for scaling AI responsibly:
    • Diverse research teams with data scientists, engineers, business/product partners, and applied researchers
    • Collaboration with lines of business, risk/compliance teams, and other internal stakeholders
    • Early and frequent engagement to align on strategic priorities and navigate challenges
  • Partnerships beyond Capital One's walls:
    • Collaborations with top-tier academic institutions
    • Partnerships with technology providers (e.g., AWS) to leverage expertise and platforms

Lessons Learned: Modernizing the Tech Stack

  • Transition to the cloud and standardization of the data ecosystem:
    • Increased flexibility, reliability, and accessibility of data
    • Enabled the use of open-source software and modern tools
  • Significant investments in building a talent pool of 14,000+ technologists
  • Strategic initiative to deploy AI at scale throughout the organization:
    • Collaboration between data scientists and engineers to address challenges
    • Adoption of tools like Kubeflow to improve efficiency and scalability

Recent Initiatives and Challenges

  • Personalized and proactive customer communication:
    • Leveraging heterogeneous, dynamic, and tabular data
    • Addressing challenges in model deployment and integration with customer-facing systems
  • Improving employee efficiency through generative AI-powered agent servicing:
    • Integrating language models, vector databases, and real-time inference
    • Ensuring robust evaluation, testing, and governance to mitigate failure modes

Conclusion

  • The journey of scaling AI at Capital One has been challenging but rewarding, with a continued focus on responsible innovation and cross-functional collaboration.
  • The company's investments in modernizing its tech stack and building a diverse talent pool have enabled it to push the boundaries of AI research and application in the finance industry.

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