Roundup: Our Research to Advance Machine Learning in Finance

From Explainable AI to NLP and privacy-preserving ML, read about our ongoing research efforts to make banking better

At Capital One, we believe the power of technology can give customers greater protection, confidence, and control of their finances. Among the most impactful ways for us to achieve these goals are the responsible, human-centered use of real-time data and machine learning.

Our machine learning research program centers on exploring and elevating forward-leaning machine learning—the methods, applications, and techniques that will make banking simpler and safer. We’re advancing this research to inform how machine learning is developed and implemented across the banking industry in the years to come. Our research findings are also incorporated into the fabric of our machine learning ecosystem, helping our models become more powerful, adaptive, and well-managed.

How We’re Advancing Research

Our research agenda explores areas critical to our business as well as machine learning theory—often alongside some of the nation’s top research universities. We’re open sourcing tools to make machine learning models more well-managed, repeatable, and searchable. We’re working to understand how a range of deep learning techniques can become more explainable and interpretable. We’re exploring novel applications of graph embeddings to uncover financial crime. And we’re leveraging neural networks to protect sensitive data.

Capital One Machine Learning Research Priorities:

  • Explainable AI: Creating transparency and ensuring fairness through explaining ML models.
  • Graph networks: Using ML on nodes and edges of financial networks to more accurately identify fraud.
  • Anomaly Detection: Identifying changes in data to protect customers and adapt to fluctuating environments.
  • Natural Language Processing: Teaching intelligent assistants to understand and generate natural language.
  • Privacy and Data: Developing models and techniques for protecting sensitive customer data.
  • Machine Learning at Scale: Building systems to scale the entire model building and deployment process.

Research Highlights & 2022 Preview

Check out some of our most recent machine learning research publications and keep an eye out for more updates in 2022. We’ll be further exploring synthetic data generation and federated learning at scale to enhance privacy efforts; tabular data solutions to bolster our machine learning capabilities in areas like fraud detection; explainability methods including topical data analysis and model introspection; sequence modeling for credit risk, and much more.





Join Us

If you’re interested in helping us research and build the technology that will drive the future of banking, learn more about our machine learning efforts and take a look at our open tech career opportunities.

Capital One Tech

Stories and ideas on development from the people who build it at Capital One.

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