Machine learning's impact on finance: A research summary

Updated March 2024

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

Our machine learning research program centers on exploring and elevating cutting edge machine learning—the methods, applications and techniques that will make banking simpler and safer. We are 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 integrated into our machine learning ecosystem, enhancing the power, adaptability, and management of our models.

Our research agenda explores  critical areas for our business, as well as machine learning theory, often in collaboration with some of the nation’s top research universities. We are open-sourcing tools to make machine learning models more well-managed, repeatable and searchable. Additionally, we are working to understand how deep learning techniques can be made more explainable and interpretable. We are also exploring novel applications of graph embeddings to uncover financial crime and leveraging neural networks to protect sensitive data.

• 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.

Check out some of our most recent machine learning research publications and explore some of the updates that took place in 2023. We explore 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.

• From Explanation to Action: An End-to-End Human-in-the-loop Framework for Anomaly Reasoning and Management, presented at ICAIF 2023

• GOAT: A Global Transformer on Large-scale Graphs, presented at ICML 2023 

• FALCON: Identifying Interpretable Subspaces in Image Representations, presented at ICML 2023 

• Adapting Self-Supervised Representations to Multi-Domain Setups, BMVC 2023

• A Performance-Driven Benchmark for Feature Selection in Tabular Deep Learning, presented at NuerIPs

• Simplifying Neural Network Training Under Class Imbalance, presented at NeurIPs

• Transfer Learning with Deep Tabular Models

• The Disagreement Problem in Faithfulness Metrics, presented at the “XAI in Action” workshop at NeurIPS 2023

• BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial Intelligence, presented at the “Machine Learning in Finance” workshop at KDD 2022

• GALE: Globally Assessing Local Explanations,  presented at the Algebraic and Geometric Learning Workshops 2022, 322-331

• Visual exploration of machine learning model behavior with hierarchical surrogate rule sets, presented at IEEE Transactions on Visualization and Computer Graphics

• Understanding Counterfactual Generation using Maximum Mean Discrepancy, presented at the Proceedings of the Third ACM International Conference on AI in Finance, 44-52

• An Interpretable Deep Classifier for Counterfactual Generation, presented at Proceedings of the Third ACM International Conference on AI in Finance, 36-43

• Calibrate: Interactive analysis of probabilistic model output, presented at IEEE Transactions on Visualization and Computer Graphics 29 (1), 853-863

• SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation, presented at IEEE Computer Graphics and Applications 42 (6), 24-36

• Dynamic Customer Embeddings for Financial Service Applications, presented at the ICML Workshop on Representation Learning in Finance
• SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training
• Latent-CF: A Simple Baseline for Reverse Counterfactual Explanations, presented at the NeurIPs workshop on Fair AI in Finance
• MetaBalance: High-Performance Neural Networks for Class-Imbalanced Data
• Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncoders
• Counterfactual Explanations via Latent Space Projection and Interpolation

• Towards Ground Truth Explainability on Tabular Data, presented at the ICML GRL+ Workshop

• Navigating the Dynamics of Financial Embeddings over Time, presented at the ICML GRL+ Workshop

• Quantifying Challenges in the Application of Graph Representation Learning, published in IEEE

• Machine Learning for Temporal Data in Finance: Challenges and Opportunities, presented at KDD MLF

• Sensitive Data Detection with High-Throughput Neural Network Models for Financial Institutions, presented at the AAAI Workshop on Knowledge Discovery in Finance

• Melody: Generating and Visualizing Machine Learning Model Summary to Understand Data and Classifiers Together

• SUBPLEX: Towards a Better Understanding of Black Box Model Explanations at the Subpopulation Level

• Effects of Model Misspecification on Bayesian Bandits: Case Studies in UX Optimization

• Graph Embeddings at Scale, presented at KDD MLG

• DeepTrax: Embedding Graphs of Financial Transactions, published in IEEE

• Global Explanations of Neural Networks: Mapping the Landscape of Predictions

• On the Interpretability and Evaluation of Graph Representation Learning, presented at the NeurIPs GRL Workshop

• Towards Explainable Deep Learning for Credit Lending: A Case Study

• Mixed Membership Recurrent Neural Networks

If you are interested in helping us research and build the technology that will drive the future of banking, learn more about our machine learning efforts and explore our open tech career opportunities.