Capital One Uses H2O for Mobile Transaction Forecasting and Anomaly Detection
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- H2O
- Sparkling Water
- Capital One Mobile App
Tech Stack
- Python
- Scala
- Apache Spark
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Customer Satisfaction
- Productivity Improvements
Technology Category
- Analytics & Modeling - Real Time Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
Use Cases
- Predictive Maintenance
- Real-Time Location System (RTLS)
Services
- Data Science Services
About The Customer
Capital One is a well-known American bank that values the power of information and technology to deliver highly customized financial products to consumers and business customers. The bank is recognized for its innovative approach, particularly in making banking secure, convenient, and user-friendly. Capital One's mobile app is rapidly becoming the preferred channel for customers to perform transactions, with up to 5,000 customers logging into the platform every minute. The bank has a dedicated technology operations group that monitors all of the bank's critical systems and platforms to ensure the mobile app is up and running at all times.
The Challenge
Capital One's mobile app is a popular platform for customers to perform transactions, with up to 5,000 customers logging in every minute. This high volume of usage means that even small outages need to be identified and resolved quickly to prevent service disruptions. The bank's technology operations group monitors all critical systems and platforms and sets up alerts based on company policies. However, setting up alerts for volume anomalies, such as a drop or spike in transaction volume, proved to be a challenge. Traditional methods of calculating volume anomalies were not scalable and required a lot of coding, development, and oversight to manage.
The Solution
To address the challenge of detecting and alerting on volume anomalies, Capital One's data scientists turned to platform engineering and open source technology. They used Sparkling Water, a tool that combines the fast, scalable machine learning algorithms of H2O with the capabilities of Apache Spark, for rapid prototyping and ad-hoc experimentation. H2O's advanced capabilities for in-memory processing matched Capital One's big data environment needs, and its support for Python, Spark, and Scala enabled a unified coding pipeline for the bank's data experts. The team started with the Generalized Linear Model (GLM), but found that the Gradient Boosting Machine (GBM) provided greater flexibility. GBM allowed the team to account for trends and seasonality in their mobile application usage, and to filter and exclude data as needed. To productize the solution, the team built a scalable and repeatable pipeline using cloud-based, open source platforms and tools.
Operational Impact
Quantitative Benefit
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