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H2O for Real Time Fraud Detection
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Finance & Insurance
- Telecommunications
Applicable Functions
- Sales & Marketing
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
The Challenge
Organizations responsible for fraud prevention are facing a host of challenges at the transaction, account, and network-level to detect fraudulent behavior and suspicious activities. Fraudulent transactions are rare, but costly if they aren’t detected. In the credit card business, for example, third-party fraud accounts for roughly 4 out of every 10,000 transactions. Modeling rare events is difficult, like finding a needle in a haystack. For best results, gather as much data as possible, and use the most advanced techniques available.
About The Customer
The article mentions several companies across industries that rely on H2O for scalable machine learning to detect fraud. These include a U.S.-based payment systems company that handles billions of dollars in payments each month, a global insurance company, a multinational telecommunications provider, and a leading U.S. credit card issuer. These companies turn to H2O because it is highly scalable and delivers superior performance; offers flexible deployment options; works seamlessly in a large scale data sets; and offers a simple interface.
The Solution
The solution to the challenge of fraud detection is the use of H2O's Deep Learning technology. This technology uses artificial neural networks (ANN) with multiple hidden layers, also called deep neural networks (DNN). Deep Learning is a rapidly growing discipline that models high-level patterns in data as complex multi-layered networks. Because it is the most general way to model a problem, Deep Learning can solve the most challenging prediction problems. The U.S.-based payment systems company uses H2O Deep Learning for real-time fraud detection. Working with a dataset of 160 million records and 1,500 features, the company’s data scientists use a test-and-learn approach to find the best-performing predictive model.
Operational Impact
Quantitative Benefit
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