H2O.ai > 实例探究 > H2O for Real Time Fraud Detection

H2O for Real Time Fraud Detection

公司规模

1,000+

地区

America

国家

United States

产品

H2O Deep Learning

技术栈

Artificial Neural Networks (ANN)
Deep Neural Networks (DNN)

实施规模

Enterprise-wide Deployment

影响指标

Cost Savings
Digital Expertise

技术

分析与建模 - 机器学习

适用行业

金融与保险
电信

适用功能

商业运营
销售与市场营销

用例

欺诈识别

服务

数据科学服务

关于客户

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.

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挑战

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.

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解决方案

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.

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运营影响

H2O’s distributed in-memory architecture enables the company to run tests quickly and build the most accurate predictive models.
The use of H2O Deep Learning technology has allowed the company to effectively detect and prevent fraudulent transactions.

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数量效益