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Leveraging Large Scale Data Sets
Technology Category
- Analytics & Modeling - Big Data Analytics
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Business Operation
Use Cases
- Fraud Detection
Services
- Data Science Services
The Challenge
The insurance company was facing a significant challenge with claims fraud, which is estimated to cost the industry $80 billion annually in the United States alone. The existing process for detecting suspicious claims was entirely manual, relying on the judgment and experience of professional claims examiners. This approach was not scalable for a growing business and was time-consuming due to the need to pull information from multiple systems. The company had consolidated data from various sources into a Hadoop data store, which included a mix of structured and unstructured data. However, Hadoop lacked the capability for sophisticated predictive analytics, and extracting the data to an analytic server was time-consuming.
About The Customer
The customer is a global insurance company that is seeking to detect and prevent claims fraud in its Workman's Compensation business. The company has a growing business and needs a more automated approach to handle the increasing volume of claims. The company has data scientists who use R for advanced analytics, but they were facing challenges in scaling their analytics to handle Hadoop-level data volumes. The company needed a solution that could provide sophisticated predictive analytics on large datasets and enable rapid deployment of fraud detection models.
The Solution
The company implemented H2O, an open-source machine learning platform, to address its challenges. H2O was co-located in the company's Hadoop cluster, allowing analysts to discover insights in the data without extracting it or taking samples. Data scientists could interact with H2O using R, but all of the work was performed in H2O where it was deployed, in the Hadoop cluster. This approach enabled the company to leverage its large datasets for predictive analytics. When an analytics project was completed, H2O exported predictive models as Plain Old Java Objects (POJOs). These POJOs could run anywhere in the organization that Java runs, enabling rapid deployment of fraud detection models in various systems.
Operational Impact
Quantitative Benefit
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