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Insurers Use Event Stream Data to Transform Services

Streaming data is transforming how insurance companies provide their services. It can offer critical information needed to underwrite risks with better information, detect and prevent fraudulent claims, deliver world-class customer service and tie together disparate internal legacy systems. In the past most insurance processing systems had a transaction-oriented approach. Each interaction with a customer was a single transaction or interaction, like issuing a new policy or processing a claim. With the advent of streaming data, insurance companies have faster access to more information so they can assess customers more holistically as they evaluate risks and process claims against the policies insuring those risks. With streaming data, insurers can operate more efficiently, underwrite more accurately, prevent or reduce losses and offer new products and services.

Our research shows that nearly all financial services organizations (97%)—a category including insurance companies—consider it important to speed the flow of information and improve the responsiveness of the organization. Even just a few years ago, capturing and evaluating this information quickly was much more challenging, but with the advent of streaming data technologies that capture and process large volumes of data in real-time, organizations can quickly turn events into valuable business outcomes in the form of new products and services or revenue. Our research of a broad cross section of financial services organizations shows that more than half of those organizations (55%) are using their event streams to identify organizational or revenue-producing improvements. And nearly half (45%) are developing new products or services.


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About the Author


David Menninger

SVP and Research Director
Ventana Research

David Menninger is responsible for the overall direction of research on data and analytics technologies at Ventana Research. He covers major areas including artificial learning and machine learning, big data, business intelligence, collaboration, data science and information management along with the additional specific research categories including blockchain, data governance, data lakes, data preparation, embedded analytics, natural language processing (NLP) and IoT.