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Improve Financial Services with Event Stream Data

Streaming data will be transformative to the future of financial services organizations. It can provide the critical, timely information needed to deliver world-class customer service, detect and prevent fraud, tie together disparate legacy systems or power new services and sources of revenue. In the past, most financial services systems had a transaction-oriented approach; each interaction with a customer was a single transaction or interaction (for example, a bank deposit). Today, financial services organizations—and all organizations—need to understand the entirety of current and historical interactions with a customer to meet their expectations. In addition to a deposit or loan transaction with a core operational system, the complete picture might include mobile banking, website interactions, calls to customer service and social media posts about your products or services.

Our research shows that nearly all financial services organizations (97%) consider it important to accelerate 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 involving 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 revenueproducing 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.