Data in Motion: The New Paradigm in Financial Services | Ness & Confluent Data Streaming Financial Services Webinar

Data is becoming more crucial by the day to the financial services industry. Financial institutions use their expanded data pools and cloud infrastructure to make better loans, improve customer services and offerings, comply with regulations, and detect fraud.

Many companies, however, need to use data to its full potential. The old ‘data at rest model’ is becoming outdated as new ‘data in motion’ systems provide companies the ability to see how data changes as it flows through their organizations, enabling them to offer new applications and services to their customers and build out more efficient operating systems for their business.

In this session, we explore how data in motion platforms empower financial services companies to make better decisions regarding conducting operations and offering services.

You will learn:

  • How financial institutions have incorporated data into their operations
  • The ways a ‘data in motion system differs from the old ‘data at rest’ system
  • How a ‘data in motion’ system helps companies improve their operations and expand their services
  • What the future of data looks like for financial services companies


Jim Zucker is Lead Architect at Ness Financial Services vertical. With over 25 years of experience, Jim is an expert in the digital transformation of risk, trading, credit, derivatives, and compliance systems. Before joining Ness, Jim held senior leadership positions at Salomon Brothers, Citi, and Calypso and was CTO of EZOPS. He is a US Army Veteran.

Duncan Ash is VP of Global Industries at Confluent. He has spent the last 25 years working at the intersection of business and technology to solve complex data and analytics problems, specifically focusing on the financial services industry. Duncan has spearheaded various solutions for low-latency trading, market risk, fraud detection, and wealth management, most recently at Splunk and previously at Qlik, SAS, and Sybase.