Frost & Sullivan

Fighting Fraud Factually: Determining Danger from Data

The COVID-19 pandemic had undeniably exacerbated the volume of fraud these past 2 years. Currently, many existing solutions lack the depth of detection and scalability required to tackle the modern fraud landscape. Organizations need to adopt data-first approaches when combating fraud. Data is one of the most crucial elements in insight generation; interpreting and expressing data in multiple ways can help uncover oversights and anticipate problems before they occur.

Machine learning can process data using built-in algorithms and rulebooks to perform automatic detection and alerting. This helps organizations stop potential fraud while minimizing human intervention, saving fraud department valuable resources, and decreasing business costs.

Supply Chain
Download this complimentary executive brief to discover:

  • The Current State of Fraud
  • How Data Can Help Identify Fraud Patterns
  • Why Organizations Need a Data-first Approach to Combat Fraud
  • 3 Essential Considerations When Assessing Fraud Detection Solutions

Download the Complimentary Executive Brief

"*" indicates required fields


This executive brief is co-hosted in partnership with Frost & Sullivan and Splunk. As a result, both Splunk and Frost & Sullivan are collecting your personal data when you submit such information as part of the registration process above. For more information on each party’s privacy practices, please see: Splunk Privacy Notice | Frost & Sullivan Privacy Notice.

Splunk may use your personal data to inform you about its products, services and events.