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. Organisations 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 organisations stop potential fraud while minimising human intervention, saving fraud department valuable resources, and decreasing business costs.
Download this complimentary executive brief to discover:
- The Current State of Fraud
- How Data Can Help Identify Fraud Patterns
- Why Organisations Need a Data-first Approach to Combat Fraud
- 3 Essential Considerations When Assessing Fraud Detection Solutions