Organizations in various industries are under increased pressure to identify subtle patterns that indicate fraud before their customers are affected by a variety of threats including account takeover, identity theft, and application fraud.
Many companies are turning to Machine Learning (ML) to help them stay one step ahead of the next generation of cybercriminals. While most organizations recognize ML analytics will help them root out various types of fraud, many of them remain in the early stages of implementing this.
Download this report to understand:
- The top pain points and types of fraud garnering the highest priority for investment over the next couple of years;
- Current and planned implementation of Machine Learning and their use of enabling platforms by these institutions;
- Which KPIs are most commonly used, modeling techniques, and the types of data inputs favored by mature organizations.