Financial Institutions (FIs), perhaps more than any other industry, are under greater pressure to identify the subtle patterns that indicate fraud before their customers are affected by a variety of threats including Account Take Over, Identity Theft, and Application Fraud. Which is why so many of them are turning to Machine Learning (ML) to help them stay one step ahead of the next generation of cybercriminals. While most FI's recognize the promise that ML analytics will have in helping them root out a variety of fraud types, many of them remain in the early stages of this journey.
To better understand how FI's are thinking about leveraging ML to combat fraud in their organization, iovation partnered with research firm Aite Group who surveyed 28 senior fraud and data analytics executives from 20 large North American financial institutions in September and October of 2017. Download this free 25 page 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 use 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
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