The rapidly changing threat landscape is making it easier for malicious actors to commit financial fraud. Worse, organized crime rings are taking advantage of COVID-19 to drive up fraud losses for financial institutions. It is becoming increasingly difficult to identify and stop fraud before customers are affected.
To combat the onslaught of attacks, fraud detection and prevention systems need the ability to do real-time fraud analysis through analytics. Anti-fraud systems must be able to analyze a broad range of data, events, and context, both in real time and historically, to make instantaneous decisions about fraud.
To help banking executives better understand the value of a risk analytics system driven by machine learning, this white paper explains continuous fraud monitoring and dynamic risk assessment in the context of the top use cases in banking.
Read this paper to learn:
Why risk analysis is key to a great user experience and reducing false positives
How to help prevent account takeover fraud, mobile attacks, and new account fraud based on stolen identities, synthetic identities, and mule activity
How to leverage a modern fraud platform with the latest machine learning, mobile device data collectors, and adaptive authentication
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