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As fraudsters find new ways to exploit technology, businesses must take a holistic approach to detect and prevent theft or malicious intrusions. Information shared across the organization, and the people responsible for managing the security of that data must work directly with those responsible for the company’s enterprise-level fraud prevention. The technologies used must help in identifying any ongoing illicit activities while also identifying and limiting possible exploitable gaps in the future.
Data analytics is a foundational part of a comprehensive fraud detection program, and the tools used should excel at connecting raw data to potentially fraudulent activity. Businesses that employ analytics are better at identifying discrepancies and limiting their potential for losses from activities like employee theft – an unfortunately common, but quite easily preventable problem, given the right technology.
A global 2019 study conducted by the ACFE (Association of Certified Fraud Examiners) reports that 26% of businesses have begun using biometrics as part of their anti-fraud programs. That number is expected to rise each year as more than half (55%) of respondents plan to spend more money on their anti-fraud technology during the next 24 months.
Looking forward, by 2021, about half (52%) of organizations anticipate employing predictive analytics and data modeling as part of their fraud detection and prevention strategies. While it has been traditionally used to manage credit risk, predictive modeling is helping investigators stay ahead of some of the most sophisticated fraudsters.