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The Rise of Machine Learning and AI in the Fraud Prevention Industry

13% of organizations today have adapted Machine Learning and AI into their fraud detection protocols. Another 25% of organizations plan on converting from a rule-based system within the next two years.

For decades, rules-based fraud controls have been the primary driver of risk management strategies. Today, with increases in data volumes and advancements in machine learning, rule-based platforms are showing signs of wear. Comparatively, a rules-based fraud management approach often proves to have an higher number of false positives, making it impactful when adopting advanced analytical methods that are proven to be more effective. Rules engines require constant supervision and maintenance, leading to a rules repository that grows more difficult to manage over time as rules are added, removed and edited in response to fraud, which is a moving target.

We are living in a time where cybercriminals are changing and adapting by the minute, and in most instances, machine learning is proving to be more efficient in detecting new fraud patterns. While rule-based systems can work as an effective fraud prevention tool, it is not an ideal option for uncovering complex fraud schemes, at least not until the damage is done. Rules tend to be more reactionary, whereas AI and ML can be more proactive.

To keep up to pace with the constantly evolving fraud scope, a machine learning adaption is important. ML systems are designed to learn and adapt to new user patterns quickly and efficiently without creating friction, or high levels of false positives. This method outweighs that of a rule-based approach when dealing with new fraud patterns that are now developing at a much more rapid rate than ever before.

More companies are flocking to ML-based systems because it serves as an ideal solution for minimizing friction. More often, when a fraud prevention solution is implemented, legitimate transactions are declined, and the checkout process becomes too time-consuming and inefficient for “good” customers. This usually leads to a decline in legitimate sales while the fraud system is tuned to the only rule out the bad actors. With the adaptation of machine learning into a fraud strategy, companies are finding successful outcomes in reducing friction and detecting fraud.

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