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No to Frauds by Implementing ML
Malware and fraudulent attacks are on the rise as the amount of data has exponentially risen throughout the web. Only way to tackle them is through the deployment of ML algorithms.
FREMONT, CA: Online fraud is not as simple as people think, as fraudsters adapt quickly to changing techniques. Their sophisticated toolbox now includes ML algorithms and bots to commit fraud at scale. The legacy move toward fraud-fighting entails static systems like rule engines. Rule engines are stiff and hard-coded and once implemented, treat fraud as though it is either black or white.
Many industries have realized that rule-based engines are inadequate as they have proved to be inflexible and are not capable of scaling. Those businesses, which were earlier nonchalant and ignorant to fraudulent behavior, have shifted to ML in keeping with changing times. This is crucial in an era when new types of threats―account takeover, fake content―arise almost every day. The rules system can only be applied to a single vector of fraudulence, whereas Machine Learning (ML) is successful across multiple channels.
Businesses launch a new product or an application first and worry about fraud detection and prevention later, so risk assessment teams are often left to pick up the after-effects after an attack that has already taken place. To make the circumstances even worse, fraudsters have begun to adopt advanced technologies such as ML and bots, and businesses have to fight fire with fire. Companies that do not succeed to focus on fraud alleviation rather than avoidance gets left behind in the race.
When appropriately managed, ML can clearly distinguish between genuine and fraudulent behavior while acclimatizing over time to new or previously unnoticed tactics. The facets of digital fraud prevention are quite complex as the ML programs need to interpret patterns in the dataset and identify between normal and abnormal behavior. The computational process requires thousands of calculations to be performed precisely within milliseconds. Without a proper perception of the domain, as well as fraud-specific techniques, enterprises can employ ML algorithms to aid them to prevent malware attacks and fraudulent assaults.