Picture this: A thriving e-commerce platform faces a constant battle against fake reviews that skew product ratings and mislead customers. In response, the company employs cutting-edge algorithms to detect and prevent fraudulent activities. Solutions like these are crucial in the modern digital landscape, safeguarding businesses from financial losses and ensuring a seamless consumer experience.
The industry has relied on rules-based systems to detect fraud for decades. They remain a vital tool in scenarios where continuous collecting of a training sample is challenging, as retraining methods and metrics can be difficult. However, machine learning outperforms rules-based systems in detecting and identifying attacks when an ongoing training sample is available.
With advancements in machine learning, fraud detection systems have become more efficient, accurate, and adaptable. In this article, I will review several ML methods for preventing fraudulent activities and discuss their weaknesses and advantages.
