Fraudulent Product Reviews

Imagine we're tasked with identifying fraudulent reviews on our products. Historical data shows that 98% of the reviews are genuine, while 2% are fraudulent. When a review is fraudulent, our machine learning algorithm has a 95% likelihood of correctly flagging it as such. Conversely, if a review is genuine, there is a 90% probability that the algorithm will correctly identify it as legitimate. What is the probability that a review is actually fraudulent when the algorithm flags it as fake?