This comment is fairly dismissive of GP's experience and intelligence. If you think you can solve a problem just by spitballing it, and GP's team at yelp couldn

This comment is fairly dismissive of GP's experience and intelligence. If you think you can solve a problem just by spitballing it, and GP's team at yelp couldn't figure it out for years, either you're much smarter than everyone on their team or you're underestimating the problem. I worked on an anti-fraud team in a similar role, and I agree with GP that it's a harder problem than people realize. It's easy to come up with a simple metric that would identify the fraud that you observe, but you don't know what false positives it would catch as collateral damage. Like this example that you find wildly implausible: >Two actors, seemingly independent, have each reviewed three businesses only. And they've reviewed the same three businesses, in the same order. And perhaps they each reviewed the first business, then two weeks later, the second business, and then the third. Odds? Getting pretty slim now. A third actor, doing the same? You're stretching the bounds of credulity for sincere reviews. I think it's highly likely that if you used this metric to find patterns, you'd find fraud, but you'd also find lots of legitimate reviews (e.g., friends who go to the same restaurants together, friends who recommend restaurants to each other).