Big data and predictive analysis, statistical modeling, spam detection. Machine learning will learn from the past cases to apply algorithms and facilitate the reply to the different cases. It saves a lot of time if compared to what humans can do in translating random data into useful information. Humans will slow down having more information to analyze, machine learning will increased its speed. It will set up building models and create decision tree to help people to understand the decision process and the rules applied.
In a rules-based environment, machine learning is already today superior to humans. This as machines can faster detect hidden patterns. On the other other hand, not all environments could be mathematically defined so far, so the creative human factor is still relevant, as "intuition" and "instinct" are required. So algorithms and humans together are the winning team to best detect frauds.
Prevention also requires the team, but of course, here the human is in the lead, as humans learn best from other humans. The results from the detection have to be included into prevention, so also here the algorithm is important.