Organizations responsible for fraud prevention understand the power of analytics: very small differences in the ability to predict fraud can have a major impact on losses. There are a host of challenges that need to be addressed at the transaction, account and network-level to detect fraudulent behavior and suspicious activities. It is estimated that fraud costs at least $80 billion a year across all lines of insurance 1 . That is why companies at risk of fraud invest in machine learning as a preemptive approach to tackling fraud. Companies across industries rely on H2O for scalable machine learning to detect fraud. The table below shows some examples of H2O users and the types of fraud they seek to prevent. These companies turn to H2O because it is highly scalable and delivers super

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