Deep Dive: Using Unsupervised ML To Fight Fraud

Many FIs and merchants that have fallen victim to fraud traditionally respond by assessing the damage, pinpointing how the attack succeeded and implementing new measures to prevent similar schemes from happening again. Some businesses are looking for solutions that will help them stop fraud from happening in the first place as criminals become increasingly creative and aggressive in their efforts to steal data and funds. The push for more intelligent anti-fraud solutions comes as the costs of such attacks are reaching new heights. Fraud losses hit $14.7 billion last year, according to the latest DataVisor Fraud Index Report. Account takeover (ATO) fraud proved to be particularly effective, causing $4 billion in losses.