The complexity of fraudulent activity, such as payment theft and money laundering, has evolved in proportionate to advancements in technology. Deep learning (DL) dramatically reduces false positives in transactional fraud.

With the availability of large volumes of customer data, such as raw transactions over time (RNN) and transaction summary vectors (RNN and CNN),  firms can train AI neural networks like autoencoders and models to identify irregularities in transactional activity patterns.