Yes, it will reduce false positives. Traditional rule-based approaches to fraud detection, typically used in the insurance, banking, and medical aid industries, often result in many valid claims being flagged as suspicious, requiring unnecessary investigation. These false positive flags result in both unnecessary expenses being incurred, delays in paying valid claims and potential damage to relationships with clients. Our machine learning fraud models aim to identify fraudulent claims more accurately, greatly reducing the number of investigations performed without materially reducing the number of claims rejected due to fraud. The models also make it easy for XTND to quickly figure out which claims are very unlikely to be fraud. This means that these claims can be paid as soon as possible without causing any more trouble for clients.