Machine-based learning anomaly detection

Machine learning is a useful type of artificial intelligence. Machine-based learning anomaly detection looks at all the numbers. It is able to learn without predefined decision rules. Machine learning constructs its own decision tree based on meta-tagged data, e.g., “red flag” or “not,” to decide how “red flag” transactions are related. Machine learning applies the learned logic to new data and is remarkably adept at making the right decision. Machine learning’s ability to learn from a complex array of data and not just a few variables leads to greater accuracy.

Traditional analytics rely on rule-based methods to detect anomalies. These decision rules follow simple Boolean logic: if a vendor address matches an employee address and its wire transfer account matches the employee bank account, then it is likely a fictitious vendor. 

Unsupervised Learning

Another type of machine learning called “unsupervised learning,” constructs decision trees without meta-tagged data; it identifies patterns of interest and anomalies using its own decision-making criteria. This allows fraud examiners to find new forms of fraud not previously detected or codified into rules-based methods. Both supervised and unsupervised machine learning systems are self-refining, in that they become more accurate as more data is encountered.