How Do Anti-fraud Systems Work?

Systems working with anti-fraud include four main tasks: monitoring, detection, decision-making, and intelligent learning. A large number of different online filters are used in the process – validators, geographic filters, authorization limits, parameter matching filters, stop lists. The more complex and risky the domain, the more different filters are used for protection.

Key risk management software technologies and capabilities used to solve these problems are as follows:

Calculation of statistical parameters 

The very tool that can detect deviations from the norm and recognize a suspicious operation.

Text analytics 

Categorizes information, instantly searches through it, and extracts entities.

Gap testing 

Identifies redundant or missing components in sequential data.

Network Analytics 

Such systems detect patterns and analyze them so that anti-fraud platforms like https://covery.ai/ can process similar situations faster in the future.

Machine learning 

Builds specific patterns based on historical data and generates predictions in real-time. This process is supervised by a specialist.

Although risk management software serves the same purpose, the methods of achieving it may differ significantly. For this reason, the classification of such fraud detection software has been developed. Such fraud detection software classification conditionally divides the risk management software into three types of solutions:

• A set of measures for detecting anomalies in transactions, identifying signs of fraud.
• A set of measures to perform highly specialized tasks.
• A set of measures for detecting signs of third-party fraud. 

In controversial situations, any fraud detection software leaves the final word to the analyst. The analyst reviews the incident and assigns characteristics ranging from “definitely legitimate” to “definitely fraudulent.”