Insurance fraud brings vast financial loss to insurance companies every year. Data science platforms and software made it possible to detect fraudulent activity, suspicious links, and subtle behavior patterns using multiple techniques.
To make this detection possible the algorithm should be fed with a constant flow of data. Usually, insurance companies use statistical models for efficient fraud detection. These models rely on the previous cases of fraudulent activity and apply sampling method to analyze them. In addition, predictive modeling techniques are applied here, for the analysis and filtering of fraud instances. Identifying links between suspicious activities helps to recognize fraud schemes that were not noticed before.
