When it comes to insurance companies and financial institutions all around, fraud detection is one of their biggest concerns: frauds are not only detrimental to the stability of the insurance company, but they also have a long term impact on each policyholder. Fraud risks can make prices increase for loyal customers, and make the payment and review of legitimate claims much more time consuming.
Over the years there have been countless attempts to try and lower fraud risk, by improving data security and creating models that predict anomalies. However, this hasn’t stopped criminals.
Thankfully, deep learning algorithms can detect fraudulent transactions with clarity: by analyzing various data points, comparing each transaction or complain to the client’s history, looking for unusual activities that would likely go unnoticed to the human eyes.
Not only can AI catch fraudulent activities with improved accuracy, but it can also adapt to ever changing fraud patterns.
Axyon uses deep learning techniques for fraud detection by taking the insurance company’s data, anonymize and normalize it to find anomalies such as suspicious transactions, purchases made in different countries during the same day, a certain amount of car accident claims from the same individual.
The algorithm then learns from these anomalies to develop predictions on future behaviors, allowing insurances to easily find out fraudulent customers and deny them unfair payments.