Risk & Fraud

Companies can detect anomalies in their data to predict fraudulent behavior using Machine Learning. Today these methods are used broadly within financial services (eg. credit card transactions) as well as telecom companies (eg. network analytics) to enable preventive actions. As fraud and hacking becomes more common, these capabilities are increasingly being adopted by companies throughout many industries. 

Risk analytics is highly prioritized within finance and insurance, to better understand risk factors within their various business areas. For example, banks have a high priority to utilize information correctly and efficiently in their credit modeling to comply with regulations and ensure that they maximize customer loans, thus optimizing their revenues.

Similarly insurance companies need to understand the risk behind each customer, in order to optimize pricing on their services. In recent days the discussion of ethical use of analytics parameters are on the rise. For example; is it okay to use the geographical origin (think segregation) of a customer to determine the price of an insurance?