Various type of transactional data in combination with customer profiles, industry trends and text data are of tremendous value to companies using it to perform advanced risk calculations and fraud predictions using Machine Learning.
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?