Market conditions might change in the performance of the insurer at the point of claim and might influence how the customer penalizes the insurance company. For example, if an insurance company performs particularly badly at the point of claim., then the customer may well penalize them in some way.
Very interesting metrics are emerging. It is important also that we take into account the policy position. For example, if a customer was declined coverage on the basis of something they may or may not do, then they have an obligation to declare that insurance had been declined to all future insurers.
Fraud analytics help predict policy holder fraud, but there are also quite big issues around supplier fraud, which is generally referred to as claims leakages. Predictive analytics has a major part to play in how companies manage their supply chain and optimize it, ensuring insurers pay no more than the absolute requirement.
Predictive analytics can guess these outputs that are being generated either around historical data or the actual data that is being given to the call center agent. The technology guess these outputs back to the agent in real time to make a difference at that inflection point based on APIs integrated within their system to ensure that the scoring mechanisms are relayed back to the contact management system in a real time fashion. They can be generally relatively easy to develop, and it just takes the right people and the proper building blocks to be able to get the output at one end, to the presentation of those outputs and on the screen for the agent on the other end.
To learn more about how predictive analytics can be used in the insurance industry, see this article.