Unfortunately, it is difficult for the insurer to accurately make such an assessment. This analysis remains easy for simple products such as those pertaining to motor risk. In this line of business, the estimation of the cost of the risk can be largely or fully automated.
However, in certain classes or categories such as the health activity, risks are more complex to assess. To resolve such cases, recourse to a third-party expert is required to overcome information asymmetry, typically in favour of the insured.
Thanks to their capacity to collect and analyze data, new technologies can usefully perform this expert work and reduce the issue of information asymmetry encountered by insurers. The identification of insured risks and their interpretation is done through data collection by the IoT.
Consequently, the analysis technologies offered by the association of the IoT and the Big Data [4] provide new methods of risk and claims assessment and management. In summary, this accuracy characterizing risk selection comes from:
- The exploitation of data collected from connected objects,
- The use of Big Data that enriches decision making at the underwriting level,
- Precise and sophisticated risk measurement, particularly during disaster risk modeling processes,
- Digitalization that makes data easy to analyze and allows for an adapted product supply.
By using a wider variety of analysis techniques, insurers can accurately assess their clients' requirements and target them with products and services tailored to their needs.
This approach has the advantage of providing underwriting assistance while reducing the risk of fraud.
However, there are limits to the collection and use of customer data. While increased digital interaction with consumers makes it easier to capture, store and manage large amounts of information, the problem of invasion of privacy is a barrier to unrestrained use of Big Data.
Protecting policyholders' data from hacking, where fraudulent use is essential.