Over the years the significant increase in financial losses comparison to the increase in premiums also draws attention in fire insurance. Although some abuses can be detected, the real rate is estimated as %1.3 of the claims.
The most common types of abuse,
Issued policies after damage or losses occur the claim or possibility of claim will be detected.
The goal is detecting anomalies just prior to possible damage from weather conditions or other causes.
It is possible to prevent abuse by detecting anomalies in fire insurance at the moment of production. Using machine learning algorithms, the anomalies are detected if there is a possibility of damage due to weather conditions or a different reason before any damage has actually occurred.
The main achievements of prevent to productions with high risk of abuse,
To avoid unnecessary claims paid by detecting abuse
To achieve profitable portfolios by providing to working with good portfolio of agencies
To recognise the portfolio of agencies and the insured