Auditing Vehicles Claims Using Neural Networks

Nowadays, fraud is a major enemy of insurance companies. For the total of R$ 28 billion in claims, an estimated R$ 7 billions must be fraud. Since the claims represent 59.9% of the premiums paid by the companies, frauds represent 15.0% of them. Therefore, great caution is to be taken in order to detect the frauds and not to pay for these claims. One of the most important detection tools is the audit. However, because it is an expensive service, it is not possible to audit all claims. Based on this caution, the goal of this work is to test some strategies of how to select claims to be audited. The strategies used become more complex, from the first to the fifth, starting with simple thoughts for the first three strategies, and the utilization of logistic models and neural network to estimate the probability of a fraud detection on the fourth and fifth strategies, respectively.