Investigation of the possibilities for detecting motor insurance fraud using fuzzy inference
Abstract
Insurance fraud occurs when a claimant tries to gain financial benefits by making an unfounded, unjustified claim. These cases can cause serious economic damage. Consequently, fraud detection is a key issue at present, especially in the motor insurance market. Soft computing techniques have emerged in recent decades to model and support problem detection. In the framework of this paper, a theoretical Mamdani-type fuzzy inference system is presented for predicting the assumed probability of insurance fraud using easily determinable parameters: the insurance payout, in Ft; the age of the not faulty participant vehicle, in years; and the payment period of the insurance contract.
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