Data mining in a credit insurance information system for bank loans risk management in developing countries
by Fouad Jameel Ibrahim Al Azzawi
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 18, No. 3, 2021

Abstract: The task of credit risk insurance in our time is critical since loans are taken by everyone and everywhere and it is quite difficult to accurately estimate the possible losses that are incurred by failing to pay those loans. This work proposes an information system module for the banking system to improve the risk management operation that distributes losses on some fair basis, as well as accepting the maximum number of loan requests. Insuring the risk associated with stumbled loans, the bank will partially or completely shift losses under this contract to the insurance company, thus minimising its own losses. The proposed module could find out for what price the bank can buy such insurance policy. The proposed module also could be a key valuable motivation for different development countries to update their strategy of current insurance market to outsource part of the state's insurance functions to independent insurance industry. Data mining techniques and mathematical induction have been used and successfully implemented this model. An optimal classification solution module for predicting risky loan requests have been successfully employed. New mathematical model has been developed for calculating the cost of insurance policy in crisis economy.

Online publication date: Fri, 23-Apr-2021

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