BAD DEBTS AS A GLOBAL PROBLEM IN BANKING SECTOR

Economic crisis caused that the credit risk has become a global major issue in the last few decades. However the need for credit risk measurement is here since people started borrowing any goods they needed. The most common subject of debt is money and the lender is a bank. When the bank lends some amount of money to a debtor, the debtor is obliged to return the full amount plus some compensation for borrowing the money. There always exists a danger that the debtor will fail to fulfill this obligation and the lender will lose some of his money, so he should be aware of the risk. Therefore he needs to estimate the probability distribution of the credit loss for the entire portfolio of loans. Although the corporate credit risk literature contains of the corporate bonds credit risk change modelling, there is fewer analysis of the credit risk for portfolios of consumer loans. Using of a stochastic process based on a transition probability - Markov chain is one of the approaches to this problem. The aim of our paper is to divide portfolio of loans into groups (classes) according to the time delay in payment, to calculate the probability of a default for each group using the fundamental matrix of the Markov chain, and to appoint present value of future bad debts.