Abstract
<abstract> <p>Banks and financial institutions all over the world manage portfolios containing tens of thousands of customers. Not all customers are high credit-worthy, and many possess varying degrees of risk to the Bank or financial institutions that lend money to these customers. Hence assessment of default risk that is calibrated and reflective of actual credit risk is paramount in the field of credit risk management. This paper provides a detailed mathematical framework using the concepts of Binomial distribution and stochastic optimisation, in order to estimate the Probability of Default for credit ratings. The empirical results obtained from the study have been illustrated to have potential application value and perform better compared to other estimation models currently in practise.</p> </abstract>
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