  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
credit risk is paramount in the field of credit risk management. This paper
discusses the use of Bayesian principles and simulation-techniques to estimate
and calibrate the default probability of credit ratings. The methodology is a
two-phase approach where, in the first phase, a posterior density of default
rate parameter is estimated based the default history data. In the second phase
of the approach, an estimate of true default rate parameter is obtained through
simulations
