Rajagopal (1996) made an attempt to overview the bank’s risk management and suggests a model for pricing the products based on credit risk assessment of the borrowers. He concluded that good risk management is good banking, which ultimately leads to profitable survival of the institution. A proper approach to risk identification, measurement and control will safeguard the interests of banking institution in long run.
Froot and Stein (1998) found that credit risk management through active loan purchase and sales activity affects banks’ investments in risky loans. Banks that purchase and sell loans hold more risky loans (Credit Risk and Loss loans and commercial real estate loans) as a percentage of the balance sheet than other banks. Again, these results are especially striking because banks that manage their credit risk (by buying and selling loans) hold more risky loans than banks that merely sell loans (but don’t buy them) or banks that merely buy loans (but don’t sell them).
Treacy and Carey (1998) examined the credit risk rating mechanism at US Banks. The paper highlighted the architecture of Bank Internal Rating System and Operating Design of rating system and made a comparison of bank system relative to the rating agency system. They concluded that banks internal rating system helps in managing credit risk, profitability analysis and product pricing.
Duffee and Zhou (1999) model the effects on banks due to the introduction of a market for credit derivatives; particularly, credit-default swaps. Their paper examined that a bank can use swaps to temporarily transfer credit risks of their loans to others, reducing the likelihood that defaulting loans trigger the bank’s financial distress. They concluded that the introduction of a credit derivatives market is not desirable because it can cause other markets for loan risk-sharing to break down.
Ferguson (2001) analyzed the models and judgments related to credit risk management. The author concluded that proper risk modelling provides a formal systematic and disciplined way for firms to measure changes in the riskiness of their portfolio and help them in designing proper strategic framework for managing changes in their risk.
Bagchi (2003) examined the credit risk management in banks. He examined risk identification, risk measurement, risk monitoring, risk control and risk audit as basic considerations for credit risk management. The author concluded that proper credit risk architecture, policies and framework of credit risk management, credit rating system, monitoring and control contributes in success of credit risk management system.
Muninarayanappa and Nirmala (2004) outlined the concept of credit risk management in banks. They highlighted the objectives and factors that determine the direction of bank’s policies on credit risk management. The challenges related to internal and external factors in credit risk management are also highlighted. They concluded that success of credit risk management require maintenance of proper credit risk environment, credit strategy and policies. Thus the ultimate aim should be to protect and improve the loan quality.
Louberge and Schlesinger (2005) aim to propose a new method for credit risk allocation among economic agents. Their paper considers a pool of bank loans subject to credit risk and develops a method for decomposing the credit risk into idiosyncratic and systematic components. The paper shows how financial contracts might be redesigned to allow for banks to manage the idiosyncratic component for their own account, while allowing systematic component to be retained, passed off to capital market or shared with borrower.
Bandyopadhyay (2006) aims at developing an early warning signal model for predicting corporate default in emerging market economy like India. He also presented the method for directly estimating probability of default using financial and non-financial variable. For predicting corporate bond default multiple discriminant analysis is used and logistic regressions model is employed for estimating Probability of Default (PD).
The author concluded that by using ‘Z’ score model, banks and investors in emerging markets like India can get early warning signals about the firm’s solvency status and reassess the magnitude of default premium they require on low grade securities. The PD estimate from logistic analysis would help banks to estimate credit risk capital and set corporate pricing on a risk adjusted return basis. This model has high classification power of sample and high prediction power in terms of its ability to detect bad firm in sample.
On making the review of the previously conducted studies, it is clear that majority of the studies that focus on credit risk management practices in banks provide conceptual framework. Hence, empirical studies on credit risk framework of banks in India are yet to be effected. Moreover, no study has made a size-wise and sector-wise comparison of the credit risk management among banks in India. The present study is an attempt to address the above issues pertaining to the credit risk management framework of banks in India.