Default Probability Assessment for Project Finance Bank Loans and Basel Regulations: Searching for a New Paradigm

Abstract

Syndicated loan data from India suggest that despite the pressure on margins and volumes, available “bankable” deals in infrastructure are still strongly contested by commercial lenders. With the implementation of Ind AS accounting norms (IFRS) for commercial banks, the expected credit loss–based provisions have to be set by the bank based on internally estimated probability of default (PD) for different loan portfolios. This change may lead to heavier risk-weighted capital requirements for banks, thus impacting the project finance business. Basel III norms and subsequent discussion papers propose a revised standardized approach doing away with internal modeling approaches and introduction of standardized output floors for specialized lending, including project finance. In this light, the authors present a cash flow simulation model to address the issue of PD estimation by simulating key risk factors. This method may be useful as each project and each sector is unique and so are the risks associated with it. Thus, the authors argue that the use of a simulation model will result in better assessment and monitoring of credit risk than conventional assessment methods, leading to lower default rates and therefore lower capital charge. The authors then suggest some new rules of engagement for project finance lenders to stay relevant in the changing regulatory scenario.

TOPICS: Simulations, project finance, credit risk management

Key Findings

• • Probability of default estimation and subsequent impact on credit risk capital is important for project finance lenders in India especially after emerging Basel III reforms and subsequent discussion papers on credit risk capital. (Informally called as Basel IV)

• • The authors suggest a cash flow simulation model developed using risk parameters “specific” to each project. The application of the model is shown on a road project and it calculates cumulative default risk probability of the project.

• • The paper argues that this model will result in better assessment and monitoring of credit risk. Authors also suggest new rules for engagement for the project finance lenders in the emerging regulatory scenario.