Credit Risk Models for Managing Bank's Agricultural Loan Portfolio

Project Report on Credit Risk Models for Managing Bank’s Agricultural Loan Portfolio

PhD Thesis on Credit Risk Models for Managing Bank’s Agricultural Loan Portfolio

A rapid growth in the rural economy and within that of agriculture in India is highly feasible provided key ingredients such as adequate supply of credit and the availability of the tools for the management of risks that agriculture is exposed to are religiously followed.

Farm level surveys have indicated that the most frequently cited risks are price, crop/weather and health. These risks among others could lower farmers’ anticipated income and have negative effects on their   standard of living, ability to provide for themselves and their families, ability to build capital and hence, their inherent creditworthiness. In order to sustain credit disbursement to agricultural farmers, public sector banks in India should be able to ease risk arising from credit exposure in agriculture. A good credit risk assessment assists banks and financial institutions in taking right and informed credit decisions, proper loan pricing,  determining the amount of loans to be disbursed, reducing the chance of default and finally, increasing the  possibility of debt recovery. Credit risk assessment involves determining the financial strength of the borrowers, estimating the probability of default and reducing the risk of non-payment to an acceptable level. In general, credit evaluations in public sector banks in India are based on the credit officer’s subjective  assessment of judgmental assessment techniques. However, this technique seems to be inefficient,  inconsistent and above all non-uniform because of subjectivity in choice of risk weights and scores, and hence, suboptimal. Rather, customized credit scoring model based on internal data of a bank has the potential of reducing the variability of credit decisions and imparting efficiencies to credit risk assessment process.

The New Basel Accord, scheduled to be implemented by the end of 2009, does not include any special treatment for agricultural lending. Basel II implies that large agricultural loans would be treated as corporate loans and small agricultural loans as retail loans. The regulators, however, need to take into account the particular characteristics of farm loans when setting capital charges for organizations involved in agricultural lending (Barry, 2001). Farm businesses are characterized by cyclical performance, seasonal production patterns, high capital intensity, leasing of farmland, participation in government programs, and annual payments of real estate loans. Because of these characteristics, losses in agricultural lending may not be frequent, but could be large due to high correlations among farm performances. At the same time, high capital intensity, especially involving farmland, offers relatively strong collateral positions, thus mitigating the severity of default when default problems do arise.

Katchova and Barry (2005) developed models for quantifying credit risk in agricultural lending. They calculated probabilities of default, loss given default, portfolio risk, and correlations using data from farm businesses. The authors showed that the calculated expected and unexpected losses under Basel II critically depend on the credit quality of the loan portfolio and the correlations among farm performances. These analyses of portfolio credit risk could be further enhanced if segmented according to primary commodity and geographical location. Agricultural lenders could then adopt similar models to quantify credit risk, a key component in the calibration of minimum capital requirements. Ramaswami et al. (2004) discussed the issue of risk management in agriculture in a comprehensive manner. Some of the risk-reducing strategies at the farmers’ level have been crop diversification, intercropping, farm fragmentation and non-farm income.

A credit risk model suitable for agricultural loan is developed based on the sample data obtained from a large Indian public sector bank. The model incorporates basic characteristics of the borrowers and various risk parameters that significantly influence the borrowers’ creditworthiness. Such a model would enable the bank to identify the key risk parameters in agricultural loan that would help the lending officers to take decisions and manage the loan portfolio in a better way to minimize credit losses. The New Basel Capital Accord (Basel II) provides added emphasis to the development of portfolio credit risk models. An important regulatory change in Basel II is the differentiated treatment in measuring capital requirements for corporate exposures and retail exposures. Basel II allows agricultural loans to be categorized and treated as the retail

exposures. However, portfolio credit risk models for agricultural loans are still in their infancy. Most portfolio credit risk models being used have been developed for corporate exposures, and are not generally applicable to agricultural loan portfolio. The objective of this study is to develop a credit risk model for agricultural loan portfolios. The model developed in this study reflects characteristics of the agricultural sector, loans and borrowers and is designed to be consistent with Basel II including the consideration given to forecasting accuracy and model applicability. This study conceptualizes a theory of loan default for farm borrowers. A theoretical model is developed based on the default theory with several assumptions to simplify the model.

While modeling credit risk for agricultural loans, one must account for the attributes of agricultural sector and its borrowers. The performance of the sector is also influenced by economic cycles and is highly correlated to farm typology, commodity, and geographical location. Credit risk of agricultural loans is closely related to a farm’s net cash flows like other retail loan categories. However, these cash flows exhibit annual cycles. Banks catering to agriculture sector need a unique credit risk model for their loan portfolio that captures these and other characteristics unique to agriculture. The objective of this study is to develop a credit risk model for an agricultural loan portfolio in India. This model takes into account the characteristics of the agricultural sector, attributes of agricultural loans and borrowers, and restrictions faced by commercial banks. The proposed model is also consistent with Basel II including consideration given in forecasting accuracy and applicability. We also highlight how such a model would help the Indian banks to mitigate risk in agricultural lending.

For individual farmers and agri-businesses, risk management involves choosing among alternatives for reducing the effects of risk on the firm, thereby affecting the firm’s welfare position. Risk management often requires the evaluation of tradeoffs between changes in risk, expected returns, entrepreneurial freedom and other factors. Research on risk management issues in agriculture has been among the main topics of interest of the Regional Research Committee for Financing Agriculture in a Changing Environment—macro, market, policy, and management issues.

A credit rating is a summary indicator of risk for banks’ individual credit exposures. Traditionally, most financial institutions relied virtually exclusively on subjective analysis or the so-called banker expert system to assess the credit risk of borrowers. Bank loan officers used information on various borrower characteristics which are called the “5 Cs” of credit. They are: (1) Character of borrower (reputation); (2) Capital (leverage); (3) Capacity (volatility of earnings); (4) Collateral; and (5) Condition (macroeconomic cycle). However, this method may be inconsistent if its risk weights are also based on expert opinion. The weights should be grounded based on the historical experiences. Accordingly, we have followed a statistical model approach which takes care of the “5 Cs” subjectively and produces consistent forecast about the borrowers’ default probability.

Bank can use such a credit rating tool in loan processing, credit monitoring, loan pricing, management decision-making, and in calculating inputs (probability of default, loss given default, default correlation and risk contribution, etc., have been discussed later in detail) for portfolio credit risk model.

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