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Purpose – This chapter investigates the influence of bank loan underwriting practices on loan losses and identifies potential determinants of lending practices for five categories of loans: business, consumer, commercial real estate, home equity, and construction and land development loans.

Methodology/approach – Using data on the riskiness of lending practices obtained from the U.S. Federal Deposit Insurance Corporation (FDIC) bank examiner surveys from January 1996 to March 2009, I fit a two-step treatment effects model to measure the effects of underwriting practices on loan losses, controlling for the potential endogeneity of lending practices.

Findings – In the selection step, I find that for business loans, the likelihood that bank management will adopt low-risk lending practices increases with bank financial performance and management quality hierarchical complexity and decreases with market competition. Results for the selection of lending practices for consumer loans and three categories of real estate loans are similar to those found for business loans but show weaker statistical relationships to all explanatory variables. In the loss determination step, I find that lower (higher) risk underwriting practices are generally associated with lower (higher) gross loan charge-offs (as percentage of gross loans and leases) for five categories of loans: business, consumer, commercial real estate, home equity, and construction and land development loans.

Originality/value of chapter – This is the first study to model the determinants of loan underwriting practices with the practices being characterized in terms of their risk to the bank. In addition, this is the first study to consider the effects of the riskiness of lending practices on loan losses, controlling for the endogeneity of practices.

This study aims to investigate the relationships between loan growth, loan losses and net income after the 2008 global financial crisis. This study further conducts a comparative analysis by considering the period of COVID-19.

This study uses panel data models such as one-step system GMM, random effects, fixed effects and OLS, with a data set of 131 Chinese commercial banks from 2009 to 2020.

The study finds no significant relationship between loan growth and future loan losses. However, after adjusting loan loss by net interest income (NII-adjusted loan loss), the study reveals that loan growth in the subsequent year decreases if NII-adjusted loan loss increases. The study also demonstrates the positive effect of loan growth on net income as newly expanded loans are funded at similar costs but offered at a lower rate compared with existing loans. During COVID-19, loan growth and net income were higher than in previous years.

The findings suggest that Chinese banks can increase lending to support the economy without sacrificing loan quality, emphasizing the importance of maintaining and enhancing credit policies and practices. Chinese banks should also continue to refine their pricing strategies for loans and deposits. The findings also imply that China's policy responses to the impact of COVID-19 could serve as lessons for future policy decisions.

A credit risk model suitable for agricultural lenders is identified. The model incorporates sector correlations and is applied to the loan portfolio of an agricultural credit association to create a distribution of loan losses. The distribution is used to derive the lender’s expected and unexpected losses. Results of the analysis indicate that the association is more than adequately capitalized based on 1997S2002 data. Since the capital position of the association is lower than that of most other associations in the Farm Credit System, this raises the issue of overcapitalization in the System.

This paper aims to examine the relationship between abnormal loan growth and risk in Swedish financial institutions by type and borrower using three indicators as proxies for risks related to loan losses, the ratio of interest income to total loans and solvency perspectives.

Using a large sample of different types of Swedish financial institutions, this paper uses a panel framework to examine the relationships between abnormal loan growth rates and loan losses, interest income as a percentage of total loans, changes in the equity to assets ratio and changes in z-score.

The findings show two important points of evidence. First, abnormal lending to retail customers increases loan losses and interest income in relation to total loans. Second, abnormal lending to other credit institutions decreases loan losses and significantly changes the capital structure by increasing the reliance on debt funding and significantly improves the z-score measure.

The findings provide useful implications for the management of loan portfolios for a wide range of Swedish financial institutions, identifying two components: abnormal lending to households may increase loan losses and increase interest income in relation to total loans, and excessive lending to other credit institutions may reduce solvency risk and allow more debt financing for the financial institution.

This is the first study to use a panel framework in analyzing the behavior of different types of Swedish financial institutions in relation to loans granted to retail customers and other credit institutions.

To raise the quality of regulatory capital, Basel III capital rules recognize unrealized gains and losses on all available-for-sale (AFS) securities in Common Equity Tier 1 Capital (CET1). However, by examining the correlations between U.S. GDP growth rate, interest rates and regulatory capital ratios computed using Basel III regulatory capital definition for six U.S. global systemically important banks (G-SIBs) since 2007, this chapter finds that Basel III regulatory capital will enhance the pro-cyclicality of Basel III leverage ratio and Tier 1 capital ratio and their sensitivity to long-term interest rates. Therefore, Basel III capital standards may have significant implications for bank supervision and bank capital risk management in the near future. As banks will hold more high-quality liquid assets (HQLAs) as required by Basel III Liquidity Coverage Ratio (LCR), the weight of unrealized gains and losses arising from fair value accounting will increase in Basel III Tier 1 capital base, the consequent increase of pro-cyclicality in a bank’s regulatory capital ratios may distort the true picture of bank capital adequacy. If an expected loss approach (EL) is used as the provisioning model, such capital risk may be increased further. Moreover, as U.S. monetary policy has started tapering quantitative easing, long-term interest rates will increase inevitably. This may increase the negative impact of unrealized gains and losses on AFS securities on bank capital. As a result, it may be difficult for banks to maintain appropriate capital ratios to meet regulatory requirements and support business activities.

The purpose of this paper is to present a comprehensive framework for assisting lending banks in their current expected credit losses (CECL) forthcoming computations.

The bottom-up approach requires multiple steps including the spline method for identifying optimal segments in the lifetimes of loans, Poisson regressions for evaluating the explanatory variables and hazard rate probes for gaining inferences toward the expected credit losses and their projected schedule.

The CECL paradigm has both advantages and disadvantages, as discussed hereafter.

The model is practical, accurate in the sense that provisions are properly and timely allocated, it can be programmed and it relies on merely a few mild assumptions, thus it can be conveniently calibrated to fit broad macroeconomic scenarios.

This study provides background on the subject, motivate each module, construct the advised model, assemble a pseudo-database, demonstrate the functionality of the procedures and further draw conclusions on the effectiveness of the current strategy.

Prior research demonstrates that share prices reflect a risk premium that is associated with earnings variability. This suggests that managers can reduce the cost of capital and increase share prices by reducing earnings variability. In this study, we investigate bank managers' use of discretion in estimating loan loss provisions (LLP) to reduce earnings variability. We find that banks with relatively high pre‐managed earnings have positive discretionary LLP and banks with relatively low pre‐managed earnings have negative discretionary LLP, results that are consistent with the hypothesis of earnings management to reduce earnings variability. In addition, we find that bank managers' decisions to reduce earnings variability are related to the need for external financing and to gains and losses on the sale of securities which serve as substitutes for accomplishing their objective of earnings variability reduction.

The purpose of this paper is to examine the factors that influence the variability of loan losses (termed as non-performing advances or NPA in India) of Indian banks in the public sector during the period of five years from 2001 to 2005.

The analysis is based on a panel approach, which considers both spatial and time dimensions of observations. Panel regression was used to explore the impact of different bank-specific factors on NPAs of 27 public sector banks (PSBs). Standard tests were used to find out suitability of different models of panel data analysis. Eight bank-specific factors were identified for analysis on the basis of review of extant literature.

Certain bank-specific factors, in particular, net interest margin and capital adequacy ratio exhibit negative and significant impact on gross non-performing advances (GNPA) ratio of Indian PSBs. The results also suggest that relative quantum of sensitive sector (SEN) (comprised of commercial real estate, commodity and capital market) advances has a positive relationship with NPA ratio, and such a relationship is statistically significant.

The sample is restricted to India and may not be reflective of other countries. The study considers bank-level factors, and there are some macro factors (e.g. gross domestic product, interest rate and inflation rate) which could have explained the variability of GNPA ratio.

Provisioning against loan losses is a major issue for stability of the banking system. Identification of appropriate causes of variability of such loan losses is important for managing credit portfolio of a bank. A positive and significant relationship between SEN advances and NPA calls for a more cautionary approach toward lending to those sectors.

This paper is believed to be the first attempt to empirically examine the role of bank-specific factors. This study attempts to enrich empirical research in the field and provides an insight into the role of various bank-specific factors on loan losses in the context of Indian PSBs. The study provides contrary evidence regarding the role of priority sector advances on a GNPA ratio.

A simulation methodology is applied to the loan loss reserve process of an agricultural lender. Weaknesses of the point‐estimate approach to estimating loan loss reserves are addressed with a “bottom‐up” model. Modeling includes consideration of the producer’s and the lender’s diversification efforts. Implementation of this model will provide the lender a better understanding of the institution’s portfolio risk, as well as the credit risk associated with each loan. This study compares the lender’s loan loss estimates to a distribution of losses with associated probabilities. The comparative results could provide the lender a basis for setting probability levels for determining the regulatory required level of loan loss reserve.