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This study aims to render a fundamental assessment of the Basel II internal ratings-based (IRB) approach by taking readings of the Australian banking sector since the implementation of Basel II and comparing them with signals from macroeconomic indicators, financial statistics and external credit ratings. The IRB approach to capital adequacy for credit risk, which implements an asymptotic single risk factor (ASRF) model, plays an important role in protecting the Australian banking sector against insolvency.

Realisations of the single systematic risk factor, interpreted as describing the prevailing state of the Australian economy, are recovered from the ASRF model and compared with macroeconomic indicators. Similarly, estimates of distance-to-default, reflecting the capacity of the Australian banking sector to absorb credit losses, are recovered from the ASRF model and compared with financial statistics and external credit ratings. With the implementation of Basel II preceding the time when the effect of the financial crisis of 2007-2009 was most acutely felt, the authors measure the impact of the crisis on the Australian banking sector.

Measurements from the ASRF model find general agreement with signals from macroeconomic indicators, financial statistics and external credit ratings. This leads to a favourable assessment of the ASRF model for the purposes of capital allocation, performance attribution and risk monitoring. The empirical analysis used in this paper reveals that the recent crisis imparted a mild stress on the Australian banking sector.

Given the range of economic conditions, from mild contraction to moderate expansion, experienced in Australia since the implementation of Basel II, the authors cannot attest to the validity of the model specification of the IRB approach for its intended purpose of solvency assessment.

Access to internal bank data collected by the prudential regulator distinguishes this paper from other empirical studies on the IRB approach and financial crisis of 2007-2009. The authors are not the first to attempt to measure the effects of the recent crisis, but they believe that they are the first to do so using regulatory data.

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.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring.

This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring.

The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model.

This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

The purpose of this paper is to develop a credit‐scoring model as an aggregate valuation procedure that integrates various financial and non‐financial factors and thereby improves small to medium‐sized enterprises' (SMEs) knowledge about their default risk.

Using panel data from a representative sample of Portuguese SMEs operating in the food or beverage manufacturing sector, this paper develops a logit scoring model to estimate one‐year predictions of default.

The probability of non‐default in the next year is an increasing function of profitability, liquidity, coverage, and activity and a decreasing function of leverage. Smaller firms and those with just one bank relationship have a higher probability of default. The findings suggest that a main bank has incentives to engage in hold up by increasing margins that ex post are too high.

Because SMEs differ from large corporations in their credit risk (e.g., riskier, lower asset correlations), this study has implications for both banks and supervisory actors. Banks should consider qualitative variables when setting internal systems and procedures to manage credit risk. Supervisory institutions should claim mixed credit ratings to determine regulatory capital requirements.

This paper offers a new model, focused specifically on SMEs, and explores the role of financial and non‐financial factors in determining internal credit risks.

The credit ratings issued by the Big 3 ratings agencies are inaccurate and slow to respond to market changes. This paper aims to develop a rigorous, transparent and robust credit assessment and rating scheme for sovereigns.

This paper develops a regression-based model using credit default swap (CDS) data, and data on financial and macroeconomic variables to estimate sovereign CDS spreads. Using these spreads, the default probabilities of sovereigns can be estimated. The new ratings scheme is then used in conjunction with these default probabilities to assign credit ratings to sovereigns.

The developed model accurately estimates CDS spreads (based on RMSE values). Credit ratings issued retrospectively using the new scheme reflect reality better.

This paper reveals that both macroeconomic and financial factors affect both systemic and idiosyncratic risks for sovereigns.

The developed credit assessment and ratings scheme can be used to evaluate the creditworthiness of sovereigns and subsequently assign robust credit ratings.

The transparency and rigor of the new scheme will result in better and trustworthy indications of a sovereign’s financial health. Investors and monetary authorities can make better informed decisions. The episodes that occurred during the debt crisis could be avoided.

This paper uses both financial and macroeconomic data to estimate CDS spreads and demonstrates that both financial and macroeconomic factors affect sovereign systemic and idiosyncratic risk. The proposed credit assessment and ratings schemes could supplement or potentially replace the credit ratings issued by the Big 3 ratings agencies.

Achieving equal treatment of credit applicants has been a legitimate concern of legislators and the credit industry. However, measures taken to date in attempting to comply with anti‐discrimination laws arguably do not allow for the most effective use of credit scoring models, and could run counter‐intuitive to the intention of legislation through indirect discrimination. The purpose of this paper is to offer an alternative interpretation that preserves the intention of legislation and also retains the integrity and effectiveness of credit scoring models.

The paper makes a legal analysis of anti‐discrimination laws in the UK, with US law as a comparison, aiming to demonstrate that concerns in using information protected under anti‐discrimination laws as variables may be misplaced, because nothing in these laws precludes the inclusion of all relevant variables in modelling.

The inclusion of variables representing protected characteristics in credit scoring models may not contradict current anti‐discrimination laws.

Limitations exist from the perspectives of customer relationship and the need for further checks and balances. Conclusive validation of the findings will need to come from the courts. The paper provides a springboard for empirical research on whether the inclusion of variables representing protected characteristics in credit scorecards continues to produce better decision‐making models.

The findings benefit credit risk modelling as a whole in facilitating the development of credit scorecards that are in compliance with anti‐discrimination laws, without sacrificing their effectiveness.

The paper presents a fresh perspective and alternative solution to legal concerns regarding the use of protected characteristics in credit scoring, which will be useful to the credit industry.

The paper aims to identify “problematic” agricultural credit co‐operatives (CCAM) and to evaluate their risk of insolvency as a function of financial indicators, providing regulators and other stakeholders with a set of tools that would be predictive of future insolvency and perhaps bankruptcy.

Using a database of CCAM failures in the period between 1995 and 2009, statistical models of failure of CCAM, are estimated and compared, using logistic regression analysis and multiple discriminant analysis for assessing the potential failure of CCAM as a function of financial/economical indicators.

The paper identified the variables customer resources growth, transformation ratio, credit overdue, expenses ratio, structural costs, liquidity, indebtedness and financial margin as determinants of CCAM failure. It suggests that CCAM take measures geared to boosting business, to shoring up the financial margin and the deposit base, to bolstering the complementary margin and to improving the credit recovery processes. Additionally it is necessary to increase cost efficiency, rationalizing structures and procedures consistent with reducing operating costs without detriment to the quality of service provided.

This paper helps to understand why agricultural credit co‐operatives fail.

The purpose of this paper is to examine, in the context of movement towards a fair‐value based pension accounting standard, the value relevance of both recognized and disclosed pension accounting information.

Using hand‐collected data from Fortune 200 firms, this study includes both recognized and disclosed pension accounting measures (aggregated and disaggregated) in multivariate regression models. The investigation employs tests of relative and incremental value relevance in both equity and credit rating evaluation contexts.

Findings indicate that pension information recognized under a fair‐value‐based accounting model is no more or less value relevant than pension information recognized under the SFAS 87 model. Also, the disclosed off‐balance sheet pension amount is incrementally value relevant for determining share prices. However, it is not value relevant for the credit rating decision.

This study tests the relevance and reliability of accounting information jointly. Theoretically, however, relevance and reliability affect information usefulness and, thus, valuation decisions independently.

This paper yields a number of significant implications for standard setters. The unique evidence that investors apply off‐balance sheet pension amounts in the equity valuation context implies that required recognition under a fair‐value standard may not provide a significant incremental benefit over DB plan disclosures. However, such a standard may yield potential improvements in the credit rating decision context and may be much more likely to impact credit rating decisions going forward. Considering the continued shift towards fair‐value‐based pension accounting standards internationally, recognizing transitory elements of fair‐value pension cost separately from operating income is essential for mitigating any potential loss in value relevance.

Quantitative methods known as scoring models have been traditionally developed for credit granting decisions using statistical procedures. The purpose of this paper is to develop a non‐parametric credit scoring model for micro enterprises that are not maintaining balance sheets, and without having a track record of performance and other credit‐worthy parameters.

Multilayer perceptron procedure is used to evaluate credit reliability in three classes of risk, i.e. bad risk credit, foreclosed risk credit and good risk credit.

The development of a neural network model for micro enterprises facilitates bankers and financial institutions in credit granting decisions in an automatic manner in the Indian context.

This study applies comprehensive information on parameters of financial package prepared by Indian financial institutions and banks to micro enterprises to design a credit risk model. This model, instead of categorizing borrowers in terms of their “ability to pay”, attempts a solution to the unsolved problem of credit availability to micro enterprises in an Indian context, having no past performance track record.