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This paper reviews the prevalence of the use of risk ratings by commercial banks that participated in the Federal Reserve’s Survey of Terms of Bank Lending to Farmers between 1997 and 2002. Adoption of risk rating procedures held about steady over the period, with a little less than half the banks on the panel either not using a risk rating system, or reporting the same rating for all their loans in the survey. However, most of these banks were small, and roughly four‐fifths of all sample loans carried an informative risk rating. After controlling for the size and performance of the bank and as many nonprice terms of the loan as possible, findings reveal that banks consistently charged higher rates of interest for the farm loans they characterized as riskier, with an average difference in rates between the most risky and least risky loans of about 1 and a half percentage points.

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 study investigates important factors that should be used by lenders in risk‐rating their farm customers. These factors predict actual farm performance and debt repayment ability. Linear and logistic regression models are used to identify the debt‐to‐asset ratio as a major predictor of repayment ability. In addition, the rate of asset turnover and family living expenses are strong predictors of farm performance. The results are tested over several time periods to verify the robustness of the predictors.

Customers of Islamic banking industry continue to be skeptical on Sharīʿah compliance of Islamic banks despite receiving fatwa from the competent authorities. The purpose of this paper is to quantify the Sharīʿah risk taken by Islamic banks, so that customers are better informed on the level of Sharīʿah compliance that will help in removing the persistent level of skepticism toward Sharīʿah compliance.

This research has used the scorecard based modeling approach to build the Sharīʿah risk rating model, which consists of 14 factors that capture Sharīʿah risk and are grouped in 5 major areas revolving around regulatory support, quality of Sharīʿah supervision, business structure, product mix and treatment of capital adequacy ratio. The score calculated by applying the model is grouped into 4 tiers reflecting the level Sharīʿah compliance at bank as non-compliant, weak compliance, satisfactory compliance and high level of Sharīʿah compliance. Three case studies were conducted by applying the model to Islamic banks from Malaysia, Pakistan and Saudi Arabia.

The final Sharīʿah risk scores calculated by the model clearly differentiate the 3 banks on basis of their Sharīʿah risk. The underlying scores also highlighted the areas where banks need to improve to reduce their Sharīʿah risk.

This model can be applied by customers of Islamic banks who are interested in understanding Sharīʿah-related aspects of Islamic banking industry. This model can be applied on standalone basis or as an extension to the conventional counter party risk rating models. This model can benefit management of Islamic banks toward allocation of capital against Sharīʿah risk under Basel III, and regulators can apply the model to measure industry wide risk of Sharīʿah non-compliance.

This study examines whether international tourism demand in the Visegrád countries is influenced by countries' risk rating on environmental, social and governance (ESG) factors, as non-economic factors relating to ESG risks have been ignored by previous researches on determinants of international tourism demand.

The study investigates panel data for the Visegrád countries comprising the Czech Republic, Hungary, Poland and Slovakia over the period 1995–2019. Recently developed techniques of augmented mean group (AMG) and common correlated effects mean group (CCEMG) estimators are employed so as to take care of cross-sectional dependence, nonstationary residuals and possible heterogeneous slope coefficients.

The regression estimates suggest that besides economic factors, the perception of international tourists regarding ESG risk is another important determinant of international tourism demand in the Visegrád countries. The study also established that income levels in the tourists' originating countries are the most critical determinant of international tourism demand to the Visegrád countries.

The research outcomes of the study include the need for the Visegrád countries to direct policies towards further mitigating their ESG risks in order to improve future international tourism demand in the area. They also need to ensure exchange rate stability to prevent volatility and sudden spikes in the relative price of tourism in their countries.

The purpose of this paper is to discuss the case-based reasoning (CBR) approach to improve microcredit initiatives by means of providing a borrower risk rating system.

The CBR approach has been used to consider the Kiva microcredit system, which provides a characterization (rating) of the risk associated with the field partner supporting the loan, but not of the specific borrower which would benefit from it. The authors discuss how the combination of available historical data on loans and their outcomes (structured as a case base) and available knowledge on how to evaluate the risk associated with a loan request can be used to provide the end users with an indication of the risk rating associated with a loan request based on similar past situations.

The adopted approach is applied and evaluated employing a selection of cases from individual loans. From this perspective, the case base and the codified knowledge about how to evaluate risks associated with a loan represent two examples of knowledge IT artifacts.

The originality of the work lies in borrower risk rating in online indirect peer-to-peer microcredit lending platforms. The case base and the codified knowledge are the two contributions in knowledge IT artifacts.

This paper aims to analyze thoroughly all of the sources of research used to develop the money laundering (ML) and terrorist financing (TF) low-risk rating, a rating attained by Norway according to the Basel Institute of Governance, and determine the reasons why Norway is one of only two countries in the world according to the 2012 report, with the other being Estonia, to gain an overall low-risk ML and TF rating.

The differences between the USA and Norway which has obtained a low-risk ranking, were compared and contrasted.

Beginning with the Basel Institute Rating index as a legitimate source for use in assessing anti-money-laundering (AML)/TF risk, and the amount of documentation used in the index’s methodology, it has been proven that the low-risk rating Norway has received is well deserved, and that the US rating of medium risk is also deserved for the time the report was published. Achieving a low-risk rating is not as ambiguous as recently thought and neither is its application on a global scale.

The paper identifies practical areas of improvement and concerns in addressing the overall issue of ML and terrorist financing.