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Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This study aimed to improve the financing credit evaluation for small and medium-sized real estate enterprises (SMREEs). A financing credit evaluation model was proposed, and a blockchain-driven financing credit evaluation framework was designed to improve the transparency, credibility and applicability of the financing credit evaluation process.

The design science research methodology was adopted to identify the main steps in constructing the financing credit model and blockchain-driven framework. The fuzzy analytic hierarchy process (FAHP)–entropy weighting method (EWM)–set pair analysis (SPA) method was used to design a financing credit evaluation model. Moreover, the proposed framework was validated using data acquired from actual cases.

The results indicate that: (1) the proposed blockchain-driven financing credit evaluation framework can effectively realize a transparent evaluation process compared to the traditional financing credit evaluation system. (2) The proposed model has high effectiveness and can achieve efficient credit ranking, reflect SMREEs' credit status and help improve credit rating.

This study proposes a financing credit evaluation model of SMREEs based on the FAHP–EWM–SPA method. All credit rating data and evaluation process data are immediately stored in the proposed blockchain framework, and the immutable and traceable nature of blockchain enhances trust between nodes, improving the reliability of the financing credit evaluation process and results. In addition, this study partially fulfills the lack of investigations on blockchain adoption for SMREEs' financing credit.

In 2022, US financial regulators proposed to mandate a single central clearing mechanism for treasury bonds and repo transactions to stabilize financial markets. The systemic risks inherent in repo markets were first highlighted by the global financial crisis and, as a response, global financial authorities such as the Financial Stability Board (FSB) and Bank for International Settlements (BIS) have advocated for the introduction of a central counterparty (CCP). This study examines the structural characteristics of Korean repo markets and proposes the introduction of CCPs as a way to mitigate systemic risk. To this end, the author analyzes the structural differences between US and European repo markets and estimates the potential consequences of introducing CCP clearing in local repo markets. In general, CCPs offer two benefits: they can reduce required capital through netting in multilateral transactions, and they can mitigate the effects of risk transfer by isolating counterparty risk during periods of turbulence. In Korea, the latter effect is expected to play a pivotal role in mitigating potential risks.

This paper explores how financial technology (FinTech) organisations address poverty-related challenges when providing digital financial services. Employing the conceptual foundation of the liability of poorness (i.e. literacy gaps, a scarcity mindset, intense non-business pressures and a lack of financial slack), this paper explores the innovative strategies that FinTechs use to address these liabilities and promote entrepreneurship.

The paper uses detailed case data collected from three FinTech organisations operating in one South Asian country.

FinTech organisations' innovative strategies reflect a combination of “high touch” (human) vs “low touch” (digital) solutions. All the organisations simplified internal systems or procedures to accommodate customers. The degree to which the three organisations adopted each of the identified strategies shows an emerging typology of FinTechs; that is, innovators with high digital interactions, a mix of digital-human interactions and high human interactions.

The paper develops a typology which categorises FinTech innovative strategies. The typology highlights strategies pro-poor FinTechs use and explains the types of entrepreneurial support innovative organisations provide for their customers. Both the typology and the innovative strategies contribute to enhanced financial inclusion and entrepreneurial promotion amongst the poor.

The originality of the paper comes from its focus on FinTechs' innovative pro-poor strategies. Existing studies typically address the technology-side of innovations. In contrast, this paper combines innovative strategies with the liability of poorness to identify issues associated with financial inclusion.

This study aims to explore credit scoring regulations, governance, variables and methods used by peer-to-peer (P2P) lending platforms in key players of the Association of Southeast Asian Nations (ASEAN) region’s P2P, Indonesia, Malaysia and Singapore.

This study explores the P2P Lending characteristics of the three countries using qualitative literature review, interview, focus group discussion and desk research.

This study concludes that the credit scoring variables used by the countries’ companies are almost the same. Key drivers of the differences are countries’ regulations, management/business core value and credit scoring data processing methods.

Ultimately, this research provides a comprehensive view for investors, businesses and researchers on the topic of ASEAN credit scoring governance and will help them navigate the complexities and improve their awareness on the importance of credit scoring governance in P2P lending companies.

This research provides an in-depth perspective on how P2P lending companies, credit scoring governance and regulations in the biggest three countries in Southeast Asia.

This study aims to scrutinize the relationship between the perception of big data (BD) features and the primary outcomes of financial accounting. Likewise, it explores whether financial accounting practices moderate the relationship between BD features and firm sustainability.

The study used a questionnaire survey based on the Likert scale for two distinct groups of participants: academic scholars and industry practitioners operating in the BD era within the energy sector.

The results reveal significant positive associations between BD features and firm performance, reporting quality, earnings determinants, fair value measurements, risk management, firm value, the efficiency of the decision-making process, narrative disclosure and firm sustainability. Besides, the path analysis indicates an indirect impact of BD on firm sustainability via financial accounting practices. The results suggest that energy firms should consider incorporating BD analysis into their financial accounting processes to improve their sustainability performance and create long-term value for their stakeholders.

The findings are particularly interesting to academics in accounting and business to improve the accounting curriculums to fit the technological revolution, especially in the field of BD analytics. Practitioners within energy industries must also refine their skills and knowledge to meet the challenges of BD in the foreseeable future. The results provide important implications for policy setters to revise current financial accounting standards to cope with technological innovation.

The study makes a valuable contribution by critically examining the impact of BD on various financial accounting practices neglected in prior research. It highlights the transformative power of BD in the domain of financial accounting and provides insights into its potential implications for energy firms.

This study investigates the relationship between credit booms and bank risk in Association of Southeast Asian Nations (ASEAN) countries, with credit information sharing acting as a moderator.

The authors use a two-step System Generalized Method of Moments (SGMM) estimator on a sample of 79 listed banks in 5 developing ASEAN countries: Indonesia, Philippines, Malaysia, Thailand and Vietnam in the period 2006–2019. In addition, the authors perform robustness tests with different proxies for credit booms and bank risk. The data are collected on an annual basis.

Bank risk is positively related to credit booms and is negatively associated with credit information sharing. Further, credit information sharing reduces the detrimental effect of credit booms on bank stability. The authors find that both public credit registries and private credit bureaus are effective in enhancing bank stability in ASEAN countries. These results are robust to regression models with alternative proxies for credit booms and bank risk.

Banks in ASEAN countries tend to have strong lending growth to support the economy, but this could be detrimental to stability of the sector. Credit information sharing schemes should be encouraged because these schemes might enable growth of credit without compromising bank stability. Therefore, policymakers could promote private credit bureaus (PCB) and public credit registries (PCR) to realize their benefits. The authors' research focuses on developing ASEAN countries, but future research could provide more evidence by expanding this study to other emerging economies. In-depth interviews and surveys with bankers and regulatory bodies about these concerns could provide additional insights in the future.

The study is the first to examine the role of PCB and PCR in alleviating the negative impact of credit booms on bank risk. Furthermore, the authors use both accounting-based and market-based risk measures to provide a fuller view of the impact. Finally, there is little evidence on the link between credit booms, credit information sharing and bank risk in ASEAN, so the authors aim to fill this gap.

The purpose of the study is to elicit risk factors that are peculiar to public-private partnership (PPP)-procured mass housing in Nigeria from the expert perspectives in ensuring the success of the scheme thereby reducing housing deficit in the country.

The risk inherent in construction projects had been established through literature in general. The risk in PPP projects is emerging because of the recent acceptance of the procurement option by governments all over the globe. The Nigerian Government has also adopted the procurement option in bridging the housing deficit in the country. This study, therefore, conducts a Delphi survey on the probability of risk occurrence peculiar to PPP mass housing projects (MHPs) in Nigeria. Pragmatic research approach through the mixed method of both quantitative and qualitative methods was adopted for this study. The quantitative method adopts the administration of questionnaires through the Delphi survey, whereas the qualitative method used interviews with the respondents. A two-stage Delphi questionnaire was administered to construction practitioners that cut across academics, the public and the private sectors by adopting convenient sampling techniques and following the Delphi principles and procedures. A total of 63 risk factors were submitted to the expert to rank on a Likert scale of 7 and any risk factors that the mean item score (MIS) falls below the grading scale of the five-point benchmark is deemed not necessary a risk factor associated with PPP MHPs and thereby expunged from the second round of the Delphi Survey. The interview was subsequently applied to the respondents to substantiate the risk factors that are peculiar to PPP-procured mass housing in the study area.

The findings show that risk factors such as maintenance frequent than expected, life of facility shorter than anticipated and maintenance cost higher than expected fall below 5.0 benchmark with MIS of 4.64 and 4.55 indicating that the risk factors are not peculiar to PPP mass housing in Nigeria.

The implication for practise of this research is that these risk factors provide the PPP stakeholders with the comprehensive checklists that can aid in developing PPP risk assessment guidelines in the sector though both partners should be aware of the dynamic nature of risk because new ones might be emerging.

The authors hereby declare that the research findings are a product of a thorough research conducted in the study area and have not to be submitted or published by another person or publisher and due acknowledgement was made where necessary.

This study aims to focus on the impact of multi-level knowledge sharing between and within organizations on the risk control of rural inclusive finance. The paper presents a synergistic risk control system integrating external and internal factors for rural inclusive finance by constructing different knowledge-sharing platforms in an environment, which is full of many uncertainties.

This study is based on survey methods. To achieve the research objectives, the authors adopt a single case study approach. For data collection, the authors apply a wide variety of methods such as semi-structured interviews, field visits, second-hand databases and official websites.

The results emphasize that using multi-level knowledge sharing such as the inter- and intra-organizational level, can facilitate the risk control of rural inclusive finance during the post-COVID-19 era. Furthermore, it is also noted that achieving knowledge sharing at different levels by building diverse knowledge-sharing platforms can promote the risk control of rural inclusive finance from the individual-organization level to the chain level of multi-organization collaboration, which contributes to the formation of symbiotic risk control ecology.

The authors have formed the “Chinese wisdom” to deal with inclusive financial risks and to promote in-depth development in relation to the “last mile” practice of inclusive finance, which means the final and the most important phase of a project. The conclusions contribute to enriching the outcomes regarding the risk control of rural inclusive finance, provide experiences to its sustainable development and offer a reference to other countries with their risk control of rural inclusive finance.

Drawing on the knowledge-sharing approach, this study creatively resolves the persistent problems in the risk control of rural inclusive finance, which forms a powerful supplement to the extant literature. Meanwhile, the paper combines the two contextual factors of the post-COVID-19 era and emerging economies, which can be deemed as a novel attempt.

The authors examine the role of CEO political ideology in the credit rating process.

This study adopts a quantitative method with panel data regressions using a sample of 5,211 observations from S&P 500 firms from 2001 to 2012.

The authors find that firms run by Republican-leaning CEOs, who tend to have conservative political ideologies, enjoy more favorable credit ratings than firms run by Democratic-leaning CEOs. In addition, the association between CEO political ideology and credit ratings is more pronounced for firms with high operating uncertainty, low capital intensity, high growth potential, weak corporate governance and low financial reporting quality. Finally, the authors find that CEO political ideology affects a firm's cost of debt incremental to credit ratings, consistent with debt investors incorporating CEO political ideology in their pricing decisions.

Leveraging CEO political ideology, the authors document that credit rating agencies incorporate managerial conservatism in their credit rating decisions. This finding suggests that CEO political ideology serves as a meaningful signal for managerial conservatism.

The study suggests that credit rating agencies incorporate CEO political ideology in their credit rating process. Other capital market participants such as auditors and retail investors can also use CEO political ideology as a proxy for managerial conservatism when evaluating firms.

The paper carries practical implications for practitioners, firm executives and regulators. The results on the association between CEO political ideology and credit ratings suggest that other financial institutions could also incorporate CEO political ideology in their evaluation in their evaluation of firms. For example, when evaluating audit risk and determining audit pricing, auditors may add CEO political ideology as a risk factor. For firms, especially those that have Democratic-leaning CEOs, the authors suggest that they could reduce the unfavorable effect of CEO political ideology on credit ratings by improving their corporate governance and financial reporting quality, as demonstrated in the cross-sectional analyses. Finally, this study shows that CEO political ideology, as measured by CEOs' political contributions, is closely related to a firm's credit ratings. This finding may inform regulators that greater transparency for CEOs' political contributions is needed as information on contributions could help capital market participants perform risk analyses for firms.

Credit rating agencies release their research methodologies for determining corporate credit ratings and identify managerial conservatism as an important factor that affects their risk assessments. The extant literature, however, has not empirically investigated the relation between credit ratings and managerial conservatism, which, according to behavioral consistency theory, can be proxied by CEO political ideology. This study provides novel empirical evidence that identifies CEO political ideology as an important input factor in the credit rating process.