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With the emergence of Fintech lending, small firms can benefit from new channels of financing. In this setting, the creditworthiness and the decision to extend credit are often based on standardized and advanced machine-learning techniques that employ limited information. This paper investigates the ability of machine learning to correctly predict credit risk ratings for small firms. By employing a unique proprietary dataset on invoice lending activities, this paper shows that machine learning techniques overperform traditional techniques, such as probit, when the set of information available to lenders is limited. This paper contributes to the understanding of the reliability of advanced credit scoring techniques in the lending process to small businesses, making it a special interesting case for the Fintech environment. [ABSTRACT FROM AUTHOR]

Credit scorecards are essential tools for banks to assess the creditworthiness of loan applicants. While advanced machine learning models like XGBoost and random forest often outperform traditional logistic regression in predictive accuracy, their lack of interpretability hinders their adoption in practice. This study bridges the gap between research and practice by developing a novel framework for constructing interpretable credit scorecards using Shapley values. We apply this framework to two credit datasets, discretizing numerical variables and utilizing one-hot encoding to facilitate model development. Shapley values are then employed to derive credit scores for each predictor variable group in XGBoost, random forest, LightGBM, and CatBoost models. Our results demonstrate that this approach yields credit scorecards with interpretability comparable to logistic regression while maintaining superior predictive accuracy. This framework offers a practical and effective solution for credit practitioners seeking to leverage the power of advanced models without sacrificing transparency and regulatory compliance. [ABSTRACT FROM AUTHOR]

India's economic growth is closely associated with its energy demand, and the oil and gas industry fulfills this demand. The importance of this industry is such that it influences the decisions of many other sectors of the Indian economy. The volatility of this industry makes it necessary to analyze the liquidity position by assessing the impact of prices on the availability of liquid resources. Liquidity analysis pertains to how quickly the assets of a company can be converted into cash to meet its current obligations so as to ensure financial stability and creditworthiness. Inability of a firm to meet its short-term responsibility is likely to result in bad credit rating as well as a decrease in the goodwill of the firm in the market. Therefore, the present paper analyzes the liquidity position of oil and gas industry by making a comparative study of selected public sector undertakings (PSUs) and private companies in India for the period 2016-17 to 2020-21. The results reveal that Oil India Ltd., ONGC and Vedanta Ltd. (Cairn Oil and Gas) have a better liquidity position than IOCL and Nayara Energy. [ABSTRACT FROM AUTHOR]

This study examines the existing credit rating methodology proposed in the literature to explore the development of a new credit rating model based on the financial variables of the enterprise. The focus is on the period after the financial crisis of 2018. This study aims to develop a credit rating model using neural networking and tests the same for its accuracy. The goal of this study is to address the issue brought up by previous research on subjectivity in the data used to determine creditworthiness. The database for the study includes financial data up to July 2022 from December 2018. A model is created to assess an enterprise's creditworthiness using neural networking. This study first evaluated the existing credit rating models proposed in the literature. Next, based on financial data and neural networking, a model is developed. It was evident that the model developed in this study has an accuracy of 85.16% and 76.47% on train and test data respectively. There exist several models to determine the creditworthiness of borrowers but all failed to address the concern of subjectivity in the data. The model created in this study made use of cutting-edge technology such as neural networking and financial data. This paper's unique approach and model construction based on a comparison of existing models is rare in the literature and justify the originality of this paper with a practical value at the global level. [ABSTRACT FROM AUTHOR]

This study investigates the way in which corporate social responsibility (CSR) practices are perceived by the U.K. bond market. In this respect, we distinguish between the two ambiguous subdimensions – that is, CSR disclosure and CSR performance. We analysed the association between CSR disclosure and the cost of corporate bonds by means of a multi‐theoretical framework combining economics with social theories. We also examine the moderating effect of the CSR performance on this relationship. We estimate a random‐effects GLS regression and random‐effects ordered probit regression using a sample of companies belonging to the FTSE 100 index during the 2013–2017 period. We find that bondholders and credit rating agencies incorporate CSR disclosure into their assessment and evaluation of the firms' creditworthiness. Mainly, our results show that bondholders encourage and value CSR reporting primarily for companies with weak CSR performance, but stop doing so once the company reach a certain level of CSR performance. We further examined each CSR disclosure subdimension and aspects of corporate creditworthiness through a disaggregated analysis. Both environmental and community relations disclosures are significant in corporate creditworthiness, whereas only responsibility disclosure is related to credit ratings. Finally, results highlight that disclosure of CSR information is especially relevant to financially healthy firms. Our findings have several implications for academics, practitioners, and policymakers in understanding the consequences of CSR disclosure and CSR performance on bond markets and credit rating. [ABSTRACT FROM AUTHOR]

Chit fund is a peer-to-peer saving and borrowing scheme operated among trusted groups of people. It is a reliable source of funds in emergencies with no guarantor and low-interest rate. Despite their enduring benefits, chit funds face challenges related to trust, transparency, and security. At present, unscrupulous subscribers might join chit-funds, borrow money, and make payment defaults. This leads to disruptions in contribution cycle and affects overall functioning. Additionally, non-transparent record-keeping and transaction processes, hinder participants from verifying fund activities, creating a susceptible environment for fraud and malpractice. These challenges are an obstacle to the sustainability and trustworthiness of the chit-fund system. Therefore, to overcome aforementioned challenges, this paper proposes TruChit, a blockchain-based chit fund system with creditworthiness evaluation framework. It leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) to assess the creditworthiness of subscribers, which ensures the credibility and reliability of individual participants. Further, proposes permissioned blockchain-assisted chit-fund framework with role-based access control to instill trust, security, and transparency within chit-fund operations. Moreover, the efficacy of TruChit is evaluated by analyzing credit score dataset and achieves 93.5% accuracy, which is better than other approaches. [ABSTRACT FROM AUTHOR]

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Originality/value: 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. [ABSTRACT FROM AUTHOR]

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Purpose: This study aims to examine the determinants of Sukuk credit ratings of issuing firms in Malaysia, and the rating changes from lower to higher rating and vice versa. Design/methodology/approach: A total of 328 Sukuk issuances and 1,110 Sukuk rating announcements from 2009 to 2014 were analysed using generalized ordered logit regressions approach. Firm financial characteristics, corporate governance attributes, macroeconomic factors and Sukuk structures (debt or equity based) were among the important determinants used to explain the different Sukuk credit ratings. Findings: The results indicate a positive association of Sukuk credit rating with issuing firm's financial information, governance attributes and the Sukuk structure whilst the macroeconomic factors did not explain the changes in the Sukuk credit rating. Specifically, firm size, profitability and leverage characteristics had significant positive effect on Sukuk credit rating for listed firms whilst only firm's profitability had a positive effect on Sukuk credit rating by unlisted firms. With regard to governance, the board structure which includes board size, board independence and CEO/Chairman non-duality is associated with positive Sukuk credit rating for listed firms. Only financial report audited by big four auditors is associated with positive Sukuk credit rating for unlisted firms. Equity-based Sukuk are associated with positive Sukuk credit rating for listed firms while for unlisted firms only the Ijarah Sukuk had a positive Sukuk credit rating. Research limitations/implications: Data on credit rating is scarce and had to be hand-collected from published reports. Furthermore, issues on the lack of standardisation of Islamic contracts in different geographical areas could constrain on the comparability of findings on determinants of ratings in different jurisdictions. Practical implications: The findings provide some guide to the rating agencies to objectively assess the issuer's creditworthiness that could mitigate default risk. Mitigating the default risk will boost investors' confidence and credibility of credit rating agencies. Originality/value: This study examines the determinants of Sukuk credit rating of issuing firms in Malaysia, which include not only the listed firms but also the unlisted firms. [ABSTRACT FROM AUTHOR]

The diffusion of Environmental, Social and Governance (ESG) metrics is increasingly affecting corporates behaviour and their ability to attract investors. Corporate ESG practices are nowadays considered as a key element in evaluating creditworthiness and the cost of capital, to direct funds to the best‐performing companies that limit the harmful impact on the planet and the societies. Due to this increased interest in ESG by companies, investors and policymakers, a high number of ESG scores and metrics have been developed, each with different methodologies and scopes. Because of this variety, it could be therefore challenging for investors to understand and compare ESG measures. In this paper, we address this issue by proposing a method to combine the information provided by different ESG scores into a single aggregate measure of company sustainability and link this combined score to the credit rating of companies. The proposed methodology can help investors to improve their investment decisions by combining more diverse information on company sustainability as a driver of companies' credit rating, thereby reducing information asymmetries. [ABSTRACT FROM AUTHOR]