Creditworthiness Of Banks Research Articles

Prediction of bank credit worthiness through credit risk analysis: an explainable machine learning study

AbstractThe control of credit risk is an important topic in the development of supply chain finance. Financial service providers should distinguish between low- and high-quality customers to predict credit risk accurately. Proper management of credit risk exposure contributes to the long-term viability and profitability of banks, systemic stability, and efficient capital allocation in the economy. Moreover, it benefits the development of supply chain finance. Supply chain finance offers convenient loan transactions that benefit all participants, including the buyer, supplier, and bank. However, poor credit risk management in supply chain finance may cause losses for finance providers and hamper the development of supply chain finance. Machine learning algorithms have significantly improved the accuracy of credit risk prediction systems in supply chain finance. However, their lack of interpretability or transparency makes decision-makers skeptical. Therefore, this study aims to improve AI transparency by ranking the importance of features influencing the decisions made by the system. This study identifies two effective algorithms, Random Forest and Gradient Boosting models, for credit risk detection. The factors that influenced the decision of the models to make them transparent are explicitly illustrated. This study also contributes to the literature on explainable credit risk detection for supply chain finance and provides practical implications for financial institutions to inform decision making.

HFTCRNet: Hierarchical Fusion Transformer for Interbank Credit Rating and Risk Assessment.

As a prominent application of deep neural networks in financial literature, bank credit ratings play a pivotal role in safeguarding global economic stability and preventing crises. In the contemporary financial system, interconnectivity among banks has reached unprecedented levels. However, many existing credit risk models continue to assess each bank independently, resulting in inevitable suboptimal performance. Thus, developing advanced neural networks to model intricate temporal dynamics and interconnected relationships in the banking system is essential for an effective credit rating and risk assessment learning system. To this end, we propose a novel hierarchical fusion transformer for interbank credit rating and risk assessment (HFTCRNet), which includes the long-term temporal transformer (LT 3 ) module, short-term cross-graph transformer (STCGT) module, attentive risk contagion transformer (ARCT) module, and hierarchical fusion transformer (HFT) module to capture the long-term growth trajectories of banks, the short-term interbank network variance, the potential propagation of risks within interbank network, and integrate these information hierarchically. We further develop an interbank credit rating dataset, encompassing quarterly financial data, interbank lending networks, and key indicators such as credit ratings and systemic risk (SRISK) for 4548 banks from 2016Q1 to 2023Q1. Notably, we also adapt the minimum density algorithm to stabilize the interbank loan network over time, aiding in the analysis of long-term and short-term network effects. Our learning system uses semi-supervised learning to handle labels of varying sparsity, integrating credit ratings and SRISK for a comprehensive assessment of individual bank creditworthiness and systemic interbank risk. Extensive experimental results on our interbank dataset show that HFTCRNet not only outperforms all the baselines in terms of credit rating accuracy but also can evaluate the systemic risk within the interbank network. Code will be available at: https://github.com/AI4Risk/HFTCRNet.

Анализ кредитного портфеля и кредитного риска в коммерческом банке

The implementation of a competent credit policy, the formation of a balanced loan portfolio, and the minimization of a level of credit risk are the main tasks of banking management. Credit operations of commercial banks have been and remain the most profitable and the banking sector profitability directly affects the financial market sustainable development. The main purpose of the study is to analyze the commercial bank credit activity and a credit risk on the example of the HCF Bank LLC commercial bank. The paper analyzes the structure of the bank’s loan portfolio, the overdue debt level, and the credit transactions restructuring. The authors performed the centralized and decentralized (coefficient) analysis of the commercial bank credit activity. The centralized analysis results showed the bank’s full compliance with the standards regulating lending activities. Ratio analysis revealed a downward trend in the quality of the retail customers’ loan portfolio due to an increase in the overdue debt and, consequently, an increase in credit risk. Based on the results of the study, the authors came to a conclusion that the level of the commercial bank credit activity for the analyzed period is high. The commercial bank has a low diversified loan portfolio, which makes it dependent on the loyalty and solvency level of its main individual customers. The assigned international and national credit ratings indicate a high degree of the commercial bank creditworthiness in the medium term period. The analysis of bank procedures for the work with credit risks shows that the bank follows the standard policy in the sphere of credit risk management. However, to avoid the growth of the riskiness level of credit operations and maintain the quality of the loan portfolio at a high level, the commercial bank should introduce more effective measures of managing credit risk, such as limiting, diversifying, and securing.

ПІДХОДИ ДО МОДЕЛЮВАННЯ ВЗАЄМОЗВ’ЯЗКУ БАНКІВСЬКОЇ СИСТЕМИ ТА ЕКОНОМІЧНОЇ АКТИВНОСТІ ПІДПРИЄМСТВ

The relevance is determined based on the need to adapt foreign developments to the conditions and specifics of banks and their impact on the real sector in Ukraine. The part of the general problem that needs to be solved is identified, namely the determination of the influence of the financial sector on domestic enterprises in order to intensify their activities. The purpose of the article is defined, which is to analyze the approaches to determining the relationship and factors of interaction of banks on the enterprises of the real sector of the economy of Ukraine. The article analyzes the approaches to modeling the relationship between enterprises and banks in countries with developed market economies. Based on the analysis, the most studied links between the banking system and economic activity of enterprises are highlighted. The first group of such links includes bank deposits and loans, bank capital and the index of activity of the banking and financial sectors of the economy. The second group includes indicators of the resource sector (deposits) and investment in fixed assets, industrial output. The third group includes the need for credit resources and the capacity of the banking system of the country (region). The fourth group includes the need for investments, innovative resources and the need for development loans. Based on the analysis of literature sources, a functional model is proposed, which reflects the dependence of industrial output on deposit resources (or a model of the country’s credit potential), a model showing the dependence of loans for current needs on certain factors and a model of innovation and investment development. It is concluded that based on the use of the proposed functional dependencies, it is possible to show the relationship between the state of the banking system, deposits, the creditworthiness of the banking system and activity in the real sector of the economy. It is concluded that studies conducted in economically developed countries have confirmed the dependence of bank loans on the creditworthiness of banks. In addition, such studies have found a link between the growth rate of bank loans and the level of employment, output, and investment.

Is bank creditworthiness associated with risk disclosure behavior? Evidence from Islamic and conventional banks in emerging countries

We investigated if banks with higher risk disclosure scores benefitted from higher credit ratings relative to banks with weaker risk disclosure scores. In addition, we investigated if Islamic banks could exert positive effects on their credit ratings. By employing a sample of 169 banks observed over the period from 2009 to 2014 and after controlling for bank-specific risk characteristics, we documented banks with high credit rating scores were keen to preserve an upper level of credit risk transparency when compared to banks with lower credit ratings. Moreover, the positive association between credit rating and risk disclosure is more pronounced for conventional banks than for Islamic banks. When compared to conventional banks, Islamic banks disclose less risk information due to the risk nature of their businesses, which affect in turn their credit ratings.

Split credit ratings of banks in times of crisis

This paper analyses whether opacity of bank creditworthiness increases during crisis periods and if the conservativeness of CRAs changes through business cycles. Univariate and multivariate methodologies are used: data from Moody's and S&P on credit ratings and watch status for 133 commercial banks across 17 developed countries from 2007 to 2015 is employed. The univariate analysis is a unique technique that provides a new perspective to assess whether splits between CRAs are defined as permanent or temporary. The evidence demonstrates that Moody's and S&P frequently disagree. S&P is shown to be the more conservative CRA overall, however, the extent to which Moody's issues higher ratings decreases over time until it becomes the more conservative CRA. The paper is the first of its kind to establish that the conservativeness of Moody's and S&P changes throughout business cycles, which should impact on the strategic decision making of investors.

Split credit ratings of banks in times of crisis

This paper analyses whether opacity of bank creditworthiness increases during crisis periods and if the conservativeness of CRAs changes through business cycles. Univariate and multivariate methodologies are used: data from Moody's and S&P on credit ratings and watch status for 133 commercial banks across 17 developed countries from 2007 to 2015 is employed. The univariate analysis is a unique technique that provides a new perspective to assess whether splits between CRAs are defined as permanent or temporary. The evidence demonstrates that Moody's and S&P frequently disagree. S&P is shown to be the more conservative CRA overall, however, the extent to which Moody's issues higher ratings decreases over time until it becomes the more conservative CRA. The paper is the first of its kind to establish that the conservativeness of Moody's and S&P changes throughout business cycles, which should impact on the strategic decision making of investors.

Do Private Signals of a Bank's Creditworthiness Predict the Bank's CDS Price? Evidence from the Eurosystem's Overnight Loan Rates

We investigate the between the daily average interbank overnight borrowing rate (AOR) and the credit default swap price (CDS) of 60 banks using the Eurosystem's proprietary data from mid-2008 to mid-2013. We find that the AOR which is observable only by the competent Eurosystem authorities leads the CDS at least by one day. The lead was concentrated on days of market stress for banks which mainly borrow from relationship lender banks. Such borrower banks are typically smaller, have weak ratings, and likely reside in crisis countries.

‘Maastricht Plus’: Managing the Logic of Inherent Imperfections

Seen from the perspective of the interconnected excessive imbalances, banking, and sovereign debt crises that beset the Euro Area from 2007, the central inadequacy of the Maastricht Treaty was the failure to address the problem of the inherent imperfections of any provisions for crisis prevention and management in Economic and Monetary Union. The inadequacy stemmed from a cluster of naïve assumptions: about state capacity to comply with Treaty-based rules to prevent crises; about the tractability of economic and political cultures; about the adequacy of moral hazard as a design principle for monetary union; and about putting in place monetary union without economic, fiscal, banking, and political union. Consequently, managing crises of sovereign and bank creditworthiness had not been a problem on the Maastricht agenda. Subsequent failures to be prepared to manage compound crises were compounded by the complacency induced by the ‘political economy of good times’ into which the Euro Area was launched. EMU posed questions about the dominant teleological assumption underpinning the Maastricht Treaty. It risked exposing not just the limits of Europeanization but also serving as a catalyst for ‘de-Europeanization’.

A logical analysis of banks’ financial strength ratings

We evaluate the creditworthiness of banks using statistical, as well as combinatorics-, optimization-, and logic-based methodologies. We reverse-engineer the Fitch risk ratings of banks using ordered logistic regression, support vector machine, and Logical Analysis of Data (LAD). The LAD ratings are shown to be the most accurate and most successfully cross-validated. The study shows that the LAD rating approach is (i) objective, (ii) transparent, and (iii) generalizable. It can be used to build internal rating systems that (iv) have varying levels of granularity, and (v) are Basel compliant, allowing for their use in the decisions pertaining to the determination of the amount of regulatory capital.

The Design of Government Guarantees for Bank Bonds

In 2010 authorities have taken the first steps to end some of the public support measures put in place in response to the financial crisis, starting with government guarantees for bond issues. Financial institutions have made extensive use of this tool, which has been effective in avoiding a further tightening of funding conditions, but this type of public support has, nonetheless, raised some concerns. First, the cost of issuing guaranteed bonds has mainly reflected the characteristics of the sovereign guarantor rather than those of the issuer, thus favouring borrowers with a strong sovereign backing. This situation has the potential to distort competition and create incentives for excessive risk taking. Such effects could have been reduced by the choice of a different fee determination mechanism. Second, the continued availability in 2010 of guarantee schemes, despite a declining overall usage, may be alleviating the pressure on some weak financial institutions to address their weaknesses: the average creditworthiness of banks issuing after mid-2009, when market conditions became more favourable, has sharply declined. JEL Classification: G01, G12, G21, G28. Keywords: financial crisis, policy response to the crisis, government guaranteed bonds, competitive distortions

Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey

This paper presents a comprehensive review of 196 studies which employ operational research (O.R.) and artificial intelligence (A.I.) techniques in the assessment of bank performance. Several key issues in the literature are highlighted. The paper also points to a number of directions for future research. We first discuss numerous applications of data envelopment analysis which is the most widely applied O.R. technique in the field. Then we discuss applications of other techniques such as neural networks, support vector machines, and multicriteria decision aid that have also been used in recent years, in bank failure prediction studies and the assessment of bank creditworthiness and underperformance.

Assessing Bank Performance with Operational Research and Artificial Intelligence Techniques: A Survey

This paper presents a comprehensive review of 179 studies which employ operational research (O.R.) and Artificial Intelligence (A.I.) techniques in the assessment of bank performance. We first discuss numerous applications of data envelopment analysis which is the most widely applied O.R. technique in the field. Then we discuss applications of other techniques such as neural networks, support vector machines, and multicriteria decision aid that have also been used in recent years, in bank failure prediction studies and the assessment of bank creditworthiness and underperformance.

A Logical Analysis of Banks’ Financial Strength Ratings

We evaluate the creditworthiness of banks using statistical, as well as combinatorics-, optimization-, and logic-based methodologies. We reverse-engineer the Fitch risk ratings of banks using ordered logistic regression, support vector machine, and Logical Analysis of Data (LAD). The LAD ratings are shown to be the most accurate and most successfully cross-validated. The study shows that the LAD rating approach is (i) objective, (ii) transparent, and (iii) generalizable. It can be used to build internal rating systems that (iv) have varying levels of granularity, and (v) are Basel 2 compliant, allowing for their use in the decisions pertaining to the determination of the amount of regulatory capital.

Limited liability and bank safety net procedures∗

A model is presented of bank behaviour which identifies the factors determining a bank's optimal capital/asset ratio, its optimal liquidity ratio, the expected value of non-performing loans and the probability of bank failure. We propose that this last variable can act as an index of bank credit-worthiness. The main factors determining this index are (i) the risk associated with bank asset returns, (ii) the variability of bank deposits, (iii) the costs associated with bank failure and (iv) the implicit or explicit government subsidy involved in depositor protection schemes. The principal general conclusion of the paper is that regulations governing capital requirements, liquidity requirements and depositor protection should be (a) risk related and (b) integrated. Depositor protection can be improved through relatively high capital requirements. However, the optimal strategy is for all bank safety net procedures and incentive mechanisms to be related to the riskiness of individual bank portfolios.