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"CREDIT ANALYSIS"
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Artificial intelligence and bank credit analysis: A review
This article teases out the ramifications of artificial intelligence (AI) use in the credit analysis process by banks and other financing institutions. The unique features of AI models, coupled with the expansion of computing power, make new sources of information (big data) available for creditworthiness assessments. Combined, the use of AI and big data can capture weak signals, whether in the form of interactions or non-linearities between explanatory variables that appear to yield prediction improvements over conventional measures of creditworthiness. At the macroeconomic level, this translates into positive estimates for economic growth. On a micro scale, instead, the use of AI in credit analysis improves financial inclusion and access to credit for traditionally underserved borrowers. However, AI-based credit analysis processes raise enduring concerns due to potential biases and ethical, legal, and regulatory problems. These limits call for the establishment of a new generation of financial regulation introducing the certification of AI algorithms and of data used by banks.
Journal Article
Financial Borrowing by Local State-Owned Enterprises in Serbia: An Assessment of National Practice
Serbian local state-owned enterprises (SOEs) owed in excess of EUR 220mn in late 2015, with estimates reaching a much higher figure. According to the national Fiscal Council, underinvestment by local governments amounted to some EUR 250mn annually. This paper looks at insufficient commercial borrowing by local SOEs trying to identify the causes of this financing gap by looking at two aspects: on one hand, we look at quantitative and qualitative inputs provided by local SOEs for credit analysis that may cause significant information asymmetries, and, on the other, we consider the possibility that bank credit analyses, even if done properly, could reveal that these firms are unable to borrow from banks. The research has revealed that the length and efficiency of the bank credit approval process is dictated by: the need to properly organise qualitative and quantitative SOE information and ensure that it reflects the actual state of affairs; the poor quality of financial statements of SOEs and their pro forma annual business planning and reporting; a common lack of appropriate revaluation of future income; and an existent drawback related to ownership over fixed assets that are considered as a public property in Serbia (rather than as a property of the SOE that uses them). On the other hand, banks do not distinguish sufficiently between private firms and SOEs. This does not allow banks to account for issues specific to SOEs such as the spillover of fiscal risk, corporate governance, relationships between the owner and its SOEs, economic and social objectives, and the like. The frequent inability of local SOEs to provide mortgages as collateral, coupled with the restriction on guarantees from local governments, nearly completely preclude lending for large-scale and long-term investment. We conclude that local SOEs have a limited access to finance due to information asymmetries caused by unsuitable qualitative and quantitative inputs made by SOEs in the credit analysis process. Nevertheless, appropriate credit analyses reveal that these companies can be able to borrow commercially, especially in lower amounts and at shorter maturities which could mitigate underinvestment by local SOEs.
Journal Article
Third International Conference on Credit Analysis and Risk Management
Held at Oakland University, School of Business Administration, Department of Accounting and Finance. This book provides a summary of state-of-the-art methods and research in the analysis of credit. As such, it offers very useful insights into this vital area of finance, which has too often been under-researched and little-taught in academia. Including an overview of processes that are utilized for estimating the probability of default and the loss given default for a wide array of debts, the book will also be useful in evaluating individual loans and bonds, as well as managing entire portfolios of such assets. Each chapter is written by authors who presented and discussed their contemporary research and knowledge at the Third International Conference on Credit Analysis and Risk Management, held on August 21-22, 2014 at the Department of Accounting and Finance, School of Business administration, Oakland University. This collection of writings by these experts in the field is uniquely designed to enhance the understanding of credit analysis in a fashion that permits a broad perspective on the science and art of credit analysis.
eBook
Quantitative credit portfolio management : practical innovations for measuring and controlling liquidity, spread, and issuer concentration risk
\"An innovative approach to post-crash credit portfolio management Credit portfolio managers traditionally rely on fundamental research for decisions on issuer selection and sector rotation. Quantitative researchers tend to use more mathematical techniques for pricing models and to quantify credit risk and relative value. The information found here bridges these two approaches. In an intuitive and readable style, this book illustrates how quantitative techniques can help address specific questions facing today's credit managers and risk analysts. A targeted volume in the area of credit, this reliable resource contains some of the most recent and original research in this field, which addresses among other things important questions raised by the credit crisis of 2008-2009. Divided into two comprehensive parts, Quantitative Credit Portfolio Management offers essential insights into understanding the risks of corporate bonds--spread, liquidity, and Treasury yield curve risk--as well as managing corporate bond portfolios. Presents comprehensive coverage of everything from duration time spread and liquidity cost scores to capturing the credit spread premium Written by the number one ranked quantitative research group for four consecutive years by Institutional Investor Provides practical answers to difficult question, including: What diversification guidelines should you adopt to protect portfolios from issuer-specific risk? Are you well-advised to sell securities downgraded below investment grade? Credit portfolio management continues to evolve, but with this book as your guide, you can gain a solid understanding of how to manage complex portfolios under dynamic events\"-- Provided by publisher.
Book
Proceedings of the Second International Conference on Credit Analysis and Risk Management
Credit risk plays a crucial role in most financial transactions in one form or another and therefore contributes to various different layers of economic activity. Three key elements in the analysis of credit risk can be distinguished, namely: (1) the lender-borrower relationship, which is at the core of the entire discussion on credit risk; (2) the pricing of credit risk in financial markets; and (3) the relevance of financial stability and regulation related to the occurrence of credit risk.
eBook
NERHF: a hybrid machine learning-driven efficient credit risk control framework
As a core part of the financial industry, credit operations are accompanied by significant risks. Therefore, accurate credit risk control is crucial for financial institutions’ lending decisions and overall risk management. In this paper, we propose a hybrid machine learning framework (Neural network-Ensemble learning-Reinforcement learning Hybrid Framework, NERHF) for efficient credit risk control. The framework utilizes neural network algorithms to extract features from credit data, enhancing the accuracy and robustness of credit risk prediction. Further, based on the extracted features, ensemble learning algorithms are employed for credit risk prediction. Finally, the improved deep reinforcement learning algorithm Pre-DDQN is applied to generate optimal credit risk control strategies for different combinations of key credit indicators, aiming to mitigate default risks. Experimental results show that NERHF demonstrates significant advantages in credit risk prediction, especially when using recurrent neural networks for feature extraction in conjunction with lightweight gradient boosting machine algorithms. Additionally, the Pre-DDQN algorithm outperforms comparative algorithms in credit risk control, highlighting its potential for practical applications.
Journal Article