Journal articles on the topic 'Credit scoring'
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Crook, J. N., D. E. Edelman, and L. C. Thomas. "Credit Scoring." Journal of the Operational Research Society 56, no. 9 (September 2005): 1003–5. http://dx.doi.org/10.1057/palgrave.jors.2602037.
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Xia, Yufei, Zijun Liao, Jun Xu, and Yinguo Li. "FROM CREDIT SCORING TO REGULATORY SCORING: COMPARING CREDIT SCORING MODELS FROM A REGULATORY PERSPECTIVE." Technological and Economic Development of Economy 28, no. 6 (December 15, 2022): 1954–90. http://dx.doi.org/10.3846/tede.2022.17045.
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Conventional credit scoring models evaluated by predictive accuracy or profitability typically serve the financial institutions and can hardly reflect their contribution on financial stability. To remedy this, we develop a novel regulatory scoring framework to quantify and compare the corresponding regulatory capital charge errors of credit scoring models. As an application of RegTech, the proposed framework considers the characteristic of example-dependence and costsensitivity in credit scoring, which is expected to enhance the ability of risk absorption of financial institutions and thus benefit the regulators. Validated on two real-world credit datasets, empirical results reveal that credit scoring models with good predictive accuracy or profitability do not necessarily provide low capital charge requirement error, which further highlights the importance of regulatory scoring framework. The family of gradient boosting decision tree (GBDT) provides significantly better average performance than industry benchmarks and deep multilayer perceptron network, especially when financial stability is the primary focus. To further examine the robustness of the proposed regulatory scoring, sampling techniques, cut-off value modification, and probability calibration are employed within the framework and the main conclusions hold in most cases. Furthermore, the analysis on the interpretability via TreeSHAP algorithm alleviates the concerns on transparency of GBDT-based models, and confirms the important roles of loan characteristics, borrowers’ solvency and creditworthiness as powerful predictors in credit scoring. Finally, the managerial implications for both financial institutions and regulators are discussed.
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Purbayati, Radia, Muhammad Muflih, and Rosma Pakpahan. "Credit Scoring Modelling For Corporate Banking Institutions." Journal Integration of Management Studies 2, no. 1 (February 13, 2024): 18–28. http://dx.doi.org/10.58229/jims.v2i1.125.
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This research aims to build a credit scoring modeling simulation of bank corporate loans. The credit scoring model is used in assessing creditworthiness in credit decisions. This model determines whether or not a company is eligible for the corporate credit facility it proposes. Observations were made of 100 companies included in the list of Kompas100 Index formers on the Indonesia Stock Exchange (IDX) that have the potential to apply for loans/credits to Bank Financial Institutions (IKB) in optimizing the corporate capital structure through bank debt facilities in the period 2022. Analysis was conducted on five financial aspects consisting of 14 research variables, including (i) liquidity aspects, including current ratio and quick ratio variables; (ii) solvency aspects, including debt asset ratio and equity ratio variables; (iii) profitability aspects including return on net assets, operating profit ratio, price to earnings ratio variables, (iv) activity aspects including total asset turnover, accounts receivable turnover, inventory turnover, current assets turnover, and (v) growth aspects including operating income growth rate, total assets growth rate, and operating profit growth rate variables. The analysis tool uses Logistic Regression through an assessment conducted on the company's credit rating as a proxy for the dependent variable, worth one if the credit application is feasible and worth 0 if the credit application is not feasible with a cut-off point of 0.5. The results show that credit scoring modeling for corporate credit is significantly formed from liquidity (CR) and solvency (DER) aspects. Out of 61 companies classified as not eligible for credit facilities, 58 companies were classified correctly, and out of 39 companies classified as eligible, 29 companies were classified correctly. The overall percentage shows 68.0, meaning that the logistic regression model has an accuracy of 68%.
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Lanzarini, Laura Cristina, Augusto Villa Monte, Aurelio F. Bariviera, and Patricia Jimbo Santana. "Simplifying credit scoring rules using LVQ + PSO." Kybernetes 46, no. 1 (January 9, 2017): 8–16. http://dx.doi.org/10.1108/k-06-2016-0158.
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Purpose One of the key elements in the banking industry relies on the appropriate selection of customers. To manage credit risk, banks dedicate special efforts to classify customers according to their risk. The usual decision-making process consists of gathering personal and financial information about the borrower. Processing this information can be time-consuming, and presents some difficulties because of the heterogeneous structure of data. Design/methodology/approach This paper presents an alternative method that is able to generate rules that work not only on numerical attributes but also on nominal ones. The key feature of this method, called learning vector quantization and particle swarm optimization (LVQ + PSO), is the finding of a reduced set of classifying rules. This is possible because of the combination of a competitive neural network with an optimization technique. Findings These rules constitute a predictive model for credit risk approval. The reduced quantity of rules makes this method useful for credit officers aiming to make decisions about granting a credit. It also could act as an orientation for borrower’s self evaluation about her/his creditworthiness. Research limitations/implications In spite of the fact that conducted tests showed no evidence of dependence between results and the initial size of the LVQ network, it is considered desirable to repeat the measurements using an LVQ network of minimum size and a version of variable population PSO to adequately explore the solution space in the future. Practical implications In the past decades, there has been an increase in consumer credit. Retail banking is a growing industry. Not only has there been a boom in credit card memberships, specially in emerging economies, but also an increase in small consumption credits. For example, it is very common in emerging economies that families buy home appliances on installments. In those countries, the association of a home appliance shop with a financial institution is usual, to provide customers with quick-decision credit line facilities. The existence of such a financial instrument aids to boost sales. This association generates conflict of interests. On one hand, the home appliance shop wants to sell products to all customers. Therefore, it is in its best interest to promote a generous credit policy. On the other hand, the financial institution wants to maximize the revenue from credits, leading to a strict surveillance of loan losses. Having a fair and transparent credit-granting policy favors a good business relationship between home appliances shops and financial institutions. One way of developing such a policy is to construct objective rules to decide to grant or deny a credit application. Social implications Better credit decision rules generate enhanced risk sharing. In addition, it improves transparency in credit acceptance decisions, giving less room to arbitrary decisions. Originality/value This study develops a new method that combines a competitive neural network and an optimization technique. It was applied to a real database of a financial institution in a developing country.
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Berger, Allen N., and W. Scott Frame. "Small Business Credit Scoring and Credit Availability*." Journal of Small Business Management 45, no. 1 (January 1, 2007): 5–22. http://dx.doi.org/10.1111/j.1540-627x.2007.00195.x.
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Santana, Patricia Jimbo, Laura Lanzarini, and Aurelio F. Bariviera. "Fuzzy Credit Risk Scoring Rules using FRvarPSO." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 26, Suppl. 1 (December 2018): 39–57. http://dx.doi.org/10.1142/s0218488518400032.
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There is consensus that the best way for reducing insolvency situations in financial institutions is through good risk management, which involves a good client selection process. In the market, there are methodologies for credit scoring, each analyzing a large number of microeconomic and/or macroeconomic variables selected mostly depending on the type of credit to be granted. Since these variables are heterogeneous, the review process carried out by credit analysts takes time. The objective of this article is to propose a solution for this problem by applying fuzzy logic to the creation of classification rules for credit granting. To achieve this, linguistic variables were used to help the analyst interpret the information available from the credit officer. The method proposed here combines the use of fuzzy logic with a neural network and a variable population optimization technique to obtain fuzzy classification rules. It was tested with three databases from financial entities in Ecuador — one credit and savings cooperative and two banks that grant various types of credits. To measure its performance, three benchmarks were used: accuracy, number of classification rules generated, and antecedent length. The results obtained indicate that the hybrid model that is proposed performs better than its previous versions due to the addition of fuzzy logic. At the end of the article, our conclusions are discussed and future research lines are suggested.
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Medina, Rosa Puertas, and Maria Luisa Martí Selva. "Análisis del credit scoring." Revista de Administração de Empresas 53, no. 3 (June 2013): 303–15. http://dx.doi.org/10.1590/s0034-75902013000300007.
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El problema de la morosidad está cobrándose una gran importancia en los países desarrollados. En este trabajo realizamos un análisis de la capacidad predictiva de dos modelos paramétricos y uno no paramétrico abordando, en este último, el problema del sobreaprendizaje mediante la validación cruzada que, muy habitualmente, se obvia en este tipo de estudios. Además proponemos la distinción de tres tipos de solicitudes dependiendo de su probabilidad cumplimiento: conceder, no conceder (de forma automática), y dudoso y, por consiguiente, proceder a su estudio manual por parte del personal bancario.
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Velykoivanenko, Halyna, Svitlana Savina, Dmitriy Kolechko, and Vladyslav Ben’. "Building the ensembles of credit scoring models." Neuro-Fuzzy Modeling Techniques in Economics 7, no. 1 (April 10, 2019): 21–43. http://dx.doi.org/10.21511/nfmte.7.2018.02.
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The article is devoted to solving the actual problem of increasing the efficiency of assessing the credit risks of individual borrowers by finding the optimal combination of the results of calculations of specific scoring models. The principles of the formation of an ensemble of models are given and the existing approaches to the construction of ensemble structures are analyzed. In the process of experimental research has been applied one of the modifications of the boosting algorithm and implemented the author's algorithm for constructing an ensemble of models based on the specialization of experts. The radial-basis function neural networks were used as specific expert models. As a result of a comparative analysis of the efficiency of the used ensemble technologies it was confirmed that the algorithm for constructing an ensemble based on the specialization of experts proposed by the authors is the most adapted for the task of assessing credit risk.
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Silva, Fabio, and Cesar Analide. "Information asset analysis: credit scoring and credit suggestion." International Journal of Electronic Business 9, no. 3 (2011): 203. http://dx.doi.org/10.1504/ijeb.2011.042542.
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Madzova, Violeta, and Nehat Ramadini. "Can credit scoring models prevent default payments in the banking industry in the period of financial crisis?" International Journal of Business & Technology 2, no. 1 (May 2013): 32–38. http://dx.doi.org/10.33107/ijbte.2013.2.1.05.
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Credit scoring is a scientific method of assessing the credit risk associated with new credit applications as well as for monitoring of the credit risk in the process of the loan payment. Therefore credit scoring models developed in the banks based on their internal information system, as well as the credit info system in the country, can help the banks to ensure more consistent underwriting and can provide management with a more insightful measure of credit risk. While credit scoring could be a valuable risk management tool in virtually any bank setting, it is probably not wide accepted in the banks with more fundamental underwriting problems such as inexperienced loan officers, inadequate credit procedures, underdeveloped internal and external credit info – system , persistent arrears problems, etc. However credit scoring is only useful , and can make reliable predictions , if the credit scoring models are properly made, data base updated and credit scoring limitations are properly understood, which is very difficult in the turbulent economic environment as it is the world economy in the recent years. Therefore, the paper objective is to analyze if the banking industry can prevent credit default payments through the implementation of the credit scoring models and what are the parameters and the weights that need to be incorporated, so that the models to be more effective in the period of financial crises.
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Abhishek Kumar, Abhijeet Kumar, Aditya Kumar Singh, and Ms. Nikita. "Credit Scoring System Using Machine Learning." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 3 (May 28, 2024): 376–80. http://dx.doi.org/10.32628/cseit2410334.
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This research paper offers a comprehensive examination of credit scoring systems. We will explore the current methodologies employed, the challenges encountered, and potential avenues for improvement. Credit scoring plays a critical role in financial decision-making, impacting both individual access to credit and lender risk management strategies. The paper will analyze traditional credit scoring models alongside emerging trends designed to enhance the accuracy and fairness of credit assessments. Additionally, we will discuss the ethical considerations surrounding credit scoring and propose recommendations for future advancements.
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Onay, Ceylan, and Elif Öztürk. "A review of credit scoring research in the age of Big Data." Journal of Financial Regulation and Compliance 26, no. 3 (July 9, 2018): 382–405. http://dx.doi.org/10.1108/jfrc-06-2017-0054.
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Purpose This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring. Design/methodology/approach Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics. Findings The authors find that credit scoring is going through a quantitative transformation, where data-centric underwriting approaches, usage of non-traditional data sources in credit scoring and their regulatory aspects are the up-coming avenues for further research. Practical implications The paper’s findings highlight the perils and benefits of using Big Data in credit scoring algorithms for corporates, governments and non-profit actors who develop and use new technologies in credit scoring. Originality/value This paper presents greater insight on how Big Data challenges traditional credit scoring models and addresses the need to develop new credit models that identify new and secure data sources and convert them to useful insights that are in compliance with regulations.
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Aggarwal, Nikita. "THE NORMS OF ALGORITHMIC CREDIT SCORING." Cambridge Law Journal 80, no. 1 (March 2021): 42–73. http://dx.doi.org/10.1017/s0008197321000015.
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AbstractThis article examines the growth of algorithmic credit scoring and its implications for the regulation of consumer credit markets in the UK. It constructs a frame of analysis for the regulation of algorithmic credit scoring, bound by the core norms underpinning UK consumer credit and data protection regulation: allocative efficiency, distributional fairness and consumer privacy (as autonomy). Examining the normative trade-offs that arise within this frame, the article argues that existing data protection and consumer credit frameworks do not achieve an appropriate normative balance in the regulation of algorithmic credit scoring. In particular, the growing reliance on consumers’ personal data by lenders due to algorithmic credit scoring, coupled with the ineffectiveness of existing data protection remedies has created a data protection gap in consumer credit markets that presents a significant threat to consumer privacy and autonomy. The article makes recommendations for filling this gap through institutional and substantive regulatory reforms.
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Chacko, Annie. "Optimized algorithm for Credit Scoring." International Journal of Advanced Trends in Computer Science and Engineering 9, no. 1.3 (June 25, 2020): 361–65. http://dx.doi.org/10.30534/ijatcse/2020/5691.32020.
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Bono, Teresa, Karen Croxson, and Adam Giles. "Algorithmic fairness in credit scoring." Oxford Review of Economic Policy 37, no. 3 (September 1, 2021): 585–617. http://dx.doi.org/10.1093/oxrep/grab020.
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Abstract The use of machine learning as an input into decision-making is on the rise, owing to its ability to uncover hidden patterns in large data and improve prediction accuracy. Questions have been raised, however, about the potential distributional impacts of these technologies, with one concern being that they may perpetuate or even amplify human biases from the past. Exploiting detailed credit file data for 800,000 UK borrowers, we simulate a switch from a traditional (logit) credit scoring model to ensemble machine-learning methods. We confirm that machine-learning models are more accurate overall. We also find that they do as well as the simpler traditional model on relevant fairness criteria, where these criteria pertain to overall accuracy and error rates for population subgroups defined along protected or sensitive lines (gender, race, health status, and deprivation). We do observe some differences in the way credit-scoring models perform for different subgroups, but these manifest under a traditional modelling approach and switching to machine learning neither exacerbates nor eliminates these issues. The paper discusses some of the mechanical and data factors that may contribute to statistical fairness issues in the context of credit scoring.
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Sengupta, Rajdeep, and Geetesh Bhardwaj. "Credit Scoring and Loan Default." International Review of Finance 15, no. 2 (April 30, 2015): 139–67. http://dx.doi.org/10.1111/irfi.12048.
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Sewart, P. "Graphical models in credit scoring." IMA Journal of Management Mathematics 9, no. 3 (July 1, 1998): 241–66. http://dx.doi.org/10.1093/imaman/9.3.241.
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West, David. "Neural network credit scoring models." Computers & Operations Research 27, no. 11-12 (September 2000): 1131–52. http://dx.doi.org/10.1016/s0305-0548(99)00149-5.
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Paleologo, Giuseppe, André Elisseeff, and Gianluca Antonini. "Subagging for credit scoring models." European Journal of Operational Research 201, no. 2 (March 2010): 490–99. http://dx.doi.org/10.1016/j.ejor.2009.03.008.
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Finlay, Steven. "Credit scoring for profitability objectives." European Journal of Operational Research 202, no. 2 (April 2010): 528–37. http://dx.doi.org/10.1016/j.ejor.2009.05.025.
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Simonov, P. M., and E. V. Zboev. "Scoring Credit Rating of Individuals." Herald of Dagestan State University 35, no. 2 (2020): 18–26. http://dx.doi.org/10.21779/2542-0321-2020-35-2-18-26.
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Journal, IJSREM. "A Machine Learning Approach to Detect Credit Fraud." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 10 (October 1, 2023): 1–11. http://dx.doi.org/10.55041/ijsrem26214.
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Credit fraud poses a significant challenge in the financial sector, leading to substantial financial losses annually. Traditional credit scoring methods often fall short in accurately detecting fraudulent transactions. This research project aims to leverage machine learning algorithms to address this issue by developing intelligent credit scoring systems with a specific focus on credit fraud detection. The objectives include gathering and analyzing a diverse dataset, exploring and evaluating various machine learning algorithms, developing and optimizing models, and evaluating their performance. The study also emphasizes the importance of model interpretability, ethical considerations, and fairness in credit scoring. Through a systematic and objective approach, the research intends to bridge existing gaps in the field, advancing the development of accurate, comprehensible, fair, and practical credit fraud detection systems. The outcomes have the potential to transform credit scoring practices, enabling financial institutions to make informed decisions while mitigating the risk of fraud. Key Words: Credit Fraud Detection, Machine Learning Algorithms, Credit Scoring, Model Interpretability, Financial Fraud Prevention.
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Amat, Oriol, Raffaele Manini, and Marcos Antón Renart. "Credit concession through credit scoring: Analysis and application proposal." Intangible Capital 13, no. 1 (January 19, 2017): 51. http://dx.doi.org/10.3926/ic.903.
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Purpose: The study herein develops and tests a credit scoring model which can help financial institutions in assessing credit requests. Design/methodology/approach: The empirical study has the objective of answering two questions: (1) Which ratios better discriminate the companies based on their being solvent or insolvent? and (2) What is the relative importance of these ratios? To do this, several statistical techniques with a multifactorial focus have been used (Multivariate Analysis of Variance, Linear Discriminant Analysis, Logit and Probit Models). Several samples of companies have been used in order to obtain and to test the model. Findings: Through the application of several statistical techniques, the credit scoring model has been proved to be effective in discriminating between good and bad creditors. Research limitations: This study focuses on manufacturing, commercial and services companies of all sizes in Spain; Therefore, the conclusions may differ for other geographical locations.Practical implications: Because credit is one of the main drivers of growth, a solid credit scoring model can help financial institutions assessing to whom to grant credit and to whom not to grant credit.Social implications: Because of the growing importance of credit for our society and the fear of granting it due to the latest financial turmoil, a solid credit scoring model can strengthen the trust toward the financial institutions assessment’s. Originality/value: There is already a stream of literature related to credit scoring. However, this paper focuses on Spanish firms and proves the results of our model based on real data. The application of the model to detect the probability of default in loans is original.
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Wilhelmina Afua Addy, Adeola Olusola Ajayi-Nifise, Binaebi Gloria Bello, Sunday Tubokirifuruar Tula, Olubusola Odeyemi, and Titilola Falaiye. "AI in credit scoring: A comprehensive review of models and predictive analytics." Global Journal of Engineering and Technology Advances 18, no. 2 (February 28, 2024): 118–29. http://dx.doi.org/10.30574/gjeta.2024.18.2.0029.
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This review provides a succinct overview of the comprehensive review exploring the integration of Artificial Intelligence (AI) in credit scoring. The analysis delves into diverse AI models and predictive analytics shaping the contemporary landscape of credit assessment. The review begins by examining the historical context of credit scoring and progresses through the transformative impact of AI on traditional credit assessment methodologies. It scrutinizes various AI models employed in credit scoring, ranging from machine learning algorithms to advanced predictive analytics. Emphasis is placed on elucidating the strengths and limitations of each model, considering factors such as interpretability, accuracy, and scalability. The evolution of credit scoring is discussed, emphasizing the transition from rule-based systems to sophisticated AI-driven approaches. The integration of alternative data sources, such as social media and unconventional financial indicators, is explored, showcasing the expanding scope of AI in capturing a more holistic view of an individual's creditworthiness. The Review underscores the significance of predictive analytics in credit scoring, outlining the nuanced techniques used to forecast credit risk. It elucidates the role of explainable AI, addressing the need for transparency in complex credit scoring models, especially in the context of regulatory compliance and consumer trust. Furthermore, the review highlights the real-world implications of AI in credit scoring, discussing its impact on financial inclusion, risk management, and decision-making processes. The ethical considerations and potential biases associated with AI models are explored, shedding light on the importance of fairness and responsible AI practices in the credit industry. In conclusion, this comprehensive review navigates the intricate landscape of AI in credit scoring, offering a holistic understanding of the models and predictive analytics that underpin modern credit assessment. The synthesis of historical perspectives, model intricacies, and real-world implications makes this review an essential resource for practitioners, researchers, and policymakers in the ever-evolving domain of AI-driven credit evaluation.
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Seleznyova, Zinaida. "Applying Complementary Credit Scores to Calculate Aggregate Ranking." Journal of Corporate Finance Research / Корпоративные Финансы | ISSN: 2073-0438 15, no. 3 (September 15, 2021): 5–12. http://dx.doi.org/10.17323/j.jcfr.2073-0438.15.3.2021.5-13.
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Researchers have been improving credit scoring models for decades, as an increase in the predictive ability of scoring even by a small amount can allow financial institutions to avoid significant losses. Many researchers believe that ensembles of classifiers or aggregated scorings are the most effective. However, ensembles outperform base classifiers by thousandths of a percent on unbalanced samples.
This article proposes an aggregated scoring model. In contrast to previous models, its base classifiers are focused on identifying different types of borrowers. We illustrate the effectiveness of such scoring aggregation on real unbalanced data.
As the effectiveness indicator we use the performance measure of the area under the ROC curve. The DeLong, DeLong and Clarke-Pearson test is used to measure the statistical difference between two or more areas. In addition, we apply a logistic model of defaults (logistic regression) to the data of company financial statements. This model is usually used to identify default borrowers. To obtain a scoring aimed at non-default borrowers, we employ a modified Kemeny median, which was initially developed to rank companies with credit ratings. Both scores are aggregated by logistic regression.
Our data Russian banks that existed or defaulted between July 1, 2010, and July 1, 2015. This sample of banks is highly unbalanced, with a concentration of defaults of about 5%. The aggregation was carried out for banks with several ratings.
We show that aggregated classifiers based on different types of information significantly improve the discriminatory power of scoring even on an unbalanced sample. Moreover, the absolute value of this improvement surpasses all the values previously obtained from unbalanced samples.
The aggregated scoring and the approach to its construction can be applied by financial institutions to credit risk assessment and as an auxiliary tool in the decision-making process thanks to the relatively high interpretability of the scores.
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Hohnen, Pernille, Michael Alexander Ulfstjerne, and Mathias Sosnowski Krabbe. "Assessing Creditworthiness in the Age of Big Data." Journal of Extreme Anthropology 5, no. 1 (June 20, 2021): 29–55. http://dx.doi.org/10.5617/jea.8315.
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The purpose of this article is twofold: first, we show how algorithms have become increasingly central to financial credit scoring; second, we draw on this to further develop the anthropological study of algorithmic governance. As such, we describe the literature on credit scoring and then discuss ethnographic examples from two regulatory and commercial contexts: the US and Denmark. From these empirical cases, we carve out main developments of algorithmic governance in credit scoring and elucidate social and cultural logics behind algorithmic governance tools. Our analytical framework builds on critical algorithm studies and anthropological studies where money and payment infrastructures are viewed as embedded in their specific cultural contexts (Bloch and Parry 1989; Maurer 2015). The comparative analysis shows how algorithmic credit scoring takes different forms hence raising different issues in the two cases. Danish banks seem to have developed a system of intensive, yet hidden credit scoring based on surveillance and harvesting of behavioural data, which, however, due to GDPR takes place in restricted silos. Credit scores are hidden to customers, and therefore there has been virtually no public debate regarding the algorithmic models behind scores. In the US, fewer legal restrictions on data trading combined with both widespread and visible credit scoring has led to the development of a credit data market and widespread use of credit scoring by ‘affiliation’ on the one hand, but also to increasing public and political critique on scoring models on the other.
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Sun, Jie, Hui Li, Pei-Chann Chang, and Qing-Hua Huang. "Dynamic credit scoring using B & B with incremental-SVM-ensemble." Kybernetes 44, no. 4 (April 7, 2015): 518–35. http://dx.doi.org/10.1108/k-02-2014-0036.
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Purpose – Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring. Design/methodology/approach – This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring. Findings – The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model. Originality/value – This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.
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Ruddy, Thomas Fay. "Anglo-American Credit Scoring and Consumer Debt in the Subprime Mortgage Crisis of 2007 as Models for Other Countries?" tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 8, no. 2 (August 28, 2010): 275–84. http://dx.doi.org/10.31269/triplec.v8i2.176.
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The system of credit scoring has been built up in recent times on the basis of a compromise struck between individuality and surveillance in ways that boosted consumption through consumer debt. This paper considers the role of credit scoring in the recent financial crisis, concluding that even if credit scoring had worked as intended under its own terms, the practice would not have been enough to limit the defaulting of mortgage borrowers under conditions of falling house prices. The broader economic problem is the crippling amount of consumer debt involved; hence the paper places credit scoring in the larger explanatory framework of consumer debt and, more generally, consumerism in its more problematic form. The sorting accomplished by credit scoring is open to abuses in the marketplace, unless it is tempered by the application of guardrails by a regulatory authority. As cultures beyond the Anglo-American sphere adopt this practice, guardrails like those applied to credit scoring in America are needed; as even the current U.S. Administration has acknowledged, the existing guardrails will have to be complemented by stricter social standards. The paper provides a derivation of the theoretical background on credit scoring as surveillance, and includes a literature survey.
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Ruddy, Thomas Fay. "Anglo-American Credit Scoring and Consumer Debt in the Subprime Mortgage Crisis of 2007 as Models for Other Countries?" tripleC: Communication, Capitalism & Critique. Open Access Journal for a Global Sustainable Information Society 8, no. 2 (August 28, 2010): 275–84. http://dx.doi.org/10.31269/vol8iss2pp275-284.
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The system of credit scoring has been built up in recent times on the basis of a compromise struck between individuality and surveillance in ways that boosted consumption through consumer debt. This paper considers the role of credit scoring in the recent financial crisis, concluding that even if credit scoring had worked as intended under its own terms, the practice would not have been enough to limit the defaulting of mortgage borrowers under conditions of falling house prices. The broader economic problem is the crippling amount of consumer debt involved; hence the paper places credit scoring in the larger explanatory framework of consumer debt and, more generally, consumerism in its more problematic form. The sorting accomplished by credit scoring is open to abuses in the marketplace, unless it is tempered by the application of guardrails by a regulatory authority. As cultures beyond the Anglo-American sphere adopt this practice, guardrails like those applied to credit scoring in America are needed; as even the current U.S. Administration has acknowledged, the existing guardrails will have to be complemented by stricter social standards. The paper provides a derivation of the theoretical background on credit scoring as surveillance, and includes a literature survey.
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de Paula, Daniel Abreu Vasconcellos, Rinaldo Artes, Fabio Ayres, and Andrea Maria Accioly Fonseca Minardi. "Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques." RAUSP Management Journal 54, no. 3 (July 8, 2019): 321–36. http://dx.doi.org/10.1108/rausp-03-2018-0003.
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Purpose Although credit unions are nonprofit organizations, their objectives depend on the efficient management of their resources and credit risk aligned with the principles of the cooperative doctrine. This paper aims to propose the combined use of credit scoring and profit scoring to increase the effectiveness of the loan-granting process in credit unions. Design/methodology/approach This sample is composed by the data of personal loans transactions of a Brazilian credit union. Findings The analysis reveals that the use of statistical methods improves significantly the predictability of default when compared to the use of subjective techniques and the superiority of the random forests model in estimating credit scoring and profit scoring when compared to logit and ordinary least squares method (OLS) regression. The study also illustrates how both analyses can be used jointly for more effective decision-making. Originality/value Replacing subjective analysis with objective credit analysis using deterministic models will benefit Brazilian credit unions. The credit decision will be based on the input variables and on clear criteria, turning the decision-making process impartial. The joint use of credit scoring and profit scoring allows granting credit for the clients with the highest potential to pay debt obligation and, at the same time, to certify that the transaction profitability meets the goals of the organization: to be sustainable and to provide loans and investment opportunities at attractive rates to members.
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Idczak, Adam Piotr. "Remarks on Statistical Measures for Assessing Quality of Scoring Models." Acta Universitatis Lodziensis. Folia Oeconomica 4, no. 343 (September 13, 2019): 21–38. http://dx.doi.org/10.18778/0208-6018.343.02.
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Granting a credit product has always been at the heart of banking. Simultaneously, banks are obligated to assess the borrower’s credit risk. Apart from creditworthiness, to grant a credit product, banks are using credit scoring more and more often. Scoring models, which are an essential part of credit scoring, are being developed in order to select those clients who will repay their debt. For lenders, high effectiveness of selection based on the scoring model is the primary attribute, so it is crucial to gauge its statistical quality. Several textbooks regarding assessing statistical quality of scoring models are available, there is however no full consistency between names and definitions of particular measures. In this article, the most common statistical measures for assessing quality of scoring models, such as the pseudo Gini index, Kolmogorov‑Smirnov statistic, and concentration curve are reviewed and their statistical characteristics are discussed. Furthermore, the author proposes the application of the well‑known distribution similarity index as a measure of discriminatory power of scoring models. The author also attempts to standardise names and formulas for particular measures in order to finally contrast them in a comparative analysis of credit scoring models.
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Chen, Keqin, Amit Yadav, Asif Khan, and Kun Zhu. "Credit Fraud Detection Based on Hybrid Credit Scoring Model." Procedia Computer Science 167 (2020): 2–8. http://dx.doi.org/10.1016/j.procs.2020.03.176.
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Saraswati, Mei Sita, Moch Abdul Mukid, and Abdul Hoyyi. "METODE GENERALIZED MEAN DISTANCE-BASED K-NEAREST NEIGHBOR CLASSIFIER (GMDKNN) UNTUK ANALISIS CREDIT SCORING CALON DEBITUR KREDIT TANPA AGUNAN (KTA)." Jurnal Gaussian 8, no. 1 (February 28, 2019): 149–60. http://dx.doi.org/10.14710/j.gauss.v8i1.26629.
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Unsecured Credit is one of the credit facilities provided by banks, where the prospective debtor can borrow some amount of fund from the bank without having to provide collateral. Credit scoring is a process that aims to assess the worthiness of credit applications and classify the credit applicants into prospective debtors whose the credit application is worthy to be accepted and prospective debtors whose the credit application should be rejected. One of the statistical methods that can be applied in examining the analysis of credit scoring is the Generalized Mean Distance-Based k-Nearest Neighbor (GMDKNN) classifier. Empirical study on this method uses 23,337 data of prospective debtor of unsecured credit in 2018, with the dependent variable being the credit scoring final decision and seven independent variables, i.e. age, child dependent, length of employment, age of the company, income, loan proposed, and duration of credit. Based on the feature selection test, all independent variables are significantly taking effect on the credit scoring final decision. The best classification model is obtained in the parameters k = 137 and p = -1 with the classification performance metrics represented by the values of APER = 0,2580, accuracy = 74,20%, sensitivity = 0,6083, specificity = 0,8393, AUC = 0,7238, and G-Mean = 0,7146.Keywords: Unsecured Credit, credit scoring, classification, Generalized Mean Distance-Based k-Nearest Neighbor (GMDKNN).
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Shen, Feng, Run Wang, and Yu Shen. "A COST-SENSITIVE LOGISTIC REGRESSION CREDIT SCORING MODEL BASED ON MULTI-OBJECTIVE OPTIMIZATION APPROACH." Technological and Economic Development of Economy 26, no. 2 (November 27, 2019): 405–29. http://dx.doi.org/10.3846/tede.2019.11337.
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Credit scoring is an important process for peer-to-peer (P2P) lending companies as it determines whether loan applicants are likely to default. The aim of most credit scoring models is to minimize the classification error rate, which implies that all classification errors bear the same cost; however, in reality, there is a significant cost-sensitive problem in credit scoring methods. Therefore, in this paper, a new cost-sensitive logistic regression credit scoring model based on a multi-objective optimization approach is proposed that has two objectives in the cost-sensitive logistic regression process. The cost-sensitive logistic regression parameters are solved using a multiple objective particle swarm optimization (MOPSO) algorithm. In the empirical analysis, the proposed model was applied to the credit scoring of a Chinese famous P2P company, from which it was found that compared with other common credit scoring models, the proposed model was able to effectively reduce type II error rates and total classification error costs, and improve the AUC, the F1 values (reconciliation average of Recall and Precision), and the G-means. The proposed model was compared with other multi-objective optimization algorithms to further demonstrate that MOPSO is the best approach for cost-sensitive logistic regression credit scoring models.
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Taiwo Inumidun Adegoke, Onyeka Chrisanctus Ofodile, Nneka Adaobi Ochuba, and Olatunji Akinrinola. "Evaluating the fairness of credit scoring models: A literature review on mortgage accessibility for under-reserved populations." GSC Advanced Research and Reviews 18, no. 3 (March 30, 2024): 189–99. http://dx.doi.org/10.30574/gscarr.2024.18.3.0104.
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This literature review critically evaluates the fairness of credit scoring models concerning mortgage accessibility for under-reserved populations. The review scrutinizes a diverse range of scholarly articles, reports, and empirical studies spanning various disciplines, including finance, economics, sociology, and public policy. It examines the methodologies, findings, and limitations of existing research to illuminate the multifaceted dimensions of credit scoring fairness and its implications for mortgage accessibility. Firstly, the review outlines the conceptual framework of credit scoring fairness, emphasizing the importance of equality, transparency, and accountability in credit assessment processes. It explores the evolution of credit scoring models and their impact on mortgage lending practices, particularly for historically marginalized communities such as racial minorities, low-income households, and individuals with limited credit histories. Secondly, the review analyzes the methodologies employed in evaluating the fairness of credit scoring models. It identifies key metrics and statistical techniques used to assess disparities in mortgage approval rates, interest rates, and loan terms across demographic groups. Thirdly, the review synthesizes empirical evidence on the extent and persistence of disparities in mortgage accessibility for under-reserved populations. It highlights systemic barriers, including discriminatory lending practices, redlining, and institutionalized biases embedded within credit scoring models. Fourthly, the review discusses the implications of credit scoring fairness for financial inclusion, social equity, and economic mobility. It underscores the need for innovative policy interventions, industry best practices, and consumer education initiatives to address systemic inequities in mortgage lending and promote inclusive homeownership opportunities for under-reserved, Finally, this literature review offers a comprehensive overview of the fairness of credit scoring models in the context of mortgage accessibility for under-reserved populations. By synthesizing empirical evidence, theoretical frameworks, and policy implications, it contributes to a deeper understanding of the challenges and opportunities in promoting equitable access to homeownership and financial security for all.
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Li, Xinyi. "Optimization Strategy of Credit Scoring System based on Support Vector Machine." Advances in Engineering Technology Research 9, no. 1 (January 25, 2024): 558. http://dx.doi.org/10.56028/aetr.9.1.558.2024.
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This article proposes a novel optimization strategy for credit scoring systems that exploits the capabilities of SVM. Focusing on the importance of personal credit scoring in today's credit dynamics, the article explores SVM's versatility in various domains through a literature review. The theoretical background underscores the unique approach and computational efficiency of SVM. The optimization strategy encompasses four critical aspects: debt solvency, earning potential, operational prowess, and growth capability using metrics such as asset-liability ratios. Experimental validation with credit card datasets from Australia and Germany illustrates the nuanced relationship between different K-values and performance metrics, and demonstrates the adaptability of SVM in improving credit scoring. In short, the article presents an original, comprehensive approach to credit risk management that integrates theoretical foundations, literature findings, and empirical experiments to improve the accuracy of credit scoring in the dynamic economic landscaper.
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Fata, Haris Diyaul, Gita Indah Marthasari, and Yufis Azhar. "Sistem Pendukung Keputusan Kelayakan Kredit pada PT. BPR Mitra Catur Mandiri Menggunakan Metode Credit Scoring." Jurnal Repositor 2, no. 5 (March 11, 2020): 649. http://dx.doi.org/10.22219/repositor.v2i5.608.
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Abstrak Kredit adalah suatu cara yang dapat dilakukan untuk mendapatkan modal usaha. Tetapi terkadang pihak bank mengalami kesulitan dalam melakukan penentuan kredit, hal ini dikarenakan terdapat beberapa kriteria yang tidak terpenuhi oleh calon nasabah. Maka dibutuhkan suatu sistem yang dapat mempermudah petugas bank dalam melakukan penentuan kelayakan kredit, yaitu dengan membangun sistem pendukung keputusan kelayakan kredit menggunakan metode credit scoring. Dalam penentuan kredit, metode credit scoring melakukan perhitungan berdasarkan kriteria-kriteria yang ada, sehingga dapat dihasilkan rekomendasi diterima atau ditolaknya sebuah pengajuan kredit. Berdasarkan penelitian yang telah dilakukan, hasil pembuatan sistem pedukung keputusan kelayakan kredit menggunakan metode credit scoring ini adalah mempermudah petugas bank dalam melakukan penentuan kredit. Berdasarkan pengujian menggunakan metode confusion matrix, sistem ini mempunyai performa yang sangat baik dengan tinggkat akurasi 93%.Abstract Credit is a way that can be done to obtain business capital. But sometimes the bank has difficulty in determining credit, this is because there are several criteria that are not met by prospective customers. Then a system is needed to facilitate bank officers in determining credit worthiness, namely by building a creditworthiness decision support system using the credit scoring method. In determining credit, the credit scoring method calculates based on existing criteria, so that recommendations can be generated or rejected for a credit proposal. Based on the research that has been done, the results of the creation of a support system for credit feasibility decisions using the credit scoring method is to facilitate bank officers in making credit determinations. Based on testing using the Confusion Matrix method, this system has a very good performance with an accuracy rate of 93%.
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Barhdadi, M., B. Benyacoub, and M. Ouzineb. "A hybrid variable neighborhood search with bootstrap resampling technique for credit scoring problem." Mathematical Modeling and Computing 11, no. 1 (2024): 109–19. http://dx.doi.org/10.23939/mmc2024.01.109.
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Credit scoring models have played a vitally important role in the granting credit by lenders and financial institutions. Recently, these have gained more attention related to the risk management practice. Many modeling techniques have been developed to evaluate the worthiness of borrowers. This paper presents a credit scoring model via one of local search methods – variable neighborhood search (VNS) algorithm. The optimizing VNS neighborhood structure is a useful method applied to solve credit scoring problems. By simultaneously tuning the neighborhood structure, the proposed algorithm generates optimized weights which are used to build a linear discriminant function. The experimental results obtained by applying this model on simulated and real datasets prove its high efficiency and evaluate its significant value on credit scoring.
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Shih, Dong-Her, Ting-Wei Wu, Po-Yuan Shih, Nai-An Lu, and Ming-Hung Shih. "A Framework of Global Credit-Scoring Modeling Using Outlier Detection and Machine Learning in a P2P Lending Platform." Mathematics 10, no. 13 (June 29, 2022): 2282. http://dx.doi.org/10.3390/math10132282.
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A great challenge for credit-scoring models in online peer-to-peer (P2P) lending platforms is that credit-scoring models simply discard rejected applicants. This selective discard can lead to an inability to increase the number of potentially qualified applicants, ultimately affecting the revenue of the lending platform. One way to deal with this is to employ reject inference, a technique that infers the state of a rejected sample and incorporates the results into a credit-scoring model. The most popular approach to reject inference is to use a credit-scoring model built only on accepted samples to directly predict the status of rejected samples. However, the distribution of accepted samples in online P2P lending is different from the distribution of rejected samples, and the credit-scoring model on the original accepted sample may no longer apply. In addition, the acceptance sample may also include applicants who cannot repay the loan. If these applicants can be filtered out, the losses to the lending platform can also be reduced. Therefore, we propose a global credit-scoring model framework that combines multiple feature selection methods and classifiers to better evaluate the model after adding rejected samples. In addition, this study uses outlier detection methods to explore the internal relationships of all samples, which can delete outlier applicants in accepted samples or increase outlier applicants in rejected samples. Finally, this study uses four data samples and reject inference to construct four different credit-scoring models. The experimental results show that the credit-scoring model combining Pearson and random forest proposed in this study has significantly better accuracy and AUC than other scholars. Compared with previous studies, using outlier detection to remove outliers in loan acceptance samples and identify potentially creditworthy loan applicants from loan rejection samples is a good strategy. Furthermore, this study not only improves the accuracy of the credit-scoring model but also increases the number of lenders, which in turn increases the profitability of the lending platform.
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Stąpor, Katarzyna, Tomasz Smolarczyk, and Piotr Fabian. "Heteroscedastic discriminant analysis combined with feature selection for credit scoring." Statistics in Transition new series 17, no. 2 (June 1, 2016): 265–80. http://dx.doi.org/10.59170/stattrans-2016-014.
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Credit granting is a fundamental question and one of the most complex tasks that every credit institution is faced with. Typically, credit scoring databases are often large and characterized by redundant and irrelevant features. An effective classification model will objectively help managers instead of intuitive experience. This study proposes an approach for building a credit scoring model based on the combination of heteroscedastic extension (Loog, Duin, 2002) of classical Fisher Linear Discriminant Analysis (Fisher, 1936, Krzyśko, 1990) and a feature selection algorithm that retains sufficient information for classification purpose. We have tested five feature subset selection algorithms: two filters and three wrappers. To evaluate the accuracy of the proposed credit scoring model and to compare it with the existing approaches we have used the German credit data set from the study (Chen, Li, 2010). The results of our study suggest that the proposed hybrid approach is an effective and promising method for building credit scoring models.
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Nur Salima, Tsania, Mukaram, and Azizah Sri Aulia Aripin. "ANALYSIS OF THE EFFECT OF MANAGEMENT OF RECEIVABLES RISK WITH CREDIT SCORING ON CREDIT COLLECTIBILITY." Applied Business and Administration Journal 3, no. 1 (January 31, 2024): 26–38. http://dx.doi.org/10.62201/abaj.v3i1.80.
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A good management of account receivable is needed to encourage company revenues to be able to fulfill financing needs. One way to manage receivables is to do credit scoring. This research was conducted to examine the effect, direction, and significance of credit scoring on credit collectability at PT Krakatau Steel in 2022 to 2023. In this study, the data used is secondary data, obtained from company archives. The population of this study is all consumers registered in the company, namely as many as 709 companies. The number of samples taken was 228 samples with the criteria of consumers who are still making transactions from 2022 to 2023. The results obtained using descriptive analysis show that credit scoring is in the good category and the company's credit collection is in the current category. Credit scoring has a positive and significant effect of 74.2% on credit collectability at PT Krakatau Steel with a significance level of 0.000
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KAO, LIE-JANE, and CHENG-FEW LEE. "ALTERNATIVE METHOD FOR DETERMINING INDUSTRIAL BOND RATINGS: THEORY AND EMPIRICAL EVIDENCE." International Journal of Information Technology & Decision Making 11, no. 06 (November 2012): 1215–35. http://dx.doi.org/10.1142/s0219622012500332.
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The financial-ratio-based credit-scoring model for bond rating system requires the maximization of two conflicting objectives (i.e., the explanatory and discriminatory power, simultaneously), which had not been directly addressed in literature. The main purpose of this study is to develop a hybrid multivariate credit-scoring model that combines the principle component analysis and Fisher's discriminant analysis using the MINIMAX goal programming technique so that the maximization of the two conflicting objectives can be compromised. The performance of alternative credit-scoring models is analyzed and compared using dataset from previous studies. We find that the proposed hybrid credit-scoring model outperforms other alternative models in both explanatory and discriminatory powers.
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Lenka, Sudhansu R., Sukant Kishoro Bisoy, Rojalina Priyadarshini, and Mangal Sain. "Empirical Analysis of Ensemble Learning for Imbalanced Credit Scoring Datasets: A Systematic Review." Wireless Communications and Mobile Computing 2022 (June 15, 2022): 1–18. http://dx.doi.org/10.1155/2022/6584352.
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Credit scoring analysis has gained tremendous importance for researchers and the financial industries around the globe. It helps the financial industries to grant credits or loans to each deserving applicant with zero or minimal risks. However, developing an accurate and effective credit scoring model is a challenging task due to class imbalance and the presence of some irrelevant features. Recent researches show that ensemble learning has achieved supremacy in this field. In this paper, we performed an extensive comparative analysis of ensemble algorithms to bring further improvements in the algorithm oversampling, and feature selection (FS) techniques are implemented. The relevant features are identified by utilizing three FS techniques, such as information gain (IG), principal component analysis (PCA), and genetic algorithm (GA). Additionally, a comparative performance analysis is performed using 5 base and 14 ensemble models on three credit scoring datasets. The experimental results exhibit that the GA-based FS technique and CatBoost algorithm perform significantly better than other models in terms of five metrics, i.e., accuracy (ACC), area under the curve (AUC), F1-score, Brier score (BS), and Kolmogorov-Smirnov (KS).
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Olkhovska, Oksana, and A. Yu Chuguevtsev. "Scoring is an expert method for predicting the credit capacity of social assets." Management of Economy: Theory and Practice. Chumachenko’s Annals, no. 2019 (2019): 171–77. http://dx.doi.org/10.37405/2221-1187.2019.171-177.
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The article defines that modern approaches to solving the credit issue, while minimizing the risk of possible losses, need to introduce new effective risk management principles and computer decision support systems. The construction of such systems involves the development and use of a variety of alternative methods for analyzing data, alternative models and relevant criteria for analyzing the quality of models and the final result – the probability of non-return of credit. It has been proven that the use of the scoring model as one of the main risk management tools of credit operations is recognized worldwide as one of the most effective. In modern foreign banking practice, when building a scoring system, most often, such client characteristics are taken into account: the number of children, marital status, income, telephone availability, and the period of cooperation with the bank. In recent years, scoring systems have become widespread in the activities of domestic banks. Keywords credit scoring, scoring model, creditworthiness of individuals, bank accountant, post-manager, credit scoring technology.
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Bo Wen, Chi, Hsu Chiun Chieh, and Ho Mei Hung. "Enhancing credit scoring model performance by a hybrid scoring matrix." African Journal of Business Management 7, no. 18 (May 14, 2013): 1791–805. http://dx.doi.org/10.5897/ajbm11.2183.
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Анашкина, Елена, Elena Anashkina, Виктор Заернюк, and Viktor Zaernyuk. "Modern points of view on the natural person problem loans management system construction in the commercial banks." Services in Russia and abroad 8, no. 6 (December 2, 2014): 158–70. http://dx.doi.org/10.12737/6695.
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The article is connected with the scoring penalties (collection scoring) actual problem. This problem study popularity is provided by a number of reasons. At first, it happened in Russia for the reason of recent decade credit market, especially the retail sector, intensive development. Objectively, the increasing lending volumes leads to the credit risk increasing. The credit risks are connected with all the banking system in the country, especially with the credit and financial institutions. Therefore, the credit risk management in the retail sector quality is one of the most important factors of competitiveness of the bank. The well adjusting scoring model as one of the instrument of borrower credit risks level minimization, becomes the leading element in a banking business system, objectively, it leads to this phenomenon science researching necessity. This very fact, its actuality and value had determined the subject choice. The authors offered the scoring penalties system main elements.
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Zein, Afrizal, and Emi Sita Eriana. "Model Sistem Penunjang Keputusan Untuk Pengelolaan Pembiayaan Nasabah Di BPR Sehat Sejahtera Universitas Pamulang." SAINSTECH: JURNAL PENELITIAN DAN PENGKAJIAN SAINS DAN TEKNOLOGI 33, no. 2 (June 12, 2023): 38–46. http://dx.doi.org/10.37277/stch.v33i2.1584.
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Abstract
One of the BPRs that is currently developing in South Tangerang is BPR Sehat Sejahtera. BPR Sehat Sejahtera is part of the Pamulang University Sasmita Group, whose function as a bank is to channel financing to community businesses so that they are able to support the country's economy. To distribute financing, BPR managers are expected to be able to conduct financing analysis to reduce the risk of financing failure. Until now, financing analysis carried out by BPRs tends to be manual and requires quite a long time to make financing decisions. One method that can be used to assist in financing analysis is the credit scoring model. To facilitate decision making, an SPK was formed with a credit scoring model to provide several choices regarding customer financing applications. The DSS development process follows the traditional software development stages, namely: (1) planning, (2) analysis, (3) design and (4) implementation. SPK is developed using a spreadsheet model with a dialog display that supports user needs. The credit scoring model developed uses several methods, namely discriminant analysis, multiple linear regression, logistic regression, and decision trees. Each method will produce a different credit scoring model to be included in the SPK model base. The SPK prototype developed resulted in several financing analyzes according to the credit scoring model used. The prediction accuracy shown by credit scoring models ranges from 70% -80%. This method is capable of producing a model that provides a fairly high predictive accuracy. Meanwhile, the decision tree method is still not optimal in producing a credit scoring model.
Keywords: financing analysis, discriminant analysis, BPR, credit scoring, decision tree, multiple linear regression, logistic regression, decision support system (DSS)
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Römer, Ulf, and Oliver Musshoff. "Can agricultural credit scoring for microfinance institutions be implemented and improved by weather data?" Agricultural Finance Review 78, no. 1 (February 5, 2018): 83–97. http://dx.doi.org/10.1108/afr-11-2016-0082.
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Purpose In recent years, the application of credit scoring in urban microfinance institutions (MFIs) became popular, while rural MFIs, which mainly lend to agricultural clients, are hesitating to adopt credit scoring. The purpose of this paper is to explore whether microfinance credit scoring models are suitable for agricultural clients, and if such models can be improved for agricultural clients by accounting for precipitation. Design/methodology/approach This study merges two data sets: 24,219 loan and client observations provided by the AccèsBanque Madagascar and daily precipitation data made available by CelsiusPro. An in- and out-of-sample splitting separates model building from model testing. Logistic regression is employed for the scoring models. Findings The credit scoring models perform equally well for agricultural and non-agricultural clients. Hence, credit scoring can be applied to the agricultural sector in microfinance. However, the prediction accuracy does not increase with the inclusion of precipitation in the agricultural model. Therefore, simple correlation analysis between weather events and loan repayment is insufficient for forecasting future repayment behavior. Research limitations/implications The results should be verified in different countries and climate contexts to enhance the robustness. Social implications By applying scoring models to agricultural clients as well, all clients can benefit from an improved risk assessment (e.g. faster decision making). Originality/value To the best of the authors’ knowledge, this is the first study investigating the potential of microfinance credit scoring for agricultural clients in general and for Madagascar in particular. Furthermore, this is the first study that incorporates a weather variable into a scoring model.
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Ince, Huseyin, and Bora Aktan. "A COMPARISON OF DATA MINING TECHNIQUES FOR CREDIT SCORING IN BANKING: A MANAGERIAL PERSPECTIVE." Journal of Business Economics and Management 10, no. 3 (September 30, 2009): 233–40. http://dx.doi.org/10.3846/1611-1699.2009.10.233-240.
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Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan officers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artificial intelligence approaches: discriminant analysis, logistic regression, neural networks and classification and regression trees. Experimental studies using real world data sets have demonstrated that the classification and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.
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Hjelkrem, Lars Ole, Petter Eilif de Lange, and Erik Nesset. "The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank." Journal of Risk and Financial Management 15, no. 12 (December 12, 2022): 597. http://dx.doi.org/10.3390/jrfm15120597.
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Banks generally use credit scoring models to assess the creditworthiness of customers when they apply for loans or credit. These models perform significantly worse when used on potential new customers than existing customers, due to the lack of financial behavioral data for new bank customers. Access to such data could therefore increase banks’ profitability when recruiting new customers. If allowed by the customer, Open Banking APIs can provide access to balances and transactions from the past 90 days before the score date. In this study, we compare the performance of conventional application credit scoring models currently in use by a Norwegian bank with a deep learning model trained solely on transaction data available through Open Banking APIs. We evaluate the performance in terms of the AUC and Brier score and find that the models based on Open Banking data alone are surprisingly effective in predicting default compared to the conventional credit scoring models. Furthermore, an ensemble model trained on both traditional credit scoring data and features extracted from the deep learning model further outperforms the conventional application credit scoring model for new customers and narrows the performance gap between application credit scoring models for existing and new customers. Therefore, we argue that banks can increase their profitability by utilizing data available through Open Banking APIs when recruiting new customers.