Credit Scoring and the Availability of Small Business Credit in Low‐ and Moderate‐Income Areas

This paper estimates that credit scoring is associated with about a $3,900 increase in small business lending per sample banking organization, per low‐ and moderate‐income (LMI) area served, and this effect is roughly equivalent to that estimated for higher‐income areas. For our sample, this corresponds to a $536 million increase in small business credit in LMI areas in 1997 than otherwise would have been the case. This effect appears to be driven by increased out‐of‐market lending by banking organizations, as in‐market lending generally declines. Overall, it does not appear that credit scoring has a disparate impact on LMI areas.     

Research Opportunities for Neural Networks: The Case for Credit

This article identifies research opportunities in the use of artificial neural networks in credit scoring and related business intelligence situations, particularly as they have been emerging in the global economy. In the literature review, particular attention is paid to commercial lending credit risk assessment and consumer credit scoring. Investors and auditors need models that can predict whether a customer will stay viable. Lenders must manage their credit risk to maximize profits and cash flow, while minimizing losses. As the global economic recession continues, investors are tightening their investment belts and need models that help them make better investment decisions, while lenders must strengthen lending practices and better identify both good and bad credit risks. Artificial neural networks may help firms improve their credit model development, and thereby their credit decisions and profitability. Such technology may also help improve development in emerging economies. Copyright © 2016 John Wiley & Sons, Ltd.     

Credit information,consolidation and credit market performance: Bank-level evidence from developing countries

Paying particular attention to the degree of banking market concentration in developing countries, this paper examines the effect of credit information sharing on bank lending. Using bank-level data from African countries over the period 2004 to 2009 and a dynamic two-step system generalised method of moments (GMM) estimation, it is found that credit information sharing increases bank lending. The degree of banking market concentration moderates the effect of credit information sharing on bank lending. The results are robust to controlling for possible interactions between credit information sharing and governance.     

Credit Unions and Small Business Lending

Among the issues raised by consolidation within the banking industry is a concern that small businesses will be less able to obtain credit as community banks are acquired by larger or non-local institutions. Community banks have traditionally been a major source of funding for small businesses. The impact of bank consolidation on credit availability may depend in part on whether the remaining community institutions expand their small business lending activities. This study examines whether credit unions have a propensity to extend business loans in markets that have experienced bank merger and acquisition activity. We find some evidence that credit unions are more likely to engage in business lending in markets characterized by greater bank merger and acquisition activity. Moreover, the estimated economic significance is meaningful in many of the specifications.

Deregulation,technological change,and the business-lending performance of large and small banks

According to DeYoung et al. [Journal of Financial Services Research, 2004] deregulation and technological change has divided the US banking industry into two primary size-based groups: very large banks, specializing in the use of “hard” information to make standardized loans and smaller banks, specializing in the use of “soft” information and relationship development to make non-standardized loans. We evaluate business-lending performance for small and large banks over the 1993–2001 period. Small business lending by small banks is characterized by relationship development and non-standardized loans. Consistent with DeYoung et al.'s model, we find that, after controlling for market concentration, cost of funds, and a variety of other factors that might influence yields, smaller banks perform better than larger banks in the small business lending market. However, larger banks appear to have the advantage in credit card lending, a market characterized by impersonal relationships and standardized loans. Interestingly, we find evidence that larger banks have been making inroads in the market for the smallest business loans, a result consistent with the use of credit scoring by large banks to make very small business loans [Berger et al., Journal of Money, Credit, and Banking, 2004].     

业主信用评分法:小微企业贷款技术的创新

我国小微企业当前的信贷环境,除了企业自身高成本和高风险的因素之外,还与不完善的小微企业贷款定价机制等有关。小微企业的信贷价值和业主的信贷价值是密切相关的,从小微企业主个人的有关信用历史,就可较准确地预期小微企业未来的还款表现,这是业主信用评分法的基本思想。本文总结了业主信用评分法在美国的发展与应用,通过分析我国小微企业贷款信用评分的现状与问题,提出应借鉴业主信用评分法,促进我国小微企业贷款的创新。     

Modelling small‐business credit scoring by using logistic regression,neural networks and decision trees

Previous research on credit scoring that used statistical and intelligent methods was mostly focused on commercial and consumer lending. The main purpose of this paper is to extract important features for credit scoring in small‐business lending on a dataset with specific transitional economic conditions using a relatively small dataset. To do this, we compare the accuracy of the best models extracted by different methodologies, such as logistic regression, neural networks (NNs), and CART decision trees. Four different NN algorithms are tested, including backpropagation, radial basis function network, probabilistic and learning vector quantization, by using the forward nonlinear variable selection strategy. Although the test of differences in proportion and McNemar's test do not show a statistically significant difference in the models tested, the probabilistic NN model produces the highest hit rate and the lowest type I error. According to the measures of association, the best NN model also shows the highest degree of association with the data, and it yields the lowest total relative cost of misclassification for all scenarios examined. The best model extracts a set of important features for small‐business credit scoring for the observed sample, emphasizing credit programme characteristics, as well as entrepreneur's personal and business characteristics as the most important ones. Copyright © 2005 John Wiley & Sons, Ltd.     

Bank Lending Standards and Access to Lines of Credit

This paper examines how changes in bank lending standards are related to the availability of bank lines of credit for private and comparable public firms. Overall, we find that access to lines of credit is more contingent on bank lending standards for private than for public firms. The impact of bank lending standards is however asymmetric: while private firms are less likely than public firms to gain access to new lines when credit market conditions are tight, we find no difference between public and private firms in terms of their use or retention of pre‐existing lines. We also find that private firms without lines of credit use more trade credit when bank lending standards are tight, which is suggestive of a supply effect. Overall, the evidence suggests that “credit crunches” are likely to have a disproportionate impact on private firms. However, pre‐existing banking relationships appear to mitigate the impact of these contractions on private firms.     

Large banks and small firm lending

We examine the long-lasting effects of the 2007 real estate price collapse on small business credit supply. Banks affected by the decline in real estate prices systematically contracted their credit to small firms. At the same time, regional and local banks, many of which were unaffected by the initial shock, increased small business lending to nearby borrowers and opportunistically expanded their branch networks, making gains in market share that persisted for the following decade. Although the net effect of the contraction in credit was negative, we show that opportunistic expansion tied to permanent market changes is an important offsetting force that dampened the negative effect on small firms during the GFC and its aftermath.     

Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works

The most important new development in the past two decades in the personal lines of insurance may well be the use of an individual's credit history as a classification and rating variable to predict losses. However, in spite of its obvious success as an underwriting tool, and the clear actuarial substantiation of a strong association between credit score and insured losses over multiple methods and multiple studies, the use of credit scoring is under attack because there is not an understanding of why there is an association. Through a detailed literature review concerning the biological, psychological, and behavioral attributes of risky automobile drivers and insured losses, and a similar review of the biological, psychological, and behavioral attributes of financial risk takers, we delineate that basic chemical and psychobehavioral characteristics (e.g., a sensation‐seeking personality type) are common to individuals exhibiting both higher insured automobile loss costs and poorer credit scores, and thus provide a connection which can be used to understand why credit scoring works. Credit scoring can give information distinct from standard actuarial variables concerning an individual's biopsychological makeup, which then yields useful underwriting information about how they will react in creating risk of insured automobile losses.     

基于拒绝推论的小企业信用评分模型研究

在小企业信用评分模型的构建中,因数据缺失和样本选择性偏差可能导致模型参数估计有偏,对模型的预测能力和应用会有很大影响。本文利用从万德数据库中筛选出的小企业信息资料,模拟银行信贷筛选,产生带有缺失数据的模拟信贷样本,利用Heckman二阶段模型预测新的信用评分模型,将其结果与忽略缺失数据的审查模型和基于完全信息的标准模型进行比较。结果显示,Heckman二阶段模型的表现优于直接忽略缺失样本数据的审查模型,更接近标准模型的结果。这表明拒绝推论能够有效解决信用评分建模中数据缺失导致的样本选择偏差,提高信用评分模型的有效性和预测能力。     

When Credit Bites Back

Using data on 14 advanced countries between 1870 and 2008 we document two key facts of the modern business cycle: relative to typical recessions, financial crisis recessions are costlier, and more credit‐intensive expansions tend to be followed by deeper recessions (in financial crises or otherwise) and slower recoveries. We use local projection methods to condition on a broad set of macro‐economic controls to study how past credit accumulation impacts key macro‐economic variables such as output, investment, lending, interest rates, and inflation. The facts that we uncover lend support to the idea that financial factors play an important role in the modern business cycle.     

The Effects of Dynamic Changes in Bank Competition on the Supply of Small Business Credit

We study the effects of structural changes in banking markets on the supply of credit to small businesses. Specifically, we examine whether bank mergers and acquisitions (M & As) and entry have ``external' effects on small business loans by other banks in the same local markets. The results suggest modest positive external effects from these dynamic changes in competition, except that large banks may reduce small business lending in reaction to entry. We confirm bank size and age as important determinants of this lending, and show that the measured age effect does not appear to be driven by local market M & A activity.     

Credit cycles and macro fundamentals

We use an intensity-based framework to study the relation between macroeconomic fundamentals and cycles in defaults and rating activity. Using Standard and Poor's U.S. corporate rating transition and default data over the period 1980–2005, we directly estimate the default and rating cycle from micro data. We relate this cycle to the business cycle, bank lending conditions, and financial market variables. In line with earlier studies, the macro variables appear to explain part of the default cycle. However, we strongly reject the correct dynamic specification of these models. The problem is solved by adding an unobserved dynamic component to the model, which can be interpreted as an omitted systematic credit risk factor. By accounting for this latent factor, many of the observed macro variables loose their significance. There are a few exceptions, but the economic impact of the observed macro variables for credit risk remains low. We also show that systematic credit risk factors differ over transition types, with risk factors for downgrades being noticeably different from those for upgrades. We conclude that portfolio credit risk models based only on observable systematic risk factors omit one of the strongest determinants of credit risk at the portfolio level. This has obvious consequences for current modeling and risk management practices.     

Bank–firm relations and the role of Mutual Guarantee Institutions at the peak of the crisis

We examine the role played by Mutual Guarantee Institutions (MGIs) in the lending policies undertaken by banks at the peak of the Great Crisis of 2007–2009. We address this issue by using a large database on Italian firms built from the credit files of UniCredit banking Group and focusing on small business. We provide an empirical analysis of the determinants of the probability that a borrowing firm will suffer financial tension and obtain two main innovative findings. First, we find that small firms supported by MGIs less likely experienced financial tensions even at that time of utmost financial stress. Second, our empirical evidence shows that MGIs played a signaling role beyond the simple provision of collateral. This latter finding suggests that the information provided by MGIs turned out to be key for bank–firm relations as scoring and rating systems – being typically based on pro-cyclical indicators – had become less informative during the crisis.     

Risk Overhang and Loan Portfolio Decisions: Small Business Loan Supply before and during the Financial Crisis

We estimate a structural model of bank portfolio lending and find that the typical U.S. community bank reduced its business lending during the global financial crisis. The decline in business credit was driven by increased risk overhang effects (consistent with a reduction in the liquidity of assets held on bank balance sheets) and by reduced loan supply elasticities suggestive of credit rationing (consistent with an increase in lender risk aversion). Nevertheless, we identify a group of strategically focused relationship banks that made and maintained higher levels of business loans during the crisis.     

A darker side to decentralized banks: Market power and credit rationing in SME lending

We use loan-level data to study how the organizational structure of banks impacts small business lending. We find that decentralized banks—where branch managers have greater autonomy over lending decisions—give larger loans to small firms and those with “soft information.” However, decentralized banks are also more responsive to their own competitive environment. They are more likely to expand credit when faced with competition but also cherry pick customers and restrict credit when they have market power. This “darker side” to decentralized banks in concentrated markets highlights that the level of local banking competition is key to determining which organizational structure provides better lending terms for small businesses.     

Foreign Banks in Transition Countries: To Whom Do They Lend and How Are They Financed?

We use focused interviews with managers of foreign parent banks and their affiliates in Central Europe and the Baltic States to analyze the small‐business lending and internal capital markets of multinational financial institutions. Our approach allows us to complement the standard empirical literature, which has difficulty in analyzing important issues such as lending technologies and capital allocation. We find that the acquisition of local banks by foreign banks has not led to a persistent bias in these banks' credit supply toward large multinational corporations. Instead, increased competition and the improvement of subsidiaries' lending technologies have led foreign banks to gradually expand into the SME and retail markets. Second, it is demonstrated that local bank affiliates are strongly influenced by the capital allocation and credit steering mechanisms of the parent bank.     

我国P2P网络借贷信用风险影响因素研究——基于排序选择模型的实证分析

P2P(peer-to-peer)网络借贷是一种借助网络平台,由个人与个人间互为借贷双方的小额借贷交易。它作为互联网与民间借贷相结合的新兴金融模式,具有较高的信用风险。采用排序选择模型,基于 ex-celVBA 数据挖掘技术截取多个 P2P 网站数据,对平台信用风险的影响因素进行实证分析,结果表明：个人特征、信用变量、历史表现、借款信息分别对网络借贷信用风险存在正向影响,由此发现网站提供的信息对投资者避免信用风险没有起到实质作用。     

Borrower Self-Selection,Underwriting Costs,and Subprime Mortgage Credit Supply

In the U.S., households participate in two very different types of credit markets. Personal lending is characterized by continuous risk-based pricing in which lenders offer households a continuous distribution of borrowing possibilities based on estimates of their creditworthiness. This contrasts sharply with mortgage markets where lenders specialize in specific risk categories of borrowers and mortgage supply is stepwise linear. The contrast between continuous lending for personal loans and discrete lending by specialized lenders for mortgage credit has led to concerns regarding the efficiency and equity of mortgage lending. This paper sheds both theoretical and empirical light on the differences in the two credit markets. The theory section demonstrates why, in a perfectly competitive credit market where all lenders have the same underwriting technology, mortgage credit supply curves are stepwise linear and lenders specialize in prime or subprime lending. The empirical section then provides evidence that borrowers are being effectively sorted based on risk characteristics by the market.