Use Of Credit Scoring Research Articles

RELIABILITY OF CREDIT SCORES IN LOAN MANAGEMENT IN A DEVELOPING COUNTRY

In developing countries, loan sales for expensive products are very common but highly risky because of frequent payment defaults. Because personal credit histories in these countries are imprecise and insufficient compared with those in developed countries, the use of credit scores in loan sales may be very unreliable. Here, to evaluate the reliability of credit scores, we build a simple cost-benefit model of loan management, dividing credit scores into 10 credit score classes (CSCs). As CSC increases, the rough net benefits increase because the total losses caused by payment defaults decrease. The use of credit scores in loan sales is thus found to be highly reliable in developing countries. Combining credit scores with newly developed sources (e.g., mobile phones) may provide highly accurate estimates for loan management.

Assessing Creditworthiness in the Age of Big Data

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.

Introduction of Credit Scores and Subprime Lending in Minority Neighborhoods

How do technological advancements in credit markets affect minority communities? We use the endorsement of FICO credit scores by the Government Sponsored Enterprises (GSEs) in mortgage underwriting to answer this question. The use of credit scores led to the sorting of prime and subprime lenders across minority and non-minority neighborhoods. In minority neighborhoods prime lenders were substituted by subprime lenders and, as a result, the share of subprime lending in minority neighborhoods increased by 5 percentage points. Prime lenders with a stronger relationship with the GSEs reduced their lending in minority neighborhoods more. The level of securitization by the GSEs in minority neighborhoods also decreased.

Estimating credit and profit scoring of a Brazilian credit union with logistic regression and machine-learning techniques

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.

Empirical Evidence on the Use of Credit Scoring for Predicting Insurance Losses with Psycho-social and Biochemical Explanations

An important development in personal lines of insurance in the United States is the use of credit history data for insurance risk classification to predict losses. This research presents the results of collaboration with industry conducted by a university at the request of its state legislature. The purpose was to see the viability and validity of the use of credit scoring to predict insurance losses given its controversial nature and criticism as redundant of other predictive variables currently used. Working with industry and government, this study analyzed more than 175,000 policyholders’ information for the relationship between credit score and claims. Credit scores were significantly related to incurred losses, evidencing both statistical and practical significance. We investigate whether the revealed relationship between credit score and incurred losses was explainable by overlap with existing underwriting variables or whether the credit score adds new information about losses not contained in existing underwriting variables. The results show that credit scores contain significant information not already incorporated into other traditional rating variables (e.g., age, sex, driving history). We discuss how sensation seeking and self-control theory provide a partial explanation of why credit scoring works (the psycho-social perspective). This article also presents an overview of biological and chemical correlates of risk taking that helps explain why knowing risk-taking behavior in one realm (e.g., risky financial behavior and poor credit history) transits to predicting risk-taking behavior in other realms (e.g., automobile insurance incurred losses). Additional research is needed to advance new nontraditional loss prediction variables from social media consumer information to using information provided by technological advances. The evolving and dynamic nature of the insurance marketplace makes it imperative that professionals continue to evolve predictive variables and for academics to assist with understanding the whys of the relationships through theory development.

The Use of Credit Scoring in Microfinance Institutions and Their Outreach

Microfinance institutions that use credit scoring increase the productivity of their loan officers, thus leading to an increase in the number of borrowers, higher growth in the number of loans, and expanding financial inclusion and developmental opportunities.

Exploring the Use of Credit Scores in Selection Processes: Beware of Adverse Impact

The use of credit checks or credit scores in personnel selection has received widespread media attention of late. Though there is speculation that basing hiring decisions (even partially) on credit-related variables may produce or increase adverse impact, virtually no empirical literature exists to support or refute this claim. The present study explores the impact of using credit scores, in the context of a larger selection system, on adverse impact. We conducted Monte Carlo simulations representing various real-world selection systems (i.e., multiple hurdle, multiple hurdle with cut-off score, single hurdle). In addition to applicant credit scores, each simulation included variables that organizations commonly use during selection (i.e., educational background, personality). Results showed that in a majority of simulated hiring scenarios, using credit scores (as opposed to a random, race-neutral variable) widened the Black-White gap in hiring, producing more violations of the 4/5ths rule and statistically significant adverse impact. These results imply that organizations should be cautious when using credit scores to evaluate potential or current employees for jobs. This is one of the first studies to provide empirical evidence of a relationship between credit scores in selection and adverse impact. The use of simulations helps organizations be proactive in regards to choosing selection practices. Our results in particular pinpoint the situations where implementing credit scores as part of a larger selection process might be most problematic in terms of adverse impact, thereby providing much needed guidance to those considering credit scores for their selection processes.

Does Credit Scoring Produce a Disparate Impact?

The widespread use of credit scoring in the underwriting and pricing of mortgage and consumer credit has raised concerns that the use of these scores may unfairly disadvantage minority populations. A specific concern has been that the independent variables that comprise these models may have a disparate impact on these demographic groups. By “disparate impact” we mean that a variable's predictive power might arise not from its ability to predict future performance within any demographic group, but rather from acting as a surrogate for group membership. Using a unique source of data that combines a nationally representative sample of credit bureau records with demographic information from the Social Security Administration and a demographic information company, we examine the extent to which credit history scores may have such a disparate impact. Our examination yields no evidence of disparate impact by race (or ethnicity) or gender. However, we do find evidence of some limited disparate impact by age, in which the use of variables related to an individual's length of credit history appear to lower the credit scores of older individuals and increase them for the young.

Health Shocks, Credit Scores, and Strategic Behavior with Credit Cards

The paper solves a puzzle related to unsecured credit and the use of credit scores. With credit scores increasingly present in credit decisions, and with the increased use of credit cards, especially in categories at risk of large negative health shocks, asymmetry of information between banks and individuals opens up wider possibilities for strategic behavior on the part of the latter. In this paper I analyze consumer strategic behavior in the credit market when faced with exogenous health shocks in the presence of banking institutions that rely on credit scores as a form of indicator of creditworthiness. By introducing multiple sources for asymmetry of information in the credit market, something not done in prior models, and introducing a dynamic discount factor, I find that the rational consumer engages in strategic behavior specific to their credit score. The paper solves an empirically parameterized model of life cycle consumption and borrowing focusing on unsecured credit and allowing for default in the presence of health shocks. It also allows for the simultaneous holding of both an interest bearing asset and unsecured debt. The findings support the hypothesis that exogenous credit shocks affect the behavior and the degree of strategy involved. While I do not possess actual consumer data on default, I develop some stylized empirical evidence in line with predictions of the theoretical model.

Demographic Variables and Credit Scores: An empirical study of a controversial selection tool

The widespread use of credit scores as a selection tool has raised flags with lawmakers and the general public, but little is known about what impact such practices might have on protected classes. To explore this issue, the reported research investigated the relationship between five demographic variables and individual consumer credit scores. Using demographic data from 142 employees and objective credit data obtained from the Fair Isaac Corporation, the reported research found minority status was negatively related to credit scores (including large effect sizes), whereas age and education level were positively related to credit scores. Implications for organizations currently using or planning to use credit scores as part of their selection practices are discussed.

The rise of risk-based pricing of mortgage interest rates in Italy

This paper assesses how much mortgage interest rates in Italy are priced on credit risk as proxied by the probability of household mortgage delinquency estimated using the EU-Silc database. Owing to data availability, we restrict the analysis of mortgage pricing to Italian households. Consistent with the more widespread use of credit scoring, estimates indicate that Italian lenders have increasingly priced mortgage interest rates on household credit risk. For mortgages granted between 2000 and 2007, we find that a 1% point increase in the probability of default is associated with a 21 basis point rise in mortgage interest rates, lower than the 38 basis point premium Edelberg (2006) estimated for the US at the end of the 1990s.

The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability, Risk, and Profitability

The literature has documented a positive relationship between the use of credit scoring for small business loans and small business credit availability, broadly defined. However, this literature is hampered by the fact that all of the studies are based on a single 1998 survey of the very largest U.S. banking organizations. This paper addresses a number of deficiencies in the extant literature by employing data from a new survey of the use of credit scoring in small business lending, primarily by community banks. The survey evidence suggests that the use of credit scores in small business lending by community banks is surprisingly widespread. Moreover, the scores employed tend to be the consumer credit scores of the small business owners, rather than the more encompassing small business credit scores that include data on the firms as well as on the owners. Our empirical analysis suggests that credit scoring is associated with an initial increase in small business lending activity that moderates over time and no change in the quality of the loan portfolio. Supplementary analysis suggests that the use of credit scores for small business lending has a negative initial effect on community bank profitability that moderates over time.

Does Credit Scoring Produce a Disparate Impact?

The widespread use of credit scoring in the underwriting and pricing of mortgage and consumer credit has raised concerns that the use of these scores may unfairly disadvantage minority populations. A specific concern has been that the independent variables that comprise these models may have a on these demographic groups. By disparate impact we mean that a variable's predictive power might arise not from its ability to predict future performance within any demographic group, but rather from acting as a surrogate for group membership. Using a unique source of data that combines a nationally representative sample of credit bureau records with demographic information from the Social Security Administration and a demographic information company, we examine the extent to which credit history scores may have such a impact. Our examination yields no evidence of by race (or ethnicity) or gender. However, we do find evidence of limited by age, in which the use of variables related to an individual's credit history appear to lower the credit scores of older individuals and increase them for the young.

Does Credit Scoring Produce a Disparate Impact?

The widespread use of credit scoring in the underwriting and pricing of mortgage and consumer credit has raised concerns that the use of these scores may unfairly disadvantage minority populations. A specific concern has been that the independent variables that comprise these models may have a disparate impact on these demographic groups. By 'disparate impact' we mean that a variable's predictive power might arise not from its ability to predict future performance within any demographic group, but rather from acting as a surrogate for group membership. Using a unique source of data that combines a nationally representative sample of credit bureau records with demographic information from the Social Security Administration and a demographic information company, we examine the extent to which credit history scores may have such a disparate impact. Our examination yields no evidence of disparate impact by race (or ethnicity) or gender. However, we do find evidence of limited disparate impact by age, in which the use of variables related to an individual's credit history appear to lower the credit scores of older individuals and increase them for the young.

The Rhetoric ofMouvementand Passionate Expression in Seventeenth-Century French Harpsichord Music

Financialization can be characterized as capital switching from the primary, secondary or tertiary circuit to the quaternary circuit of capital. Housing is a central aspect of financialization. The financialization of mortgage markets demands that not just homes but also homeowners become viewed as financially exploitable. It is exemplified by the securitization of mortgage loans, but also by the use of credit scoring and risk-based pricing. In the past century, mortgage markets were transformed from being a ‘facilitating market’ for homeowners in need of credit to one increasingly facilitating global investment. Since mortgage markets are both local consumer markets and global investment markets, the dynamics of financialization and globalization directly relate homeowners to global investors thereby increasing the volatility in mortgage markets, as the current crisis shows all too well.

Arrears on Mortgages: Differences Across Countries and Their Effect on the Pricing of the Loan

The aim of this paper is twofold. Using the Eu-Silc database for seven European countries, we first analyse the frequency of households in arrears in repaying mortgages. By considering only households with mortgages, we uncover some new evidence compared to previous analyses. Although the shares of households in arrears on mortgages calculated on the whole population are low and similar across countries, the findings for households with mortgages show a wider heterogeneity, with the highest percentage of households in arrears in Italy and Spain. The probability of being late in repaying mortgage is lower when education and income are higher and increases when the head of the household is unemployed or in bad health. In the second part of the paper, we assess how much the price of the mortgage is linked to the household specific credit risk, as measured by its predicted probability of being in arrears on mortgage. This analysis regards only Italian households for which the national Eu-Silc database contains some additional variables on the mortgage contract, such as the interest rates. The evidence is that lenders decide the price of the mortgage also on the basis of the household credit scoring. Using a linear model for the estimation of the interest rates and considering mortgages granted since 2002 and 2006, an increase in the probability of default by 0.01 entails a rise in mortgage interest rates by around 12 basis points; in the Us at the end of the '90 Edelberg(2006) finds a higher risk-premium equal to 38 basis points. The impact has increased over time consistently with a more widespread use of credit scoring.

The Surprising Use of Credit Scoring in Small Business Lending by Community Banks and the Attendant Effects on Credit Availability and Risk

Working Paper 2009-9 March 2009 Abstract: The literature has documented a positive relationship between the use of credit scoring for small business loans and small business credit availability, broadly defined. However, this literature is hampered by the fact that all of the studies are based on a single 1998 survey of the very largest U.S. banking organizations. This paper addresses a number of deficiencies in the extant literature by employing data from a new survey on the use of credit scoring in small business lending, primarily by community banks. The survey evidence suggests that the use of credit scores in small business lending by community banks is surprisingly widespread. Moreover, the scores employed tend to be the consumer credit scores of the small business owners rather than the more encompassing small business credit scores that include data on the firms as well as on the owners. Our empirical analysis suggests that credit scoring is associated with increased small business lending after a learning period, with no material change in the quality of the loan portfolio. However, these quantity and quality results appear to vary depending on the way in which credit scores are implemented in the underwriting process. JEL classification: G21, G28, L23 Key words: banks, small business, credit scoring I. Introduction Commercial bank lending to small businesses has received a great deal of research attention over the past two decades. The overriding issue in this literature is one of credit availability, given that small firms have historically faced significant difficulties in accessing funding for creditworthy (i.e., positive net present value) projects due to a lack of credible information. Small businesses are typically much more informationally opaque than large corporations because small firms often do not have certified audited financial statements to yield credible financial information on a regular basis. As well, these firms typically do not have publicly traded equity or debt, yielding no market prices or public ratings that might suggest their quality. To address the informational opacity problem, financial institutions use a number of different lending technologies (e.g., Berger and Udell 2006). One lending technology that has recently received considerable research attention is small business credit scoring (SBCS). This technology confronts the opacity problem by combining personal financial data about the owner of the business with the relatively limited information about the firm using statistical methods to predict future credit performance. Consumer credit scoring (CCS) has been widely used for many years in retail credit markets (e.g., mortgages, credit cards, and automobile credits), but SBCS is a more recent phenomenon. Most large U.S. banks did not adopt SBCS until the mid-1990s due to concerns regarding firm heterogeneity and nonstandardized loan documentation (e.g., Mester 1997). As discussed below, some banks instead use the consumer credit scores of small business owners to evaluate small business loan applications. The application of CCS to small business lending has not been previously studied. The empirical literature studying the effects of SBCS has documented significant favorable effects of this lending technology on small business credit availability, broadly defined. Specifically, the adoption of SBCS is empirically associated with 1) increases in the quantity of lending (Frame, Srinivasan, and Woosley 2001, Frame, Padhi, and Woosley 2004, Berger, Frame, and Miller 2005); 2) more lending to relatively opaque, risky borrowers (Berger, Frame, and Miller 2005); 3) lending within low-income as well as high-income areas (Frame, Padhi, and Woosley 2004); and 4) lending over greater distances (DeYoung, Glennon, and Nigro 2008). (1,2) See Berger and Frame (2007) for a more comprehensive review of these studies. While the extant research provides some important information about SBCS, this literature is hampered by the fact that all of the empirical studies are based on a single survey of the largest U. …

The Sociology and Geography of Mortgage Markets: Reflections on the Financial Crisis

The article presents a symposium discussion assessing the role the sociological and geographical aspects of the mortgage market played in the 2008 financial crisis. Combinations of interrelated causes of the global financial crisis include deregulation and re-regulation, financialization and globalization, and bubbles and wrong incentives. The various ways the mortgage market is shaped and reshaped by both mortgage lenders and state institutions is examined. It is stressed that mortgage loan securitization is largely an invention of government and government erected institutions such as Fannie Mae and Freddie Mac.

Deconstructing the Subprime Debacle Using New Indices of Underwriting Quality and Economic Conditions: A First Look

We document that technical progress in originating and pricing mortgages has enabled a trend since 1979 toward more relaxed credit standards on mortgage lending, which is reflected in rising foreclosure rates. We then decompose annual variation in mortgage performance measured by share of loans entering foreclosure into a part due to economic conditions and a part due to underwriting changes. The decomposition provides natural metrics or indices of national underwriting quality and economic conditions. The results suggest that the recent subprime debacle can be attributed about equally to each factor. The deterioration since 1990 was marked by two periods. In the first, during the 1990s, there was a lowering of observable credit standards, like loan to value ratios. It was deliberate and related to the use of credit scores and the development of more sophisticated underwriting systems. The negative effects of eroding loan quality on foreclosures were to some extent masked by strong local and national economic conditions during this period. In the second period, after 2002, there was little change in observable loan characteristics like loan to value or credit history. This second period is associated with the rise of subprime and Alt-A markets but also with subprime and other non-agency securitization. Securitization induced moral hazard and a deterioration in underwriting standards that was not easily observed by investors in the securities.

Lending Organizational Structure and the Use of Credit Scoring: Evidence from a Survey on Italian Banks

This paper examines the results of a survey carried out in 2007 by the Bank of Italy concerning different characteristics of the organization of lending activities. Between 2003 and 2006 the physical distance between the headquarters and the branches increased, the limits to the decision-making process of loan officers were eased, their mobility raised and the use of economic incentives to reward their activity expanded. The huge heterogeneity in organizational structures persists even within relatively homogenous size classes. The diffusion of statistical models to assess credit risk (scoring), accelerated recently particularly among large banks, boosted by the new Basel Capital Accord. Scoring is either very important or determinant in decisions on credit extension while it is rarely influential in setting interest rates, the duration of the credit and the amount and type of collateral required. The survey shows that banks have been progressively adapting their organizational structure in order to incorporate the credit scoring tools into their lending processes.