Statistical Classification Methods in Consumer Credit Scoring: A Review

Hand, David J.; Henley, William

doi:10.1111/j.1467-985x.1997.00078.x

Cited by 719 publications

(401 citation statements)

References 56 publications

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“…To tackle this problem, bankers typically combine two strategies: credit scoring and relationship lending. By the rst of these strategies, the lending institutions assess the creditworthiness of potential borrowers from their personal and/or business characteristics (Hand and Henley, 2007;Lewis, 1994). The sound strategy is a time-consuming process by which credit o cers learn about their clients' creditworthiness (Berger and Udell, 1995;Boot, 2000) and o er them progressively increasing loans after timely repayments (Egli, 2004).…”

Section: Introductionmentioning

confidence: 99%

Subjectivity in credit allocation to micro-entrepreneurs: evidence from Brazil

Agier

Szafarz

2012

Small Bus Econ

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This paper estimates the impact of loan officers' subjectivity on microcredit granting by exploiting an exceptionally detailed database from a Brazilian microfinance institution. Loan officers collect field data, meet with applicants, and make recommendations to the credit committee that in turn has the final say on both loan approval and loan size. The loan officers' subjectivity is captured through the lens of disparate treatment based on gender. Indeed, our estimations show that an unfair gender gap is observed in loan size, and that this gap is almost exclusively attributable to the loan officers. We interpret this finding as evidence that, despite monitoring and wage incentivization, microcredit officers keep letting their subjective preferences interfere with loan granting. We conclude by suggesting alternative means to curb subjectivity in credit allocation to micro-entrepreneurs. AbstractThis paper estimates the impact of loan o cers' subjectivity on microcredit granting by exploiting an exceptionally detailed database from a Brazilian micro nance institution. Loan o cers collect eld data, meet with applicants, and make recommendations to the credit committee that in turn has the nal say on both loan approval and loan size. The loan o cers' subjectivity is captured through the lens of disparate treatment based on gender. Indeed, our estimations show that an unfair gender gap is observed in loan size, and that this gap is almost exclusively attributable to the loan o cers. We interpret this nding as evidence that, despite monitoring and wage incentivization, microcredit o cers keep letting their subjective preferences interfere with loan granting. We conclude by suggesting alternative means to curb subjectivity in credit allocation to micro-entrepreneurs.

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Section: Introductionmentioning

confidence: 99%

Subjectivity in credit allocation to micro-entrepreneurs: evidence from Brazil

Agier

Szafarz

2012

Small Bus Econ

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“…For instance, data mining and machine learning classification models are constructed on the basis of historical data exactly with the purpose of learning the distinctive elements of different classes, such as good/bad debtor in credit/insurance scoring systems [3,10,27] or good/bad worker in personnel selection [6]. When applied for automatic decision making, DSS can potentially guarantee less arbitrary decisions, but still they can be discriminating in the social, negative sense.…”

Section: Icail-2009mentioning

confidence: 99%

Integrating induction and deduction for finding evidence of discrimination

2010

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Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. While less arbitrary decisions can potentially be guaranteed, automatic DSS can still be discriminating in the socially negative sense of resulting in unfair or unequal treatment of people. We present a reference model for finding (prima facie) evidence of discrimination in automatic DSS which is driven by a few key legal concepts. First, frequent classification rules are extracted from the set of decisions taken by the DSS over an input pool dataset. Key legal concepts are then used to drive the analysis of the set of classification rules, with the aim of discovering patterns of discrimination. We present an implementation, called LP2DD, of the overall reference model integrating induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools.

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“…Therefore, it is not surprising to …nd that they have been the subject of a considerable literature (see, for example, Altman et al, 1981, Maddala, 1996, Hand and Henley, 1997, and the references therein).…”

Section: Introductionmentioning

confidence: 99%

Estimation of default probabilities using incomplete contracts data

Silva

Murteira

2009

Journal of Empirical Finance

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This paper develops a count data model for credit scoring which allows the estimation of default probabilities using incomplete contracts data. The model is based on the beta-binomial distribution, which is found to be particularly adequate to describe this sort of data. A well known data set on personal loans granted by a Spanish bank is used to illustrate the application of the proposed model. JEL classi…cation code: C21, C51, G21.

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Statistical Classification Methods in Consumer Credit Scoring: A Review

Cited by 719 publications

References 56 publications

Subjectivity in credit allocation to micro-entrepreneurs: evidence from Brazil

Subjectivity in credit allocation to micro-entrepreneurs: evidence from Brazil

Integrating induction and deduction for finding evidence of discrimination

Estimation of default probabilities using incomplete contracts data

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