A two-stage dynamic credit scoring model, based on customers’ profile and time horizon

Mavri, Maria; Angelis, Vassilis; Ioannou, George; Gaki, Eleni; Koufodontis, Iason

doi:10.1057/fsm.2008.2

Cited by 17 publications

(11 citation statements)

References 18 publications

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“…Some models of credit scoring examine repayment ability of the customer, outstanding debt (which is weighted as a positive criteria in many models) or the frequency of the repayments from the customer (Hsieh, 2004). Other models assess the risk level of the credit card applicant and the likelihood of default during a period of time (Mavri et al ., 2008). Advanced computational systems for data mining such as neural networks, classification and regression trees, and multivariate adaptive regression splines are increasingly being employed (Lee et al ., 2002; Malhotra and Malhotra, 2003).…”

Section: Profiling Vulnerable (And Profitable) Customersmentioning

confidence: 99%

“…Advanced technologies used for profiling are giving banks and lenders far greater and more detailed information about consumers' behaviour and likely choices than ever before. Both academic sources in data mining and systems technology, and marketing magazines speak to the benefits of profiling to increase sales and financial gains for lenders (Macintyre, 1999;Tillett, 2000;Pallatto, 2003;Cohen, 2004;Dragoon, 2006;Bailor, 2007;Curko et al, 2007;Williams, 2007;Cruz-George, 2008;Mavri et al, 2008).…”

Section: Profiling Vulnerable (And Profitable) Customersmentioning

confidence: 99%

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The ethical and policy implications of profiling ‘vulnerable’ customers

Harrison

Gray

2010

Int J Consumer Studies

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In the shadow of the global financial crisis, the issue of the marketing of credit has become an increasing concern in the past 12 months. Outstanding personal debt in the UK currently stands at £1479 billion and is rising by £1 million every 10.6 min. In Australia, there is currently $44.6 billion worth of outstanding credit card debt, and in the US, $2596 billion was owed on credit cards in 2008. At present, the banking sector utilizes sophisticated research methods to profile consumers, including those who might be considered financially vulnerable. However, the policy frameworks in most industrialized countries do not account for this form of target marketing when considering how to protect vulnerable groups. This paper is an initial attempt to examine the different methods by which profiling is conducted and the policy implications of this sophisticated form of segmentation and targeting. We argue that current consumer policies are inadequate in protecting vulnerable consumers from these marketing techniques, and recent recommendations from the Federal Reserve Bank of the United States, and the Australian Law Reform Commission to allow banks and lenders to ‘pre‐screen’ potential customers will exacerbate personal debt levels, rather than reducing them.

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Section: Profiling Vulnerable (And Profitable) Customersmentioning

confidence: 99%

Section: Profiling Vulnerable (And Profitable) Customersmentioning

confidence: 99%

The ethical and policy implications of profiling ‘vulnerable’ customers

Harrison

Gray

2010

Int J Consumer Studies

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“…While an accurate classification algorithm leads to higher profitability, bad credit can impact a lender in terms of loss in capital, lower revenues and increased losses, leading to bankruptcy (Abdou and Pointon, 2011;Lessmann et al, 2015). Management of credit risk has become more important, especially in the aftermath of 2008 crisis, where banks use their own credit scoring models under internal ratings-based approach and face stricter capital Credit scoring is essentially a type of a classification problem to determine whether the borrower will default on a loan (Mavri et al, 2008). Hence, it is an assessment of the risk associated with lending to an organization or individual (Paleologo et al, 2010).…”

Section: Theme Analysismentioning

confidence: 99%

A review of credit scoring research in the age of Big Data

Onay¹,

Öztürk

2018

JFRC

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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 datacentric 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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“…A large part of the available studies recall the employment of high dimensional data in order to get a proxy of consumer credit response (see Yap et al 2011 for a review). Abdou et al (2008) and Mavri et al (2008), for example, used many variables including gender, marital status, and education together with monthly income in order to evaluate credit risk level of the retail potential clients. Generally such variables are employed when the model is performed on consumer data because they capture relevant differences between "good" and "bad".…”

Section: Credit Scoring Datamentioning

confidence: 99%

Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis

Liberati

Camillo

Saporta

2015

Adv Data Anal Classif

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Due to the recent financial turmoil, a discussion in the banking sector about how to accomplish long term success, and how to follow an exhaustive and powerful strategy in credit scoring is being raised up. Recently, the significant theoretical advances in machine learning algorithms have pushed the application of kernel-based classifiers, producing very effective results. Unfortunately, such tools have an inability to provide an explanation, or comprehensible justification, for the solutions they supply. In this paper, we propose a new strategy to model credit scoring data, which exploits, indirectly, the classification power of the kernel machines into an operative field. A reconstruction process of the kernel classifier is performed via linear regression, if all predictors are numerical, or via a general linear model, if some or all predictors are categorical. The loss of performance, due to such approximation, is balanced by better interpretability for the end user, which is able to order, understand and to rank the influence of each category of the variables set in the prediction. An Italian bank case study has been illustrated and discussed; empirical results reveal a promising performance of the introduced strategy.

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A two-stage dynamic credit scoring model, based on customers’ profile and time horizon

Cited by 17 publications

References 18 publications

The ethical and policy implications of profiling ‘vulnerable’ customers

The ethical and policy implications of profiling ‘vulnerable’ customers

A review of credit scoring research in the age of Big Data

Advances in credit scoring: combining performance and interpretation in kernel discriminant analysis

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