Improving credit scoring by differentiating defaulter behaviour

Bravo, Cristián; Thomas, Lyn C.; Weber, Richard W.

doi:10.1057/jors.2014.50

Cited by 41 publications

(5 citation statements)

References 30 publications

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“…Here, it is still possible to reason about the good/bad dichotomy, simply by considering the predictions Pr(Y = 0|X = x). This suggestion, to split the "bads" into subclasses, is familiar in credit scoring where different types of bad debt are considered, such as the "can't pay" and "won't pay" behavioural distinction [3].…”

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confidence: 99%

Issues using logistic regression with class imbalance, with a case study from credit risk modelling

Li¹,

Bellotti²,

Adams³

2019

Foundations of Data Science

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The class imbalance problem arises in two-class classification problems, when the less frequent (minority) class is observed much less than the majority class. This characteristic is endemic in many problems such as modeling default or fraud detection. Recent work by Owen [19] has shown that, in a theoretical context related to infinite imbalance, logistic regression behaves in such a way that all data in the rare class can be replaced by their mean vector to achieve the same coefficient estimates. We build on Owen's results to show the phenomenon remains true for both weighted and penalized likelihood methods. Such results suggest that problems may occur if there is structure within the rare class that is not captured by the mean vector. We demonstrate this problem and suggest a relabelling solution based on clustering the minority class. In a simulation and a real mortgage dataset, we show that logistic regression is not able to provide the best out-of-sample predictive performance and that an approach that is able to model underlying structure in the minority class is often superior.

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confidence: 99%

Issues using logistic regression with class imbalance, with a case study from credit risk modelling

Li¹,

Bellotti²,

Adams³

2019

Foundations of Data Science

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“…Typically, alternative data variables are strong indicators of both the willingness and capacity to repay a loan (Bravo, Thomas, & Weber, 2015). However, the types and varieties of these indicators (almost 20 new variables had significant effects upon the estimation) result in significant gains in both financial and statistical terms.…”

Section: Discussionmentioning

confidence: 99%

Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications

Roa,

Correa-Bahnsen,

Suarez

et al. 2020

Preprint

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In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments traditionally underserved by banks and financial institutions. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals, who are also the most likely to engage with alternative lenders. Furthermore, using the TreeSHAP method for Stochastic Gradient Boosting interpretation, our results also revealed interesting non-linear trends in the variables originating from the app, which would not normally be available to traditional banks. Our results represent an opportunity for technology companies to disrupt traditional banking by correctly identifying alternative data sources and handling this new information properly. At the same time alternative data must be carefully validated to overcome regulatory hurdles across diverse jurisdictions.

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“…Although the exact reasons for retail credit defaults are innumerable (e.g., job loss, marital breakdown, financial naivety, fraud), they are crudely grouped into either fraud ('won't pay') or financial distress ('can't pay'), as explored in Thomas (2009, pp. 282) and Bravo, Thomas and Weber (2015). Modelling the exact reason and its underlying causes is challenging in practice since lenders rarely keep record of defaulting reasons.…”

Section: A Defining Background On Loan Defaultmentioning

confidence: 99%

A procedure for loss-optimising default definitions across simulated credit risk scenarios

Botha¹,

Beyers²,

Villiers³

2019

Preprint

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A new procedure is presented for the objective comparison and evaluation of default definitions. This allows the lender to find a default threshold at which the financial loss of a loan portfolio is minimised, in accordance with Basel II. Alternative delinquency measures, other than simply measuring payments in arrears, can also be evaluated using this optimisation procedure. Furthermore, a simulation study is performed in testing the procedure from 'first principles' across a wide range of credit risk scenarios. Specifically, three probabilistic techniques are used to generate cash flows, while the parameters of each are varied, as part of the simulation study. The results show that loss minima can exist for a select range of credit risk profiles, which suggests that the loss optimisation of default thresholds can become a viable practice. The default decision is therefore framed anew as an optimisation problem in choosing a default threshold that is neither too early nor too late in loan life. These results also challenges current practices wherein default is pragmatically defined as '90 days past due', with little objective evidence for its overall suitability or financial impact, at least beyond flawed roll rate analyses or a regulator's decree.

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Improving credit scoring by differentiating defaulter behaviour

Cited by 41 publications

References 30 publications

Issues using logistic regression with class imbalance, with a case study from credit risk modelling

Issues using logistic regression with class imbalance, with a case study from credit risk modelling

Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications

A procedure for loss-optimising default definitions across simulated credit risk scenarios

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