A window of opportunity: Assessing behavioural scoring

Kennedy, Kenneth; Namee, Brian Mac; Delany, Sarah Jane; O'Sullivan, Mark; Watson, Nick J.

doi:10.1016/j.eswa.2012.08.052

Cited by 31 publications

(21 citation statements)

References 22 publications

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“…The majority of previous work focus on application scoring, while prior work on behavioral scoring is much less developed, see e.g. Thomas (2000) and Kennedy et al (2013). The methodology proposed in this paper may be used both for application and behavioral scoring.…”

Section: Introductionmentioning

confidence: 99%

Predicting mortgage default using convolutional neural networks

Kvamme

Sellereite

Aas

et al. 2018

Expert Systems with Applications

120

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We predict mortgage default by applying convolutional neural networks to consumer transaction data. For each consumer we have the balances of the checking account, savings account, and the credit card, in addition to the daily number of transactions on the checking account, and amount transferred into the checking account. With no other information about each consumer we are able to achieve a ROC AUC of 0.918 for the networks, and 0.926 for the networks in combination with a random forests classifier.

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Section: Introductionmentioning

confidence: 99%

Predicting mortgage default using convolutional neural networks

Kvamme

Sellereite

Aas

et al. 2018

Expert Systems with Applications

120

View full text Add to dashboard Cite

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“…Detailed accounts of the various modelling approaches to credit scoring 100 are included in Crook et al (2007), Crook and Bellotti (2009), Thomas (2009), Hand (2009b,and Martin (2013). However, with the exceptions of Galindo and Tamayo (2000), or Feldman and Gross (2005), Kennedy et al (2013a), most of the literature concentrates on credit card or personal lending only. This is somewhat surprising given the importance of mortgage lending as a business line to banks in advanced economies, but may be due to a lack of publicly available information A comparison by Bastos (2008) found that BRT performed well compared to Neural Networks (multilayer perceptrons) and Support Vector Machines on two credit scoring tasks.…”

Section: Related Literature and Main Contributionsmentioning

confidence: 99%

An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

Fitzpatrick

Mues

2016

European Journal of Operational Research

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Highlights• We evaluate default prediction performance of machine learning/regression models.• Including boosted trees, random forests, penalised linear/semi-parametric logistic regression.• Using data on over 300,000 residential mortgage loans.• The results indicate varying degrees of predictive power.• Statistical tests suggest boosted regression trees outperform penalised logistic regression.

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“…Retail banking practice uses application and behavioral credit scoring models for automation of loan approval process for individuals (Kennedy, Namee, Delany, O'Sullivan, & Watson, 2013). In retail banking decision to grant a loan based on fundamental analysis and credit analyst assessment is left to be applied only for high amount or non-standard loans.…”

Section: Introductionmentioning

confidence: 99%