Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

Lessmann, Stefan; Baesens, Bart; Seow, Hsin‐Vonn; Thomas, Lyn C.

doi:10.1016/j.ejor.2015.05.030

Cited by 789 publications

(612 citation statements)

References 85 publications

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“…Particularly, with the development of new predictive modelling techniques in machine learning and the statistical literature, various studies have assessed how these newer approaches perform compared to more established methods with regards to scoring unsecured consumer loans such as personal loans and credit cards (Baesens et al, 2003;Kennedy et al, 2013b;Lessmann et al, 2015). However, when it comes to 15 secured lending, research findings regarding credit risk assessment of mortgage loans are much more scarce, despite the fact that they are among the largest class of assets on European banks' balance sheets.…”

Section: Accepted Manuscriptmentioning

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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Section: Accepted Manuscriptmentioning

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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“…A sequential ensemble learning combines a series of weak base learners that process different hypothesizes sequentially to form a better hypothesis, thus making good predictions [65] [66] [67]. To verify the performances of their proposal, five real world credit datasets are utilized, including two datasets from two P2P lending companies.…”

Section: Credit Card Fraud Detectionmentioning

confidence: 99%

A state of the art survey of data mining-based fraud detection and credit scoring

Zhou¹,

Cheng²,

Zhu³

et al. 2018

MATEC Web Conf.

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Abstract.Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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“…Due to its simple and intuitive character, as well as the relatively good results it provides, logistic regression has maintained its position as a standard tool even after more sophisticated models were developed, such as neural networks, support vector machines, genetic algorithms and various hybrid and ensemble models. For an overview see Abdou, Pointon (2011), Li, Zhong (2012), or Lessmann et al (2015).…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, logistic regression has become the standard and the most-used approach in credit scoring (Crook et al, 2007;Lessmann et al, 2015;Nguyen, 2015). According to Rachev (2008), credit scoring is the most popular application of logistic regression.…”

Section: Introductionmentioning

confidence: 99%

The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast

Vaněk

Hampel

2017

Acta Univ. Agric. Silvic. Mendelianae Brun.

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In this paper we propose a straightforward, flexible and intuitive computational framework for the multi-period probability of default estimation incorporating macroeconomic forecasts. The concept is based on Markov models, the estimated economic adjustment coefficient and the official economic forecasts of the Czech National Bank. The economic forecasts are taken into account in a separate step to better distinguish between idiosyncratic and systemic risk. This approach is also attractive from the interpretational point of view. The proposed framework can be used especially when calculating lifetime expected credit losses under IFRS 9.

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Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

Cited by 789 publications

References 85 publications

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

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

A state of the art survey of data mining-based fraud detection and credit scoring

The Probability of Default Under IFRS 9: Multi-period Estimation and Macroeconomic Forecast

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