Predicting mortgage default using convolutional neural networks

Kvamme, Håvard; Sellereite, Nikolai; Aas, Kjersti; Sjursen, Steffen

doi:10.1016/j.eswa.2018.02.029

Cited by 128 publications

(57 citation statements)

References 30 publications

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“…A lot of research effort has been committed to evaluating classification algorithms in credit scoring, ranging from traditional statistical methods, such as logistic regression [1], to non-parametric algorithms, such as neural networks [9]. In the recent years there has been an increased interest for using hybrid and ensemble classifiers in credit risk, such as boosted regression trees, random forests, deep learning methods and other [10], [11], [12], [13] [14]. A number of benchmark studies have been performed, comparing classification accuracy of different classification algorithms [15], [8].…”

Section: Background Workmentioning

confidence: 99%

An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

Andric¹,

Kalpić²,

Bohaček³

2019

COMSIS J

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In this paper we investigate the role of sample size and class distribution in credit risk assessments, focusing on real life imbalanced data sets. Choosing the optimal sample is of utmost importance for the quality of predictive models and has become an increasingly important topic with the recent advances in automating lending decision processes and the ever growing richness in data collected by financial institutions. To address the observed research gap, a large-scale experimental evaluation of real-life data sets of different characteristics was performed, using several classification algorithms and performance measures. Results indicate that various factors play a role in determining the optimal class distribution, namely the performance measure, classification algorithm and data set characteristics. The study also provides valuable insight on how to design the training sample to maximize prediction performance and the suitability of using different classification algorithms by assessing their sensitivity to class imbalance and sample size.

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Section: Background Workmentioning

confidence: 99%

An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

Andric¹,

Kalpić²,

Bohaček³

2019

COMSIS J

View full text Add to dashboard Cite

show abstract

“…Furthermore, some authors used NN-based approaches to credit scoring on the aggregated transactional data. For example in [20] authors applied shallow convolutional neural networks on daily transactional statistics.…”

Section: Related Workmentioning

confidence: 99%

E.T.-RNN

Babaev

Savchenko

Tuzhilin

et al. 2019

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery &Amp; Data Mining

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In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational data to compute credit scores for the loan applicants. We demonstrate that our approach significantly outperforms the baselines based on the customer data of a large European bank. We also conducted a pilot study on loan applicants of the bank, and the study produced significant financial gains for the organization. In addition, our method has several other advantages described in the paper that are very significant for the bank.

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“…The most important point of CNN is that it can detect local features of documents by adopting m convolving filters. For more information about CNN, please refer to literature [47].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

Pan

Chen

et al. 2019

Energies

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Accurate forecasts of corporate failure in the Chinese energy sector are drivers for both operational excellence in the national energy systems and sustainable investment of the energy sector. This paper proposes a novel integrated model (NIM) for corporate failure forecasting in the Chinese energy sector by considering textual data and numerical data simultaneously. Given the feature of textual data and numerical data, convolutional neural network oriented deep learning (CNN-DL) and support vector machine (SVM) are employed as the base classifiers to forecast using textual data and numerical data, respectively. Subsequently, soft set (SS) theory is applied to integrate outputs of CNN-DL and SVM. Hence, NIM inherits advantages and avoids disadvantages of CNN-DL, SVM, and SS. It is able to improve the forecasting performance by taking full use of textual data and numerical data. For verification, NIM is applied to the real data of Chinese listed energy firms. Empirical results indicate that, compared with benchmarks, NIM demonstrates superior performance of corporate failure forecasting in the Chinese energy sector.

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Predicting mortgage default using convolutional neural networks

Cited by 128 publications

References 30 publications

An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

An insight into the effects of class imbalance and sampling on classification accuracy in credit risk assessment

E.T.-RNN

Forecasting Corporate Failure in the Chinese Energy Sector: A Novel Integrated Model of Deep Learning and Support Vector Machine

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