E.T.-RNN

Babaev, Dmitrii; Savchenko, Maxim; Tuzhilin, Alexander; Umerenkov, Dmitry

doi:10.1145/3292500.3330693

Cited by 56 publications

(9 citation statements)

References 29 publications

(35 reference statements)

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“…To resolve these issues, deep learning methods were developed recently. Babaev et al applied RNN for credit loan application task [31]. Kvammea et al applied a convolutional neural network for mortgage default prediction [32].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Multi-view GCN for Loan Default Risk Prediction

Chen

Wang

et al. 2023

Preprint

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Loan default risk prediction is a major application of machine learning for financial institutions to evaluate the client's default probability. Existing deep learning models rarely consider the connection among application records for loan default detection. We believe similar records, as auxiliary information, are also significant for loan default prediction, particularly for those records with many missing data. Additionally, in practical scenarios, the data distribution is imbalanced since the default records are small samples, which may also lead the model to achieve sub-optimal results. To this end, we propose multi-view loan application graphs, dubbed MLAGs, for small sample augmentation. Additionally, based on the graph convolution, similar records can also be aggregated to alleviate the issue of missing values. Moreover, a multi-view graph convolution network, named MGCN, is applied for loan default risk prediction. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Multi-view GCN for Loan Default Risk Prediction

Chen

Wang

et al. 2023

Preprint

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“…Figure 3: A schematic overview of the HCS loan approval rule engine. Kruppa et al 2013), to nsemble models like extreme gradient boosting (XGBoost) (Chen and Guestrin 2016), and even deep neural networks (Babaev et al 2019).…”

Section: Related Workmentioning

confidence: 99%

A Submodular Optimization Approach to Accountable Loan Approval

Lee,

Yoo,

Shin

et al. 2024

AAAI

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In the field of finance, the underwriting process is an essential step in evaluating every loan application. During this stage, the borrowers' creditworthiness and ability to repay the loan are assessed to ultimately decide whether to approve the loan application. One of the core components of underwriting is credit scoring, in which the probability of default is estimated. As such, there has been significant progress in enhancing the predictive accuracy of credit scoring models through the use of machine learning, but there still exists a need to ultimately construct an approval rule that takes into consideration additional criteria beyond the score itself. This construction process is traditionally done manually to ensure that the approval rule remains interpretable to humans. In this paper, we outline an automated system for optimizing a rule-based system for approving loan applications, which has been deployed at Hyundai Capital Services (HCS). The main challenge lay in creating a high-quality rule base that is simultaneously simple enough to be interpretable by risk analysts as well as customers, since the approval decision should be accountable. We addressed this challenge through principled submodular optimization. The deployment of our system has led to a 14% annual growth in the volume of loan services at HCS, while maintaining the target bad rate, and has resulted in the approval of customers who might have otherwise been rejected.

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“…The reason could be found in the fact that most of the datasets used in fields other than credit scoring are transactional, time sequence data, what is ideal for LSTM utilization. Another paper 14 uses LSTM in credit scoring but not stacked LSTM and not on classical public German or Australian dataset but on a massive transactional dataset with 200 million transactions and over 740.000 clients.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent credit scoring using deep learning methods

Gicić

Ðonko

Subaşı

2023

Concurrency and Computation

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Summary Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.

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E.T.-RNN

Cited by 56 publications

References 29 publications

Multi-view GCN for Loan Default Risk Prediction

Multi-view GCN for Loan Default Risk Prediction

A Submodular Optimization Approach to Accountable Loan Approval

Intelligent credit scoring using deep learning methods

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