Proposal of a model for credit risk prediction based on deep learning methods and SMOTE techniques for imbalanced dataset

Gicić, Adaleta; Ðonko, Dženana

doi:10.1109/icat57854.2023.10171259

Cited by 3 publications

(1 citation statement)

References 22 publications

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“…UCI Credit data set (Gicić & Ðonko, 2023): The UCI Credit data set is usually used to study the credit default situation of credit card customers and the construction of credit assessment models, where UCI stands for University of California, Irvine. It contains a large amount of borrower details, covering key data such as personal attributes, loan application information, credit history, and loan status.…”

Section: Experiments Data Setsmentioning

confidence: 99%

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

Fan,

Wang,

2024

Journal of Organizational and End User Computing

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Consumer credit assessment has always been a crucial concern in the financial industry. It involves evaluating an individual's credit history and their ability to repay loans, playing a pivotal role in the risk management and lending decisions made by credit institutions. In the present landscape, traditional credit assessment methods confront various shortcomings. Firstly, they typically only consider static features and are unable to capture the dynamic changes in an individual's credit profile over time. Secondly, traditional methods struggle with processing complex time series data, failing to fully exploit the importance of time-related information. To address these challenges, we propose an innovative solution – the XGBoost-LSTM model optimized with the AdaBound algorithm. This hybrid model combines two powerful machine learning techniques, XGBoost and LSTM, to leverage both static and dynamic features effectively.

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Section: Experiments Data Setsmentioning

confidence: 99%

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

Fan,

Wang,

2024

Journal of Organizational and End User Computing

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An Overview of the Use of Deep Learning Algorithms to Predict Bankruptcy

Singh,

Kumar,

Kumar

et al. 2024

Lecture Notes in Networks and Systems

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Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation

Gicic,

Đonko,

Subasi

2024

Entropy

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Although deep learning (DL) algorithms have been proved to be effective in diverse research domains, their application in developing models for tabular data remains limited. Models trained on tabular data demonstrate higher efficacy using traditional machine learning models than DL models, which are largely attributed to the size and structure of tabular datasets and the specific application contexts in which they are utilized. Thus, the primary objective of this paper is to propose a method to use the supremacy of Stacked Bidirectional LSTM (Long Short-Term Memory) deep learning algorithms in pattern discovery incorporating tabular data with customized 3D tensor modeling in feeding neural networks. Our findings are empirically validated using six diverse, publicly available datasets each varying in size and learning objectives. This paper proves that the proposed model based on time-sequence DL algorithms, which were generally described as inadequate when dealing with tabular data, yields satisfactory results and competes effectively with other algorithms specifically designed for tabular data. An additional benefit of this approach is its ability to preserve simplicity while ensuring fast model training also with large datasets. Even with extremely small datasets, models can be applied to achieve exceptional predictive results and fully utilize their capacity.

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Proposal of a model for credit risk prediction based on deep learning methods and SMOTE techniques for imbalanced dataset

Cited by 3 publications

References 22 publications

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

Application of AdaBound-Optimized XGBoost-LSTM Model for Consumer Credit Assessment in Banking Industries

An Overview of the Use of Deep Learning Algorithms to Predict Bankruptcy

Time Sequence Deep Learning Model for Ubiquitous Tabular Data with Unique 3D Tensors Manipulation

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