Application of Deep Neural Networks to assess corporate Credit Rating

Golbayani, Parisa; Wang, Dan; Florescu, Ionut

doi:10.48550/arxiv.2003.02334

Cited by 5 publications

(6 citation statements)

References 30 publications

(40 reference statements)

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“…For example, Chee Kian Leong (2015) uses data from a firm in Singapore [12]. Authors in [13][14][15][16][17][18][19][20][21][22][23][24][25][26] all employ their unique dataset. Those articles mainly emphasize the significance and the veracity of the original data.…”

Section: The Datasets and Approaches Of The Reviewed Articlesmentioning

confidence: 99%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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Credit risk assessment is at the core of modern economies. Traditionally, it is measured by statistical methods and manual auditing. Recent advances in financial artificial intelligence stemmed from a new wave of machine learning (ML)-driven credit risk models that gained tremendous attention from both industry and academia. In this paper, we systematically review a series of major research contributions (76 papers) over the past eight years using statistical, machine learning and deep learning techniques to address the problems of credit risk. Specifically, we propose a novel classification methodology for ML-driven credit risk algorithms and their performance ranking using public datasets. We further discuss the challenges including data imbalance, dataset inconsistency, model transparency, and inadequate utilization of deep learning models. The results of our review show that: 1) most deep learning models outperform classic machine learning and statistical algorithms in credit risk estimation, and 2) ensemble methods provide higher accuracy compared with single models. Finally, we present summary tables in terms of datasets and proposed models.

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Section: The Datasets and Approaches Of The Reviewed Articlesmentioning

confidence: 99%

Machine learning-driven credit risk: a systemic review

Shi

Tse

Luo

et al. 2022

Neural Comput & Applic

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“…With the rapid development of deep learning, deep neural networks (DNNs) are receiving increasing attention in many fields, and even have become ubiquitous in a variety of applications, e.g., image processing [3], [4] and natural language processing [5], [6], because they can ex-tract valuable high-rank features automatically from original data without artificial feature engineering. Naturally, several previous studies [7], [8], [9], [10] have applied DNNs to the enterprise credit rating task to automatically learn the low dimensional representations of company credit. However, DNN-based forecasting models typically lack interpretability.…”

Section: Introductionmentioning

confidence: 99%

Explainable Enterprise Credit Rating via Deep Feature Crossing Network

Guo

Yang

Wu³

et al. 2021

Preprint

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Due to the powerful learning ability on high-rank and non-linear features, deep neural networks (DNNs) are being applied to data mining and machine learning in various fields, and exhibit higher discrimination performance than conventional methods. However, the applications based on DNNs are rare in enterprise credit rating tasks because most of DNNs employ the "end-to-end" learning paradigm, which outputs the high-rank representations of objects and predictive results without any explanations. Thus, users in the financial industry cannot understand how these high-rank representations are generated, what do they mean and what relations exist with the raw inputs. Then users cannot determine whether the predictions provided by DNNs are reliable, and not trust the predictions providing by such "black box" models. Therefore, in this paper, we propose a novel network to explicitly model the enterprise credit rating problem using DNNs and attention mechanisms. The proposed model realizes explainable enterprise credit ratings. Experimental results obtained on real-world enterprise datasets verify that the proposed approach achieves higher performance than conventional methods, and provides insights into individual rating results and the reliability of model training.

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“…With the rapid growth of the amount of financial information on the Internet, credit rating becomes fundamental for helping financial institutions to know companies well so as to mitigate credit risks [1]. It is an indication of the level of the risk in investing with the corporation and represents the likelihood that the corporation pays its financial obligations on time [2].…”

Section: Introductionmentioning

confidence: 99%

Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

Feng

Xue

et al. 2020

2020 IEEE 6th International Conference on Computer and Communications (ICCC)

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Credit rating is an analysis of the credit risks associated with a corporation, which reflect the level of the riskiness and reliability in investing. There have emerged many studies that implement machine learning techniques to deal with corporate credit rating. However, the ability of these models is limited by enormous amounts of data from financial statement reports. In this work, we analyze the performance of traditional machine learning models in predicting corporate credit rating. For utilizing the powerful convolutional neural networks and enormous financial data, we propose a novel end-to-end method, Corporate Credit Ratings via Convolutional Neural Networks, CCR-CNN for brevity. In the proposed model, each corporation is transformed into an image. Based on this image, CNN can capture complex feature interactions of data, which are difficult to be revealed by previous machine learning models. Extensive experiments conducted on the Chinese public-listed corporate rating dataset which we build, prove that CCR-CNN outperforms the state-of-the-art methods consistently.

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Application of Deep Neural Networks to assess corporate Credit Rating

Cited by 5 publications

References 30 publications

Machine learning-driven credit risk: a systemic review

Machine learning-driven credit risk: a systemic review

Explainable Enterprise Credit Rating via Deep Feature Crossing Network

Every Corporation Owns Its Image: Corporate Credit Ratings via Convolutional Neural Networks

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