Machine Learning approach for Credit Scoring

Provenzano, Angela Rita; Trifirò, D.; Datteo, A.; Giada, L.; Jean, Nicola; A., Riciputi,; Pera, Giacomo Le; Spadaccino, Maurizio; Massaron, Luca; Nordio, C.

doi:10.48550/arxiv.2008.01687

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…Machine Learning (ML) is a predominant tool nowadays to solve several challenges in different industries, such as credit scoring [ 1 ], fraud analysis [ 2 ], product recommendation [ 3 ], and demand forecasting [ 4 ], among other extensively explored use cases. Under this premise, the research of the quantum computing properties applied to ML has expanded rapidly in recent years since a proven advantage could be a highly useful cross-industry.…”

Section: Introductionmentioning

confidence: 99%

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms

Mancilla¹,

Pere

2022

Entropy

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Quantum Machine Learning (QML) has not yet demonstrated extensively and clearly its advantages compared to the classical machine learning approach. So far, there are only specific cases where some quantum-inspired techniques have achieved small incremental advantages, and a few experimental cases in hybrid quantum computing are promising, considering a mid-term future (not taking into account the achievements purely associated with optimization using quantum-classical algorithms). The current quantum computers are noisy and have few qubits to test, making it difficult to demonstrate the current and potential quantum advantage of QML methods. This study shows that we can achieve better classical encoding and performance of quantum classifiers by using Linear Discriminant Analysis (LDA) during the data preprocessing step. As a result, the Variational Quantum Algorithm (VQA) shows a gain of performance in balanced accuracy with the LDA technique and outperforms baseline classical classifiers.

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Section: Introductionmentioning

confidence: 99%

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms

Mancilla¹,

Pere

2022

Entropy

View full text Add to dashboard Cite

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“…Therefore, it is of great importance to model the profile of the corporation. Rating agencies including Standard and Poor's, Moody's and CCXI come up with these credit ratings based on analyzing various aspects of companies' financial data and nonfinancial data [3]. This assessment process is very expensive and complicated, which often takes months with many experts involved to analyze all kinds of variables that reflect the reliability of a corporation.…”

Section: Introductionmentioning

confidence: 99%

“…Besides, machine learning techniques gained lots of interest of researchers and engineers in credit rating prediction due to its highly practical value. The work [3] builds a stack of machine learning models aiming at composing a state-of-theart credit rating system.…”

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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“…However, this assessment process is usually very expensive and complicated, which often takes months with many experts involved to analyze all kinds of variables, which reflect the reliability of a corporation. One way to deal with this problem may be to build a model based on historical financial information [24] of the corporation.…”

Section: Introductionmentioning

confidence: 99%

“…Parisa et al [12] apply four machine learning techniques (Bagged Decision Trees, Random Forest, Support and Multilayer Perceptron) to predict corporate credit rating. Recently, [24] builds a stack of machine learning models aiming at composing a state-of-the-art credit rating system. In the work [13], they analyze the performance of four neural network architectures including MLP, CNN, CNN2D, LSTM in predicting corporate credit rating as issued by Standard and Poor's.…”

Section: Introductionmentioning

confidence: 99%

Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks

Feng,

Xu,

Xue

et al. 2020

Preprint

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Credit rating is an analysis of the credit risks associated with a corporation, which reflects the level of the riskiness and reliability in investing, and plays a vital role in financial risk. There have emerged many studies that implement machine learning and deep learning techniques which are based on vector space to deal with corporate credit rating. Recently, considering the relations among enterprises such as loan guarantee network, some graph-based models are applied in this field with the advent of graph neural networks. But these existing models build networks between corporations without taking the internal feature interactions into account. In this paper, to overcome such problems, we propose a novel model, Corporate Credit Rating via Graph Neural Networks, CCR-GNN for brevity. We firstly construct individual graphs for each corporation based on self-outer product and then use GNN to model the feature interaction explicitly, which includes both local and global information. Extensive experiments conducted on the Chinese publiclisted corporate rating dataset, prove that CCR-GNN outperforms the state-of-the-art methods consistently.

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Machine Learning approach for Credit Scoring

Cited by 5 publications

References 12 publications

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms

A Preprocessing Perspective for Quantum Machine Learning Classification Advantage in Finance Using NISQ Algorithms

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

Every Corporation Owns Its Structure: Corporate Credit Ratings via Graph Neural Networks

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