Credit Risk Analysis using LightGBM and a comparative study of popular algorithms

Ponsam, J. Godwin; Gracia, S.V. Juno Bella; Geetha, G.; Karpaselvi, S.; Nimala, K.

doi:10.1109/iccct53315.2021.9711896

Cited by 11 publications

(4 citation statements)

References 11 publications

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“…Moreover, the processing time of LightGBM is always around one minutes and while tuning parameters it takes less time than Random Forest although the number of combinations is larger than Random Forest. The result found is consistent with previous findings of the key reference (Chada, 2019), as well as other research in the literature (Ge et al, 2020;Ke et al, 2017;Ponsam et al, 2021).…”

Section: Discussionsupporting

confidence: 93%

E-commerce Price Suggestion Algorithm – A Machine Learning Application

Quang,

Thao,

Nhi

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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E-commerce is a key move that has altered the way businesses are conducted, particularly in the retail industry.In the e-commerce business, pricing is one of the most significant aspects in determining profitability, and closely linked to the company's sales. The rise of online markets has necessitated the creation of a Machine Learning tool for pricing suggestions. The aim of this study is to select a machine learning model to create a price suggestion tool for Ecommerce enterprises. Three machine learning algorithms -Linear Regression, Random Forest and LightGBM -are tested on a dataset of an Ecommerce enterprise to indicate the performance of models when using in a dataset with several features and millions of rows. The study also processes to make improvement the output of models, including parameters tuning, feature selection or remove outstanding values. The result shows that Light GBM after Grid search CV process outperforms in terms of both prediction error and processing time.

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Section: Discussionsupporting

confidence: 93%

E-commerce Price Suggestion Algorithm – A Machine Learning Application

Quang,

Thao,

Nhi

et al. 2022

Proceedings of the International Conference on Industrial Engineering and Operations Management

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“…Machine learning techniques including logistic regression, kNN, k-means clustering, SVM, neural networks, decision trees, naïve Bayes, XGBoost, catboost, adaboost, LightGBM had been used recently in credit risk analysis applied to various dataset (Pandey et al, 2016, Qiu et al, 2019, Menendez, 2019, Pillai et al, 2019, Tian et al, 2020, Wang et al, 2020a, Wang et al, 2020b, Turjo et al, 2021, Ponsam, et al, 2021, Guo and Zhou, 2022.…”

Section: Introductionmentioning

confidence: 99%

Credit risk analysis using boosting methods

Coşkun

Turanli

2023

Journal of Applied Mathematics, Statistics and Informatics

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The use of credit for various occasions has become a routine in our lives. In return, banking and financial institutions require to determine whether the loan demands from them contain any risk. Accordingly, these institutions have been increased their activities in determining whether credit rating models from past credit records of the person applying for the loan works properly. Machine learning-based technologies have opened a new era in this field. AI and machine learning based methods for credit scoring are currently implemented by banking or non-banking financial institutions. Employed models are to extract meaningful features from the required data in which wide variety of information available. In this study, credit risk assessment is conducted using boosting methods such as CatBoost, XGBoost and Light GBM. To this aim, Kaggle Home Credit Default Risk dataset is used and the effect of crediting tendency on the results is also considered. The results have shown that gradient boosting methods provide results that are close to each other, and crediting tendency produces better AUC score in CatBoost while it causes a small decrement in AUC score of XGBoost and LightGBM.

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“…LightGBM adopts a histogram optimization strategy, sorting the features in each dimension of the sample before training, and then dividing the feature histogram. In subsequent training, the algorithm only needs to use the histogram as a "feature" for constructing the decision tree, which significantly reduces the number of traversals of the sample set [7]. The algorithm is shown in Figure 5…”

Section: Introduction To the Model And Its Conceptsmentioning

confidence: 99%

Corporate Credit Risk Rating Model Based on Financial Big Data

Tang¹,

Zeng²,

Zhao³

2023

BCPBM

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In recent years, leveraging financial big data and machine learning to identify corporate risks has emerged as a crucial approach for financial risk management. This paper proposes a method based on financial big data and the LightGBM model to effectively assess corporate credit risk ratings. Feature engineering is performed on corporate financial datasets, using correlation coefficients, chi-square tests, and machine learning techniques to select essential financial indicators. Subsequently, bayesian optimization is employed for hyperparameter tuning, using the classification accuracy of high risk and highest risk categories as the objective function. This process yields a multi-classification model capable of effectively identifying corporate credit risk ratings through financial data. The results demonstrate that the model exhibits strong identification capabilities for high credit risk corporates. The model achieves the best classification performance for high-risk categories, with an accuracy of 74%. The comprehensive classification accuracy and recall rate for both high-risk and highest-risk categories reach 70%. The overall classification accuracy across all categories is approximately 64%. In summary, through judicious model selection, data preprocessing, feature selection, Bayesian parameter tuning, and the establishment of appropriate objective functions, the LightGBM model demonstrates robust performance in addressing corporate credit risk rating problems.

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Credit Risk Analysis using LightGBM and a comparative study of popular algorithms

Cited by 11 publications

References 11 publications

E-commerce Price Suggestion Algorithm – A Machine Learning Application

E-commerce Price Suggestion Algorithm – A Machine Learning Application

Credit risk analysis using boosting methods

Corporate Credit Risk Rating Model Based on Financial Big Data

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