[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120388-en":3,"doc-seo-120388-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120388,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation - read online","Accurate loan default prediction is essential for credit risk evaluation because small accuracy deviations can generate substantial financial losses for lending institutions. This study applies a nonparametric approach to compare five machine learning classifiers using sufficiently large datasets and evaluates performance via accuracy, precision, recall, F1-score, and ROC-AUC. It also tests data preprocessing (normalization, standardization, missing-value imputation, and SMOTE for imbalance) and the effect of hyper-parameters within model pipelines, using out-of-sample results from training/testing splits of 80:20 across two datasets (1000 and 30,000).","Article  \nAccuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation  \nHaokun Dong, Rui Liu and Allan W. Tham *  \nFaculty of Science and Technology, University of Canberra, Canberra 2617, Australia;  \n[haokun.dong@canberra.edu.au](haokun.dong@canberra.edu.au) (H.D.); [drruiliu@yeah.net](drruiliu@yeah.net) (R.L.)  \n* Correspondence: [allan.tham@canberra.edu.au](allan.tham@canberra.edu.au)  \nCitation: Dong, Haokun, Rui Liu, and Allan W. Tham. 2024. Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation. Journal of Risk and Financial Management 17: 50 .  \n[https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jrfm17020050  \nAcademic Editor: Thanasis Stengos  \nReceived: 1 October 2023  \nRevised: 19 January 2024  \nAccepted: 22 January 2024  \nPublished: 29 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nAbstract: An accurate prediction of loan default is crucial in credit risk evaluation. A slight deviation from true accuracy can often cause financial losses to lending institutes. This study describes the nonparametric approach that compares five different machine learning classifiers combined with a focus on sufficiently large datasets. It presents the findings on various standard performance measures such as accuracy, precision, recall and F1 scores in addition to Receiver Operating Curve-Area Under Curve (ROC-AUC). In this study, various data pre-processing techniques including normalization and standardization, imputation of missing values and the handling of imbalanced data using SMOTE will be discussed and implemented. Also, the study examines the use of hyper-parameters in various classifiers. During the model construction phase, various pipelines feed data to the five machine learning classifiers, and the performance results obtained from the five machine learning classifiers are based on sampling with SMOTE or hyper-parameters versus without SMOTE and hyper-parameters. Each classifier is compared to another in terms of accuracy during training and prediction phase based on out-of-sample data. The 2 data sets used for this experiment contain 1000 and 30,000 observations, respectively, of which the training/testing ratio is 80:20 . The comparative results show that random forest outperforms the other four classifiers both in training and actual prediction.  \nKeywords: financial data analysis; machine learning algorithms; loan default assessment; classification  \n1. Introduction  \nFinancial institutions are facing increasing challenges in mitigating various kinds of risks. In his “taxonomy of risks”, Christoffersen (2011) defines risks as market volatility, liquidity, operational, credit and business risks. Due to uncertainties, financial risk evaluation (FRE) is increasingly playing a pivotal role in ensuring organizations maximize their profitability by minimizing losses due to a failure to mitigate risks. Noor and Abdalla (2014) argue that there is a direct negative impact on profitability in proportion to unmitigated risks. Hence, the primary approach of FRE is to identify risks in advance to allow for an appropriate course of action before any investments or decisions can be made. As financial risks evolve over time due to factors such as economic fluctuations, market conditions and other factors beyond control, the evaluation process requires constant update to keep up with market conditions.  \nCredit risk analysis undertaken in recent years mostly involves financial risk prediction. For example, loan default analysis, which often comes in the form of binary classification problems, has become an integral pa","cbCaiceLAQyVuWWF","https://ap.wps.com/l/cbCaiceLAQyVuWWF","pdf",3786334,1,19,"English","en",105,"# Abstract\n# Introduction\n## Financial risk evaluation and credit risk\n## Loan default as a classification task\n# Methods and Experimental Setup\n## Classifiers and pipeline design\n## Preprocessing and imbalanced data handling\n## Hyper-parameter evaluation\n## Performance measures and evaluation protocol\n# Results and Discussion\n## Comparative accuracy on training and out-of-sample data\n## Best-performing model analysis\n# Conclusion","[{\"question\":\"Which evaluation metrics are used to compare the five machine learning algorithms?\",\"answer\":\"The study compares algorithms using accuracy, precision, recall, F1-score, and ROC-AUC, covering both training and prediction performance on out-of-sample data.\"},{\"question\":\"How does the study address missing values and class imbalance?\",\"answer\":\"It applies data preprocessing techniques including normalization and standardization, imputes missing values, and handles imbalanced data using SMOTE before model training.\"},{\"question\":\"What datasets and train/test split are used in the experiments?\",\"answer\":\"Two datasets are used with 1,000 and 30,000 observations respectively, and the training/testing ratio is set to 80:20.\"}]","Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation - 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