AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector
Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs. Machine Learning (ML) models are also being used to make predictions in these applications. Accurate forecasting requires knowledge of these machine learning models and their distinguishing feat...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2023-12-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2022.9020037 |
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| author | Vikas Kumar Shaiku Shahida Saheb Preeti Atif Ghayas Sunil Kumari Jai Kishan Chandel Saroj Kumar Pandey Santosh Kumar |
| author_facet | Vikas Kumar Shaiku Shahida Saheb Preeti Atif Ghayas Sunil Kumari Jai Kishan Chandel Saroj Kumar Pandey Santosh Kumar |
| author_sort | Vikas Kumar |
| collection | DOAJ |
| description | Every real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs. Machine Learning (ML) models are also being used to make predictions in these applications. Accurate forecasting requires knowledge of these machine learning models and their distinguishing features. The datasets we use as input for each of these different types of ML models, yielding different results. The choice of an ML model for a dataset is critical. A loan risk model is used to show how ML models for a dataset can be linked together. The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk. This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields. If credit risk is considered, a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried. Various approaches to credit risk calculation will be examined, ranging from the most basic to the most complex. In addition, we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches, logistic regression, decision tree, and gradient boost to see which one produced the most commercially useful insights. |
| format | Article |
| id | doaj-art-7234b0a50c664fad9ee773149ffe742b |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2023-12-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-7234b0a50c664fad9ee773149ffe742b2025-02-03T00:40:18ZengTsinghua University PressBig Data Mining and Analytics2096-06542023-12-016447849010.26599/BDMA.2022.9020037AI-Based Hybrid Models for Predicting Loan Risk in the Banking SectorVikas Kumar0Shaiku Shahida Saheb1Preeti2Atif Ghayas3Sunil Kumari4Jai Kishan Chandel5Saroj Kumar Pandey6Santosh Kumar7Humanities and Management Department, Dr. B. R. Ambedkar National Institute of Technology Jalandhar, Jalandhar 144027, IndiaMittal School of Business, Lovely Professional University, Phagwara 144402, IndiaDepartment of Commerce & Business Administration, Kanya Maha Vidyalaya (KMV), Jalandhar 144004, IndiaSchool of Management, Gitam (to be deemed university), Bangalore 561203, IndiaGovernment College for Women, Indra Gandhi University, Ateli 123021, IndiaInstitute of Management Studies, Kurukshetra University, Kurukshetra 136119, IndiaDepartment of Computer Engineering and Applications, GLA University, Mathura 281406, IndiaDepartment of Management, Jaipuriya Institute of Management, Jaipur 302033, IndiaEvery real-world scenario is now digitally replicated in order to reduce paperwork and human labor costs. Machine Learning (ML) models are also being used to make predictions in these applications. Accurate forecasting requires knowledge of these machine learning models and their distinguishing features. The datasets we use as input for each of these different types of ML models, yielding different results. The choice of an ML model for a dataset is critical. A loan risk model is used to show how ML models for a dataset can be linked together. The purpose of this study is to look into how we could use machine learning to quantify or forecast mortgage credit risk. This phrase refers to the process of evaluating massive amounts of data in order to derive useful information for making decisions in a variety of fields. If credit risk is considered, a method based on an examination of what caused and how mortgage credit risk affected credit defaults during the still-current economic crisis of 2021 will be tried. Various approaches to credit risk calculation will be examined, ranging from the most basic to the most complex. In addition, we will conduct a case study on a sample of mortgage loans and compare the results of three different analytical approaches, logistic regression, decision tree, and gradient boost to see which one produced the most commercially useful insights.https://www.sciopen.com/article/10.26599/BDMA.2022.9020037artificial intelligence (ai)machine learning (ml)loan predictionsupport vector machine (svm)random forest (rf)accuracy |
| spellingShingle | Vikas Kumar Shaiku Shahida Saheb Preeti Atif Ghayas Sunil Kumari Jai Kishan Chandel Saroj Kumar Pandey Santosh Kumar AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector Big Data Mining and Analytics artificial intelligence (ai) machine learning (ml) loan prediction support vector machine (svm) random forest (rf) accuracy |
| title | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector |
| title_full | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector |
| title_fullStr | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector |
| title_full_unstemmed | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector |
| title_short | AI-Based Hybrid Models for Predicting Loan Risk in the Banking Sector |
| title_sort | ai based hybrid models for predicting loan risk in the banking sector |
| topic | artificial intelligence (ai) machine learning (ml) loan prediction support vector machine (svm) random forest (rf) accuracy |
| url | https://www.sciopen.com/article/10.26599/BDMA.2022.9020037 |
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