跳到主要內容

臺灣博碩士論文加值系統

(44.210.151.5) 您好!臺灣時間:2024/07/13 10:22
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:黃鏸誼
研究生(外文):Hui-I Huang
論文名稱:機器學習在個人貸款承作預測分析
論文名稱(外文):Predictive Analysis for Personal Loans by Using Machine Learning
指導教授:王昭文王昭文引用關係
指導教授(外文):Wang, Chou-Wen
學位類別:碩士
校院名稱:國立中山大學
系所名稱:國際資產管理研究所
學門:商業及管理學門
學類:其他商業及管理學類
論文種類:學術論文
論文出版年:2024
畢業學年度:112
語文別:中文
論文頁數:66
中文關鍵詞:銀行機器學習個人貸款支持向量機梯度提升模型
外文關鍵詞:BankMachine LearningPersonal LoansSupport Vector MachineGradient Boosting Model
相關次數:
  • 被引用被引用:0
  • 點閱點閱:43
  • 評分評分:
  • 下載下載:15
  • 收藏至我的研究室書目清單書目收藏:0
本研究採用五種常見的機器學習演算法進行消費者是否承作個人貸款的預測,包括邏輯迴歸、支持向量機、多層感知器、梯度提升決策樹Catboost及Xgboost等五種模型。本研究使用來自公開資料庫Kaggle之Thera Bank資料,其中欄位包含年齡、工作經驗、收入、家庭人數、信用卡平均支出、教育程度、房貸、證券帳戶、存款帳戶、網銀使用狀況等,採用SMOTE (Synthetic Minority Over-sampling Technique)方法處理不平衡資料的問題,並以五種模型搭配三種不同的抽樣次數,多方比較各項模型預測的準確性和穩定性,找出機最佳模型及關鍵因子。經由本研究實證結果發現,以梯度提升Catboost模型及支持向量機模型在不同的抽樣比例下表現出穩定且精準的結果,可以被視為最佳模型;此外,透過梯度提升Xgboost模型,本研究歸納出關鍵特徵,如教育因素、收入、家庭成員人數、是否開立存款帳戶及每年信用卡消費等,本研究的結果可為日後金融機構制定個人貸款行銷策略時,提供關鍵參考因子。
This study adopts five common machine learning algorithms for predicting consumer personal loan uptake, including Logistic Regression, Support Vector Machine, Multilayer Perceptron, Gradient Boosting Decision Trees Catboost, and Xgboost. The research utilizes data from Thera Bank available in the public database Kaggle, featuring fields like age, work experience, income, family size, average credit card expenditure, education level, home loans, securities account, deposit account, and internet banking usage. The study addresses the issue of imbalanced data using the SMOTE (Synthetic Minority Over-sampling Technique) method and compares the accuracy and stability of predictions using the five models with three different sampling rates to identify the optimal model and key factors. Empirical results show that the Gradient Boosting Catboost model and the Support Vector Machine model perform with stability and precision across different sampling ratios, making them the best models. Moreover, through the Gradient Boosting Xgboost model, the study identifies key features such as educational factors, income, family size, the existence of a deposit account, and annual credit card spending. The findings of this research can provide crucial factors for financial institutions when formulating marketing strategies for personal loans.
審定書i
公開授權書ii
摘要iii
Abstractiv
第一章 緒論1
第一節 研究動機與現況1
第二節 研究目的3
第三節 研究貢獻3
第四節 研究架構4
第二章 文獻探討6
第一節 機器學習模型應用在金融領域6
第二節 機器學習模型結合個人貸款預測8
第三章 研究方法11
第一節 資料來源11
第二節 個案因子探討11
第三節 機器學習模型15
第四節 衡量模型預測能力23
第四章 實證結果25
第一節 敘述統計25
第二節 機器學習模型結果分析29
第三節 最佳預測模型48
第四節 Xgboost重要因子52
第五章 結論與建議55
第一節 結論55
第二節 研究建議56
參考文獻57
中文文獻
1.吳泰緯(2023)。運用機器學習於台股價格突破策略之實證分析。﹝碩士論文。國立中山大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/28f5hy。
2.劉家宏(2023)。XGBoost模型在小型台灣指數期貨價格預測之實證分析。﹝碩士論文。國立中山大學﹞臺灣博碩士論文知識加值系統。 https://hdl.handle.net/11296/58u42p。

英文文獻
1.Akça, M. F., & Sevli, O. (2022). Predicting acceptance of the bank loan offers by using support vector machines. International Advanced Researches and Engineering Journal, 6(2), 142-147.
2.Agarwal, K., Jain, M., & Kumawat, A. (2022). Comparing Classification Algorithms on Predicting Loans. In Information Systems and Management Science: Conference Proceedings of 3rd International Conference on Information Systems and Management Science (ISMS) 2020 (pp. 240-249). Springer International Publishing.
3.Amari, S. (1967). A theory of adaptive pattern classifiers. IEEE Transactions on Electronic Computers, (3), 299-307.
4.Anand, M., Velu, A., & Whig, P. (2022). Prediction of loan behaviour with machine learning models for secure banking. Journal of Computer Science and Engineering (JCSE), 3(1), 1-13.
5.Arun, K., G. Ishan, and K. Sanmeet, Loan approval prediction based on machine learning approach. IOSR J. Comput. Eng, 2016. 18(3): p. 18-21.
6.Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992, July). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational learning theory (pp. 144-152).
7.Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society Series B: Statistical Methodology, 20(2), 215-232.
8.Cramer, J. S. (2002). The origins of logistic regression.
9.Eletter, S. F., & Yaseen, S. G. (2017). Loan decision models for the Jordanian commercial banks. Global Business and Economics Review, 19(3), 323-338.
10.Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 1-24.
11.Ibrahim, A. A., Ridwan, R. L., Muhammed, M. M., Abdulaziz, R. O., & Saheed, G. A. (2020). Comparison of the CatBoost classifier with other machine learning methods. International Journal of Advanced Computer Science and Applications, 11(11).
12.Li, X., Ergu, D., Zhang, D., Qiu, D., Cai, Y., & Ma, B. (2022). Prediction of loan default based on multi-model fusion. Procedia Computer Science, 199, 757-764.
13.Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504.
14.Nosratabadi, S., Mosavi, A., Duan, P., Ghamisi, P., Filip, F., Band, S. S., ... & Gandomi, A. H. (2020). Data science in economics: comprehensive review of advanced machine learning and deep learning methods. Mathematics, 8(10), 1799.
15.Prasad, K.G.S., P.V.S. Chidvilas, and V.V. Kumar, Customer loan approval classification by supervised learning model. International Journal of Recent Technology and Engineering, 2019. 8(4): 9898-9901.
16.Sreesouthry, S., A. Ayubkhan, M.M. Rizwan, D. Lokesh, and K.P. Raj, Loan Prediction Using Logistic Regression in Machine Learning. Annals of the Romanian Society for Cell Biology, 2021. 25(4): p. 2790-2794.
17.Tax, N., de Vries, K. J., de Jong, M., Dosoula, N., van den Akker, B., Smith, J., ... & Bernardi, L. (2021). Machine learning for fraud detection in e-Commerce: A research agenda. In Deployable Machine Learning for Security Defense: Second International Workshop, MLHat 2021, Virtual Event, August 15, 2021, Proceedings 2 (pp. 30-54). Springer International Publishing.
18.Zhang, D., Gong, Y., Yu, L., & Wang, X. (2020, October). P2P Online Loan Willingness Prediction and Influencing Factors Analysis Based on Factor Analysis and XGBoost. In Journal of Physics: Conference Series (Vol. 1624, No. 4, p. 042039). IOP Publishing.
19.Zhu, Y., Zhou, L., Xie, C., Wang, G. J., & Nguyen, T. V. (2019). Forecasting SMEs'' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach. International Journal of Production Economics, 211, 22-33.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
無相關期刊