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An Analysis of Residual Financial Contagion in Romania’s Banking Market for Mortgage Loans. (2023). Ionescu, Tefan ; Chiri, Nora ; Nica, Ionu ; Delcea, Camelia.
In: Sustainability.
RePEc:gam:jsusta:v:15:y:2023:i:15:p:12037-:d:1211596.

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    RePEc:zbw:kbawps:61.

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  7. A comparative study of corporate credit ratings prediction with machine learning. (2022). Buyukkor, Yasin ; Doan, Seyyide ; Atan, Murat.
    In: Operations Research and Decisions.
    RePEc:wut:journl:v:32:y:2022:i:1:p:25-47:id:2643.

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  8. Credit Scoring with Drift Adaptation Using Local Regions of Competence. (2022). Doumpos, Michalis ; Nikolaidis, Dimitrios.
    In: SN Operations Research Forum.
    RePEc:spr:snopef:v:3:y:2022:i:4:d:10.1007_s43069-022-00177-1.

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  9. Dangerous liasons and hot customers for banks. (2022). Cerqueti, Roy ; Pampurini, Francesca ; Quaranta, Anna Grazia ; Pezzola, Annagiulia.
    In: Review of Quantitative Finance and Accounting.
    RePEc:kap:rqfnac:v:59:y:2022:i:1:d:10.1007_s11156-022-01039-x.

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  10. Exploitation of Machine Learning Algorithms for Detecting Financial Crimes Based on Customers’ Behavior. (2022). Ahmad, Tauseef ; Kumar, Sanjay ; Bharany, Salil ; Ahmed, Rafeeq ; Shafiq, Muhammad ; Shuaib, Mohammed ; Eldin, Elsayed Tag ; Ur, Ateeq.
    In: Sustainability.
    RePEc:gam:jsusta:v:14:y:2022:i:21:p:13875-:d:953058.

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  11. The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank. (2022). de Lange, Petter Eilif ; Hjelkrem, Lars Ole ; Nesset, Erik.
    In: JRFM.
    RePEc:gam:jjrfmx:v:15:y:2022:i:12:p:597-:d:1000763.

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  12. Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring. (2022). Schmitt, Marc.
    In: Papers.
    RePEc:arx:papers:2205.10535.

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  13. Deep Learning in Business Analytics: A Clash of Expectations and Reality. (2022). Schmitt, Marc Andreas.
    In: Papers.
    RePEc:arx:papers:2205.09337.

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  14. To supervise or to self-supervise: a machine learning based comparison on credit supervision. (2021). Pereira, Jose Americo.
    In: Financial Innovation.
    RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00242-4.

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  15. Accuracies of some Learning or Scoring Models for Credit Risk Measurement. (2021). SADEFO KAMDEM, Jules ; Osei, Salomey ; Fadugba, Jeremiah ; Mpinda, Berthine Nyunga.
    In: Working Papers.
    RePEc:hal:wpaper:hal-03194081.

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  16. Automated Valuation Modelling: Analysing Mortgage Behavioural Life Profile Models Using Machine Learning Techniques. (2021). Ionescu, Tefan ; Paramon, Simona Liliana ; Nica, Ionu ; Alexandru, Daniela Blan.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:9:p:5162-:d:549169.

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  17. Mechanism Underlying the Formation of Virtual Agglomeration of Creative Industries: Theoretical Analysis and Empirical Research. (2021). Jiang, Yao ; Chen, XU ; Liu, Chunhong ; Gao, Changchun.
    In: Sustainability.
    RePEc:gam:jsusta:v:13:y:2021:i:4:p:1637-:d:492716.

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  18. A New Model Averaging Approach in Predicting Credit Risk Default. (2021). Cucculelli, Marco ; Jha, Paritosh Navinchandra.
    In: Risks.
    RePEc:gam:jrisks:v:9:y:2021:i:6:p:114-:d:570809.

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  19. A Machine Learning Approach for Micro-Credit Scoring. (2021). Nde, Titus Nyarko ; Date, Paresh ; Constantinescu, Corina ; Ampountolas, Apostolos.
    In: Risks.
    RePEc:gam:jrisks:v:9:y:2021:i:3:p:50-:d:513405.

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  20. Deep learning for credit scoring: Do or don’t?. (2021). Lemahieu, Wilfried ; Broucke, Seppe Vanden ; Oskarsdottir, Maria ; Gunnarsson, Bjorn Rafn ; Baesens, Bart.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:295:y:2021:i:1:p:292-305.

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  21. Predicting mortgage early delinquency with machine learning methods. (2021). Guo, Zhengfeng ; Zhao, Xinlei ; Chen, Shunqin.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:290:y:2021:i:1:p:358-372.

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  22. Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management. (2021). Redzepagic, Srdjan ; Milojevi, Nenad.
    In: Journal of Central Banking Theory and Practice.
    RePEc:cbk:journl:v:10:y:2021:i:3:p:41-57.

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  23. Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach. (2021). Naik, K S.
    In: Papers.
    RePEc:arx:papers:2110.02206.

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  24. A Sparsity Algorithm with Applications to Corporate Credit Rating. (2021). Chen, Zhi ; Florescu, Ionut ; Wang, Dan.
    In: Papers.
    RePEc:arx:papers:2107.10306.

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  25. Transparency of credit institutions. (2020). Bulyga, Roman P ; Kashirskaya, Liudmila V ; Safonova, Irina V ; Sitnov, Alexey A.
    In: Entrepreneurship and Sustainability Issues.
    RePEc:ssi:jouesi:v:7:y:2020:i:4:p:3158-3172.

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  26. Bankruptcy Prediction Using Deep Learning Approach Based on Borderline SMOTE. (2020). Smiti, Salima ; Soui, Makram.
    In: Information Systems Frontiers.
    RePEc:spr:infosf:v:22:y:2020:i:5:d:10.1007_s10796-020-10031-6.

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  27. Deep Learning and Implementations in Banking. (2020). Huang, XU ; Ghodsi, Mansi ; Hassani, Hossein ; Silva, Emmanuel.
    In: Annals of Data Science.
    RePEc:spr:aodasc:v:7:y:2020:i:3:d:10.1007_s40745-020-00300-1.

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  28. Comparison study of two-step LGD estimation model with probability machines. (2020). Tanoue, Yuta ; Yamashita, Satoshi ; Nagahata, Hideaki.
    In: Risk Management.
    RePEc:pal:risman:v:22:y:2020:i:3:d:10.1057_s41283-020-00059-y.

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  29. Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics. (2020). Faghan, Yaser ; Ardabili, Sina Faizollahzadeh ; Band, Shahab S ; Duan, Puhong ; Ghamisi, Pedram ; Salwana, Ely ; Mosavi, Amirhosein.
    In: Mathematics.
    RePEc:gam:jmathe:v:8:y:2020:i:10:p:1640-:d:417900.

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  30. Bayesian regularized artificial neural networks for the estimation of the probability of default. (2020). Germano, Guido ; Sariev, Eduard.
    In: LSE Research Online Documents on Economics.
    RePEc:ehl:lserod:101029.

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  31. Can deep learning predict risky retail investors? A case study in financial risk behavior forecasting. (2020). Yang, Y ; Ma, T ; Sung, M.-C., ; Lessmann, S ; Johnson, J. E. V., ; Kim, A.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:283:y:2020:i:1:p:217-234.

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  32. A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees. (2020). Chatterjee, Rupak ; Golbayani, Parisa ; Florescu, Ionu.
    In: The North American Journal of Economics and Finance.
    RePEc:eee:ecofin:v:54:y:2020:i:c:s1062940820301480.

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  33. Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data. (2020). Yousefi, Nooshin ; Clements, Jillian M ; Efimov, Dmitry ; Xu, DI.
    In: Papers.
    RePEc:arx:papers:2012.15330.

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  34. Machine Learning approach for Credit Scoring. (2020). Massaron, L ; Giada, L ; le Pera, G ; Trifiro, D ; Nordio, C ; Provenzano, A R ; Riciputi, A ; Datteo, A ; Jean, N ; Spadaccino, M.
    In: Papers.
    RePEc:arx:papers:2008.01687.

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  35. A comparative study of forecasting Corporate Credit Ratings using Neural Networks, Support Vector Machines, and Decision Trees. (2020). Chatterjee, Rupak ; Golbayani, Parisa ; Florescu, Ionuct.
    In: Papers.
    RePEc:arx:papers:2007.06617.

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  36. Intelligent Credit Limit Management in Consumer Loans Based on Causal Inference. (2020). Wang, Zhun ; Fang, Yanming ; Yu, Quan ; Jia, Quanhui ; Jiang, Linbo ; Zhao, Kui ; Miao, Hang.
    In: Papers.
    RePEc:arx:papers:2007.05188.

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  37. Determining Secondary Attributes for Credit Evaluation in P2P Lending. (2020). Bhuvaneswari, Revathi ; Segalini, Antonio.
    In: Papers.
    RePEc:arx:papers:2006.13921.

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  38. Can Deep Learning Predict Risky Retail Investors? A Case Study in Financial Risk Behavior Forecasting. (2019). Johnson, J. E. V., ; Kolesnikova, A ; Yang, Y ; Ma, T ; Sung, M.-C., ; Lessmann, S.
    In: IRTG 1792 Discussion Papers.
    RePEc:zbw:irtgdp:2019023.

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  39. Estimation Procedures of Using Five Alternative Machine Learning Methods for Predicting Credit Card Default. (2019). Lee, Michael ; Teng, Huei-Wen.
    In: Review of Pacific Basin Financial Markets and Policies (RPBFMP).
    RePEc:wsi:rpbfmp:v:22:y:2019:i:03:n:s0219091519500218.

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  40. Artificial Intelligence, Data, Ethics. An Holistic Approach for Risks and Regulation. (2019). Guegan, Dominique ; Bogroff, Alexis.
    In: Working Papers.
    RePEc:ven:wpaper:2019:19.

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  41. A chemical-reaction-optimization-based neuro-fuzzy hybrid network for stock closing price prediction. (2019). Nayak, Sarat Chandra ; Misra, Bijan Bihari.
    In: Financial Innovation.
    RePEc:spr:fininn:v:5:y:2019:i:1:d:10.1186_s40854-019-0153-1.

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  42. Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation. (2019). Guegan, Dominique ; Bogroff, Alexis.
    In: Post-Print.
    RePEc:hal:journl:halshs-02181597.

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  43. Artificial Intelligence, Data, Ethics: An Holistic Approach for Risks and Regulation. (2019). Bogroff, Alexis ; Guegan, Dominique.
    In: Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers).
    RePEc:hal:cesptp:halshs-02181597.

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  44. Risk Measurement. (2019). Hassani, Bertrand K ; Guegan, Dominique.
    In: Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers).
    RePEc:hal:cesptp:halshs-02119256.

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  45. Machine Learning in Banking Risk Management: A Literature Review. (2019). Sharma, Suneel ; Leo, Martin ; Maddulety, K.
    In: Risks.
    RePEc:gam:jrisks:v:7:y:2019:i:1:p:29-:d:211265.

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  46. A robust machine learning approach for credit risk analysis of large loan-level datasets using deep learning and extreme gradient boosting. (2019). Siakoulis, Vasilis ; Stavroulakis, Evaggelos ; Klamargias, Aristotelis ; Petropoulos, Anastasios.
    In: IFC Bulletins chapters.
    RePEc:bis:bisifc:50-22.

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  47. A robust machine learning approach for credit risk analysis of large loan level datasets using deep learning and extreme gradient boosting. (2019). Siakoulis, Vasilis ; Stavroulakis, Evaggelos ; Klamargias, Aristotelis ; Petropoulos, Anastasios.
    In: IFC Bulletins chapters.
    RePEc:bis:bisifc:49-49.

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  48. The use of big data analytics and artificial intelligence in central banking. (2019). Committee, Irving Fisher.
    In: IFC Bulletins.
    RePEc:bis:bisifb:50.

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  49. Insolvency prediction for Portuguese agro-industrial SME: Tree Bagging Methodology. (2019). Canto, Jose Augusto ; Leite, Gabriela ; Machado-Santos, Carlos ; Ferreira, Amelia Cristina.
    In: Agricultural Economics Review.
    RePEc:ags:aergaa:330639.

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  50. The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans. (2018). Ghulam, Yaseen ; Hill, Sophie ; Naseem, Sana ; Dhruva, Kamini.
    In: Risks.
    RePEc:gam:jrisks:v:6:y:2018:i:3:p:101-:d:169957.

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