Deep learning for credit scoring: Do or don’t?
•Deep learning techniques are compared to both conventional methods and ensemble methods for credit scoring.•This comparison is executed over a significant number of credit scoring data sets stemming from a real-life environment.•The models are evaluated and compared over a number of performance mea...
Uloženo v:
| Vydáno v: | European journal of operational research Ročník 295; číslo 1; s. 292 - 305 |
|---|---|
| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
16.11.2021
|
| Témata: | |
| ISSN: | 0377-2217, 1872-6860 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | •Deep learning techniques are compared to both conventional methods and ensemble methods for credit scoring.•This comparison is executed over a significant number of credit scoring data sets stemming from a real-life environment.•The models are evaluated and compared over a number of performance measures.•Bayesian hypothesis testing is considered and compared to an advanced non-parametric statistical testing procedure.
Developing accurate analytical credit scoring models has become a major focus for financial institutions. For this purpose, numerous classification algorithms have been proposed for credit scoring. However, the application of deep learning algorithms for classification has been largely ignored in the credit scoring literature. The main motivation for this research is to consider the appropriateness of deep learning algorithms for credit scoring. To this end two deep learning architectures are constructed, namely a multilayer perceptron network and a deep belief network, and their performance compared to that of two conventional methods and two ensemble methods for credit scoring. The models are then evaluated using a range of credit scoring data sets and performance measures. Furthermore, Bayesian statistical testing procedures are introduced in the context of credit scoring and compared to frequentist non-parametric testing procedures which have traditionally been considered best practice in credit scoring. This comparison will highlight the benefits of Bayesian statistical procedures and secure empirical findings. Two main conclusions emerge from comparing the different classification algorithms for credit scoring. Firstly, the ensemble method, XGBoost, is the best performing method for credit scoring of all the methods considered here. Secondly, deep neural networks do not outperform their shallower counterparts and are considerably more computationally expensive to construct. Therefore, deep learning algorithms do not seem to be appropriate models for credit scoring based on this comparison and XGBoost should be preferred over the other credit scoring methods considered here when classification performance is the main objective of credit scoring activities. |
|---|---|
| AbstractList | •Deep learning techniques are compared to both conventional methods and ensemble methods for credit scoring.•This comparison is executed over a significant number of credit scoring data sets stemming from a real-life environment.•The models are evaluated and compared over a number of performance measures.•Bayesian hypothesis testing is considered and compared to an advanced non-parametric statistical testing procedure.
Developing accurate analytical credit scoring models has become a major focus for financial institutions. For this purpose, numerous classification algorithms have been proposed for credit scoring. However, the application of deep learning algorithms for classification has been largely ignored in the credit scoring literature. The main motivation for this research is to consider the appropriateness of deep learning algorithms for credit scoring. To this end two deep learning architectures are constructed, namely a multilayer perceptron network and a deep belief network, and their performance compared to that of two conventional methods and two ensemble methods for credit scoring. The models are then evaluated using a range of credit scoring data sets and performance measures. Furthermore, Bayesian statistical testing procedures are introduced in the context of credit scoring and compared to frequentist non-parametric testing procedures which have traditionally been considered best practice in credit scoring. This comparison will highlight the benefits of Bayesian statistical procedures and secure empirical findings. Two main conclusions emerge from comparing the different classification algorithms for credit scoring. Firstly, the ensemble method, XGBoost, is the best performing method for credit scoring of all the methods considered here. Secondly, deep neural networks do not outperform their shallower counterparts and are considerably more computationally expensive to construct. Therefore, deep learning algorithms do not seem to be appropriate models for credit scoring based on this comparison and XGBoost should be preferred over the other credit scoring methods considered here when classification performance is the main objective of credit scoring activities. |
| Author | vanden Broucke, Seppe Lemahieu, Wilfried Gunnarsson, Björn Rafn Óskarsdóttir, María Baesens, Bart |
| Author_xml | – sequence: 1 givenname: Björn Rafn orcidid: 0000-0002-5062-5354 surname: Gunnarsson fullname: Gunnarsson, Björn Rafn email: bjornrafn.gunnarsson@kuleuven.be organization: Research Center for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, Leuven 3000, Belgium – sequence: 2 givenname: Seppe surname: vanden Broucke fullname: vanden Broucke, Seppe email: seppe.vandenbroucke@kuleuven.be organization: Research Center for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, Leuven 3000, Belgium – sequence: 3 givenname: Bart surname: Baesens fullname: Baesens, Bart email: bart.baesens@kuleuven.be organization: Research Center for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, Leuven 3000, Belgium – sequence: 4 givenname: María surname: Óskarsdóttir fullname: Óskarsdóttir, María email: mariaoskars@ru.is organization: Department of Computer Science, Reykjavík University, Menntavegi 1, Reykjavík 101, Iceland – sequence: 5 givenname: Wilfried surname: Lemahieu fullname: Lemahieu, Wilfried email: wilfried.lemahieu@kuleuven.be organization: Research Center for Information Systems Engineering (LIRIS), KU Leuven, Naamsestraat 69, Leuven 3000, Belgium |
| BookMark | eNp9kM1KAzEURoNUsK2-gKt5gRlvEpOZEUGk9Q8KbnQd0js3kqFOShIEd76Gr-eTOKWuXHR14cD54NwZmwxhIMbOOVQcuL7oK-pDrAQIXoGsAPQRm_KmFqVuNEzYFGRdl0Lw-oTNUuoBgCuupgyWRNtiQzYOfngrXIgFRup8LhKGOKKrYhmKkXZh-Pn6zjen7NjZTaKzvztnr_d3L4vHcvX88LS4XZUotcqlBudAY8fJ6g4vlXOoqGub1gmFsnZ6razTFhDF2rbYNErSCCRy2dp6reScNftdjCGlSM6gzzb7MORo_cZwMLty05tdudmVG5BmLB9V8U_dRv9u4-dh6Xov0Rj14SmahJ4GHH8RCbPpgj-k_wJUbHVz |
| CitedBy_id | crossref_primary_10_1016_j_eswa_2025_126448 crossref_primary_10_1016_j_ejor_2024_12_014 crossref_primary_10_1007_s10115_023_01943_1 crossref_primary_10_1016_j_bar_2023_101241 crossref_primary_10_3390_jrfm15120597 crossref_primary_10_1080_00207543_2023_2257807 crossref_primary_10_1016_j_eswa_2022_118143 crossref_primary_10_1016_j_eswa_2023_119599 crossref_primary_10_1111_jori_12452 crossref_primary_10_3390_sci6040074 crossref_primary_10_1007_s10479_025_06528_5 crossref_primary_10_1016_j_ejor_2025_06_027 crossref_primary_10_1111_exsy_13259 crossref_primary_10_1186_s40537_022_00665_5 crossref_primary_10_1145_3728366 crossref_primary_10_1002_for_2891 crossref_primary_10_1007_s10462_023_10697_9 crossref_primary_10_1016_j_ejor_2021_06_023 crossref_primary_10_1016_j_ejor_2023_08_027 crossref_primary_10_3390_axioms12040402 crossref_primary_10_1080_03081079_2025_2515980 crossref_primary_10_1016_j_jsmc_2021_05_004 crossref_primary_10_1016_j_ejor_2025_01_040 crossref_primary_10_1016_j_eswa_2025_127893 crossref_primary_10_1002_ijfe_3097 crossref_primary_10_1016_j_datak_2025_102490 crossref_primary_10_1016_j_elerap_2023_101292 crossref_primary_10_3846_tede_2022_17045 crossref_primary_10_1016_j_ejor_2023_08_040 crossref_primary_10_1186_s40854_024_00689_1 crossref_primary_10_1016_j_ejor_2022_06_035 crossref_primary_10_1016_j_ejor_2025_04_020 crossref_primary_10_1016_j_aei_2023_102227 crossref_primary_10_1016_j_eswa_2023_119882 crossref_primary_10_1016_j_ejor_2023_08_039 crossref_primary_10_4018_JGIM_308806 crossref_primary_10_1093_jcde_qwad039 crossref_primary_10_1016_j_techfore_2024_123491 crossref_primary_10_3390_math11051137 crossref_primary_10_1007_s10479_023_05209_5 crossref_primary_10_1080_14765284_2022_2162246 crossref_primary_10_1016_j_ejor_2025_01_022 crossref_primary_10_1016_j_ijforecast_2024_07_005 crossref_primary_10_3390_electronics11193181 crossref_primary_10_1007_s10618_023_00985_x crossref_primary_10_1016_j_techfore_2023_123008 crossref_primary_10_1007_s10479_025_06754_x crossref_primary_10_1177_14738716231180803 crossref_primary_10_3390_jrfm16040221 crossref_primary_10_1007_s10479_024_06385_8 crossref_primary_10_1109_TKDE_2025_3544284 crossref_primary_10_1016_j_engappai_2025_110009 crossref_primary_10_1016_j_ejor_2023_09_009 crossref_primary_10_1016_j_pacfin_2024_102612 crossref_primary_10_1155_2024_5585575 crossref_primary_10_1016_j_ejor_2024_09_025 crossref_primary_10_1007_s10479_025_06621_9 crossref_primary_10_3846_tede_2025_23060 crossref_primary_10_1007_s10479_023_05259_9 crossref_primary_10_1016_j_heliyon_2024_e39286 crossref_primary_10_1080_01605682_2024_2418882 crossref_primary_10_1080_01605682_2024_2339510 crossref_primary_10_25300_MISQ_2024_18340 crossref_primary_10_1007_s43069_022_00177_1 crossref_primary_10_1103_PhysRevResearch_5_043117 crossref_primary_10_1016_j_jhydrol_2024_131767 crossref_primary_10_1016_j_ejor_2023_11_022 crossref_primary_10_1016_j_ejor_2021_12_024 crossref_primary_10_1016_j_jbusres_2025_115349 crossref_primary_10_1080_01605682_2024_2416908 crossref_primary_10_1016_j_jjimei_2025_100323 crossref_primary_10_1016_j_ejor_2025_01_001 crossref_primary_10_1109_TNNLS_2024_3398559 crossref_primary_10_1016_j_ejor_2024_10_046 crossref_primary_10_1016_j_heliyon_2024_e39770 crossref_primary_10_1111_itor_13467 crossref_primary_10_1016_j_eswa_2022_117013 crossref_primary_10_1016_j_ejor_2025_05_029 crossref_primary_10_1007_s00291_024_00787_7 crossref_primary_10_1016_j_ejor_2023_11_013 crossref_primary_10_1016_j_asoc_2021_108160 crossref_primary_10_1016_j_engappai_2023_106056 crossref_primary_10_1016_j_ejco_2025_100115 crossref_primary_10_1016_j_ejor_2023_03_012 crossref_primary_10_1016_j_ejor_2023_09_026 crossref_primary_10_1109_ACCESS_2023_3286018 crossref_primary_10_1016_j_irfa_2021_101971 crossref_primary_10_1016_j_ribaf_2024_102722 crossref_primary_10_1080_01605682_2025_2485263 crossref_primary_10_1111_jori_12427 crossref_primary_10_3390_systems12070254 crossref_primary_10_3390_info16050397 crossref_primary_10_1016_j_elerap_2022_101155 crossref_primary_10_1016_j_eswa_2022_117363 crossref_primary_10_1016_j_ribaf_2023_102186 crossref_primary_10_1108_JEFAS_09_2021_0193 crossref_primary_10_3233_JCM_247181 crossref_primary_10_2139_ssrn_4496106 crossref_primary_10_1111_obes_12592 crossref_primary_10_1080_00036846_2022_2140769 crossref_primary_10_1016_j_patcog_2022_108701 crossref_primary_10_1016_j_ribaf_2022_101869 crossref_primary_10_1016_j_eswa_2023_121484 crossref_primary_10_3389_fams_2022_1076083 crossref_primary_10_3390_ai5040101 crossref_primary_10_1016_j_ribaf_2024_102397 crossref_primary_10_1016_j_tre_2025_103969 crossref_primary_10_1186_s40854_022_00390_1 crossref_primary_10_3233_JIFS_233141 crossref_primary_10_1016_j_eswa_2024_125327 crossref_primary_10_1016_j_tre_2025_104020 crossref_primary_10_1016_j_ejor_2023_06_036 crossref_primary_10_1016_j_jfds_2022_07_002 crossref_primary_10_1016_j_ejor_2022_04_027 crossref_primary_10_1016_j_ejor_2023_12_028 crossref_primary_10_1007_s11518_025_5663_y crossref_primary_10_1016_j_asoc_2025_112771 crossref_primary_10_1016_j_ejor_2025_05_039 crossref_primary_10_1016_j_eswa_2024_124072 crossref_primary_10_1016_j_frl_2024_105937 crossref_primary_10_1002_for_70004 crossref_primary_10_1002_ijfe_70012 |
| Cites_doi | 10.1016/j.neucom.2013.05.020 10.1002/isaf.1437 10.1016/j.ejor.2012.04.009 10.1023/A:1010933404324 10.3758/s13423-016-1221-4 10.1080/00031305.2016.1154108 10.1016/j.ejor.2014.01.044 10.1016/j.ejor.2014.04.001 10.14569/SpecialIssue.2014.040203 10.1016/j.knosys.2020.105758 10.1016/j.eswa.2018.01.012 10.1007/BF02478259 10.1109/ACCESS.2018.2870052 10.3390/risks6020038 10.1016/j.dss.2017.10.007 10.1109/ACCESS.2018.2887138 10.1016/j.engappai.2016.12.002 10.1016/j.ins.2009.12.010 10.1016/j.eswa.2007.12.020 10.1016/j.eswa.2009.05.024 10.1038/nature14539 10.3390/jrfm11010012 10.1007/s10654-016-0149-3 10.1057/palgrave.jors.2601545 10.1016/j.dss.2019.01.002 10.21314/JCR.2005.025 10.1016/j.neunet.2014.09.003 10.1177/1094428112457829 10.1007/s11518-006-5023-5 10.1016/j.ejor.2015.05.030 10.1016/j.ejor.2019.01.072 10.1016/j.ejor.2019.09.018 10.1016/j.asoc.2018.10.004 10.1016/j.ejor.2021.03.008 10.1016/S0169-7439(97)00061-0 10.1126/science.1127647 10.1109/TASL.2011.2109382 10.1038/506150a 10.1007/s10994-017-5641-9 10.1016/j.eswa.2012.03.033 10.1016/S0031-3203(96)00142-2 10.5120/14249-2444 10.1016/j.eswa.2011.06.023 10.1016/j.dss.2005.10.001 10.1016/j.eswa.2017.02.017 |
| ContentType | Journal Article |
| Copyright | 2021 Elsevier B.V. |
| Copyright_xml | – notice: 2021 Elsevier B.V. |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.ejor.2021.03.006 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Computer Science Business |
| EISSN | 1872-6860 |
| EndPage | 305 |
| ExternalDocumentID | 10_1016_j_ejor_2021_03_006 S037722172100196X |
| GroupedDBID | --K --M -~X .DC .~1 0R~ 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 6OB 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABAOU ABBOA ABFNM ABFRF ABJNI ABMAC ABUCO ABYKQ ACAZW ACDAQ ACGFO ACGFS ACIWK ACNCT ACRLP ACZNC ADBBV ADEZE ADGUI AEBSH AEFWE AEKER AENEX AFKWA AFTJW AGHFR AGUBO AGYEJ AHHHB AHZHX AIALX AIEXJ AIGVJ AIKHN AITUG AJOXV ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM ARUGR AXJTR BKOJK BKOMP BLXMC CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W KOM LY1 M41 MHUIS MO0 MS~ N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ RXW SCC SDF SDG SDP SDS SES SPC SPCBC SSB SSD SSV SSW SSZ T5K TAE TN5 U5U XPP ZMT ~02 ~G- 1OL 29G 41~ 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADIYS ADJOM ADMUD ADNMO ADXHL AEIPS AEUPX AFFNX AFJKZ AFPUW AGQPQ AI. AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB HVGLF HZ~ R2- SEW VH1 WUQ ~HD |
| ID | FETCH-LOGICAL-c365t-60ff06cd1ea6dc45ffc5ed989f25c37f6b5af6a0cc2ba9c8853eaf63c139a7b53 |
| ISICitedReferencesCount | 134 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000661773000019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0377-2217 |
| IngestDate | Sat Nov 29 07:24:18 EST 2025 Tue Nov 18 22:41:08 EST 2025 Fri Feb 23 02:41:48 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Decision support systems Deep learning Bayesian statistical testing Risk analysis Credit scoring |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c365t-60ff06cd1ea6dc45ffc5ed989f25c37f6b5af6a0cc2ba9c8853eaf63c139a7b53 |
| ORCID | 0000-0002-5062-5354 |
| PageCount | 14 |
| ParticipantIDs | crossref_citationtrail_10_1016_j_ejor_2021_03_006 crossref_primary_10_1016_j_ejor_2021_03_006 elsevier_sciencedirect_doi_10_1016_j_ejor_2021_03_006 |
| PublicationCentury | 2000 |
| PublicationDate | 2021-11-16 |
| PublicationDateYYYYMMDD | 2021-11-16 |
| PublicationDate_xml | – month: 11 year: 2021 text: 2021-11-16 day: 16 |
| PublicationDecade | 2020 |
| PublicationTitle | European journal of operational research |
| PublicationYear | 2021 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Thomas, Edelman, Crook (bib0063) 2002 Saberi, Mirtalaie, Hussain, Azadeh, Hussain, Ashjari (bib0056) 2013; 122 Hollander, Wolfe, Chicken (bib0027) 2014; 751 McCulloch, Pitts (bib0047) 1943; 5 Wasserstein, Lazar (bib0072) 2016; 70 Xia, Liu, Li, Liu (bib0073) 2017; 78 Marqués, García, Sánchez (bib0046) 2012; 39 Nuzzo (bib0052) 2014; 506 Kraus, Feuerriegel, Oztekin (bib0034) 2020; 281 Benavoli, Corani, Mangili, Zaffalon, Ruggeri (bib0008) 2014 Wang, Han, Liu, Luo (bib0070) 2018; 7 Huang, Hung, Jiau (bib0031) 2006; 7 Dua, D., & Graff, C. (2017). UCI machine learning repository. Demšar (bib0015) 2006; 7 Greenland, Senn, Rothman, Carlin, Poole, Goodman, Altman (bib0021) 2016; 31 Xiao, Zhao, Fei (bib0074) 2006; 15 Mancisidor, R. A., Kampffmeyer, M., Aas, K., & Jenssen, R. (2019). Deep generative models for reject inference in credit scoring. arXiv preprint arXiv Jiang, Wang, Zhao (bib0033) 2019; 277 Van Gestel, Baesens, Van Dijcke, Suykens, Garcia, Alderweireld (bib0066) 2005; 1 Spanoudes, P., & Nguyen, T. (2017). Deep learning in customer churn prediction: Unsupervised feature learning on abstract company independent feature vectors. arXiv preprint arXiv Vinyals, Ravuri (bib0069) 2011 . Hosmer, Lemeshow, Sturdivant (bib0028) 2013; 398 Baesens, Van Gestel, Viaene, Stepanova, Suykens, Vanthienen (bib0006) 2003; 54 Hua, Guo, Zhao (bib0030) 2015 Addo, Guegan, Hassani (bib0002) 2018; 6 Akkoç (bib0003) 2012; 222 Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv Hamori, Kawai, Kume, Murakami, Watanabe (bib0022) 2018; 11 Kruschke, Liddell (bib0037) 2018; 25 Sun, Vasarhelyi (bib0061) 2018; 25 Wang, Yu, Ji (bib0071) 2018 Lopes, Ribeiro (bib0041) 2015 Hssina, Merbouha, Ezzikouri, Erritali (bib0029) 2014; 4 LeCun, Bengio, Hinton (bib0038) 2015; 521 Mohamed, Dahl, Hinton (bib0049) 2012; 20 García, Fernández, Luengo, Herrera (bib0019) 2010; 180 Chen, Guo, Zhao (bib0012) 2020 Verbraken, Bravo, Weber, Baesens (bib0068) 2014; 238 Chen, Guestrin (bib0013) 2016 Hinton, Salakhutdinov (bib0026) 2006; 313 Kruschke, Aguinis, Joo (bib0036) 2012; 15 Benavoli, Corani, Demšar, Zaffalon (bib0007) 2017; 18 Yu, Yao, Wang, Lai (bib0076) 2011; 38 Breiman (bib0011) 2001; 45 Bradley (bib0010) 1997; 30 Van-Sang, Ha-Nam (bib0067) 2016; 54 Corani, Benavoli, Demšar, Mangili, Zaffalon (bib0014) 2017; 106 Maldonado, Bravo, López, Pérez (bib0044) 2017; 104 Munkhdalai, Wang, Park, Ryu (bib0051) 2019 Adadi, Berrada (bib0001) 2018; 6 Kruschke (bib0035) 2011 Van Gestel, Baesens, Van Dijcke, Garcia, Suykens, Vanthienen (bib0065) 2006; 42 Zhou, Lai, Yu (bib0078) 2010; 37 Haykin (bib0023) 1994; 2 Lessmann, Baesens, Seow, Thomas (bib0040) 2015; 247 Board of Governors of the Federal Reserve System (2019). Federal reserve statistical release. [Online; accessed 28-February-2019]. Goodfellow, Bengio, Courville (bib0020) 2016 Baesens (bib0004) 2014 Lesaffre, Lawson (bib0039) 2012 Luo, Wu, Wu (bib0043) 2017; 65 Svozil, Kvasnicka, Pospichal (bib0062) 1997; 39 Mohamed, Sainath, Dahl, Ramabhadran, Hinton, Picheny (bib0050) 2011 Óskarsdóttir, Bravo, Sarraute, Vanthienen, Baesens (bib0053) 2019; 74 Sharma, Agrawal, Sharma (bib0058) 2013; 82 Mohamed, Dahl, Hinton (bib0048) 2009 Hinton (bib0025) 2012 Zhang, Gao, Shi (bib0077) 2014; 237 Schmidhuber (bib0057) 2015; 61 Lundberg, Lee (bib0042) 2017 Tieleman, Hinton (bib0064) 2012; 4 Papouskova, Hajek (bib0054) 2019; 118 Baesens, Roesch, Scheule (bib0005) 2016 Zhu, Yang, Wang, Yuan (bib0079) 2018 Stevenson, M., Mues, C., & Bravo, C. (2020). The value of text for small business default prediction: A deep learning approach. arXiv preprint arXiv Deng (bib0016) 2014; 3 Durand (bib0018) 1941 Yeh, Lien (bib0075) 2009; 36 He, Zhang, Zhang (bib0024) 2018; 98 Ribeiro, Singh, Guestrin (bib0055) 2016 Hinton (10.1016/j.ejor.2021.03.006_bib0026) 2006; 313 Wasserstein (10.1016/j.ejor.2021.03.006_bib0072) 2016; 70 Yu (10.1016/j.ejor.2021.03.006_bib0076) 2011; 38 Sharma (10.1016/j.ejor.2021.03.006_bib0058) 2013; 82 10.1016/j.ejor.2021.03.006_bib0009 He (10.1016/j.ejor.2021.03.006_bib0024) 2018; 98 Kruschke (10.1016/j.ejor.2021.03.006_bib0037) 2018; 25 Lopes (10.1016/j.ejor.2021.03.006_bib0041) 2015 Schmidhuber (10.1016/j.ejor.2021.03.006_bib0057) 2015; 61 García (10.1016/j.ejor.2021.03.006_bib0019) 2010; 180 10.1016/j.ejor.2021.03.006_bib0045 Hinton (10.1016/j.ejor.2021.03.006_bib0025) 2012 Thomas (10.1016/j.ejor.2021.03.006_bib0063) 2002 Van Gestel (10.1016/j.ejor.2021.03.006_bib0066) 2005; 1 Baesens (10.1016/j.ejor.2021.03.006_bib0004) 2014 Chen (10.1016/j.ejor.2021.03.006_bib0013) 2016 Mohamed (10.1016/j.ejor.2021.03.006_sbref0048) 2009 Zhang (10.1016/j.ejor.2021.03.006_bib0077) 2014; 237 10.1016/j.ejor.2021.03.006_bib0017 Xia (10.1016/j.ejor.2021.03.006_bib0073) 2017; 78 Bradley (10.1016/j.ejor.2021.03.006_bib0010) 1997; 30 Baesens (10.1016/j.ejor.2021.03.006_bib0005) 2016 Saberi (10.1016/j.ejor.2021.03.006_bib0056) 2013; 122 Kruschke (10.1016/j.ejor.2021.03.006_bib0036) 2012; 15 Goodfellow (10.1016/j.ejor.2021.03.006_bib0020) 2016 Greenland (10.1016/j.ejor.2021.03.006_bib0021) 2016; 31 Munkhdalai (10.1016/j.ejor.2021.03.006_bib0051) 2019 Chen (10.1016/j.ejor.2021.03.006_bib0012) 2020 Hamori (10.1016/j.ejor.2021.03.006_bib0022) 2018; 11 LeCun (10.1016/j.ejor.2021.03.006_bib0038) 2015; 521 10.1016/j.ejor.2021.03.006_bib0059 Xiao (10.1016/j.ejor.2021.03.006_bib0074) 2006; 15 Benavoli (10.1016/j.ejor.2021.03.006_bib0007) 2017; 18 Lesaffre (10.1016/j.ejor.2021.03.006_bib0039) 2012 Svozil (10.1016/j.ejor.2021.03.006_bib0062) 1997; 39 Demšar (10.1016/j.ejor.2021.03.006_bib0015) 2006; 7 Yeh (10.1016/j.ejor.2021.03.006_bib0075) 2009; 36 Kruschke (10.1016/j.ejor.2021.03.006_bib0035) 2011 Addo (10.1016/j.ejor.2021.03.006_bib0002) 2018; 6 Durand (10.1016/j.ejor.2021.03.006_bib0018) 1941 Kraus (10.1016/j.ejor.2021.03.006_bib0034) 2020; 281 Ribeiro (10.1016/j.ejor.2021.03.006_bib0055) 2016 Zhu (10.1016/j.ejor.2021.03.006_bib0079) 2018 Hollander (10.1016/j.ejor.2021.03.006_bib0027) 2014; 751 Maldonado (10.1016/j.ejor.2021.03.006_bib0044) 2017; 104 Zhou (10.1016/j.ejor.2021.03.006_bib0078) 2010; 37 Van-Sang (10.1016/j.ejor.2021.03.006_bib0067) 2016; 54 Huang (10.1016/j.ejor.2021.03.006_bib0031) 2006; 7 Adadi (10.1016/j.ejor.2021.03.006_bib0001) 2018; 6 Marqués (10.1016/j.ejor.2021.03.006_bib0046) 2012; 39 Tieleman (10.1016/j.ejor.2021.03.006_bib0064) 2012; 4 Lessmann (10.1016/j.ejor.2021.03.006_bib0040) 2015; 247 Deng (10.1016/j.ejor.2021.03.006_bib0016) 2014; 3 Papouskova (10.1016/j.ejor.2021.03.006_bib0054) 2019; 118 10.1016/j.ejor.2021.03.006_bib0060 Verbraken (10.1016/j.ejor.2021.03.006_bib0068) 2014; 238 Haykin (10.1016/j.ejor.2021.03.006_bib0023) 1994; 2 McCulloch (10.1016/j.ejor.2021.03.006_bib0047) 1943; 5 Van Gestel (10.1016/j.ejor.2021.03.006_bib0065) 2006; 42 Mohamed (10.1016/j.ejor.2021.03.006_bib0049) 2012; 20 Mohamed (10.1016/j.ejor.2021.03.006_bib0050) 2011 Hua (10.1016/j.ejor.2021.03.006_bib0030) 2015 Akkoç (10.1016/j.ejor.2021.03.006_bib0003) 2012; 222 Hssina (10.1016/j.ejor.2021.03.006_bib0029) 2014; 4 Lundberg (10.1016/j.ejor.2021.03.006_bib0042) 2017 Hosmer (10.1016/j.ejor.2021.03.006_bib0028) 2013; 398 Óskarsdóttir (10.1016/j.ejor.2021.03.006_bib0053) 2019; 74 10.1016/j.ejor.2021.03.006_bib0032 Nuzzo (10.1016/j.ejor.2021.03.006_bib0052) 2014; 506 Corani (10.1016/j.ejor.2021.03.006_bib0014) 2017; 106 Luo (10.1016/j.ejor.2021.03.006_bib0043) 2017; 65 Baesens (10.1016/j.ejor.2021.03.006_bib0006) 2003; 54 Sun (10.1016/j.ejor.2021.03.006_bib0061) 2018; 25 Wang (10.1016/j.ejor.2021.03.006_bib0071) 2018 Wang (10.1016/j.ejor.2021.03.006_bib0070) 2018; 7 Benavoli (10.1016/j.ejor.2021.03.006_bib0008) 2014 Vinyals (10.1016/j.ejor.2021.03.006_bib0069) 2011 Breiman (10.1016/j.ejor.2021.03.006_bib0011) 2001; 45 Jiang (10.1016/j.ejor.2021.03.006_bib0033) 2019; 277 |
| References_xml | – volume: 2 year: 1994 ident: bib0023 article-title: Neural networks – reference: Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv: – start-page: 1135 year: 2016 end-page: 1144 ident: bib0055 article-title: “Why should I trust you?” Explaining the predictions of any classifier publication-title: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining – volume: 6 start-page: 52138 year: 2018 end-page: 52160 ident: bib0001 article-title: Peeking inside the black-box: A survey on explainable artificial intelligence (XAI) publication-title: IEEE Access – reference: Stevenson, M., Mues, C., & Bravo, C. (2020). The value of text for small business default prediction: A deep learning approach. arXiv preprint arXiv: – start-page: 4596 year: 2011 end-page: 4599 ident: bib0069 article-title: Comparing multilayer perceptron to deep belief network tandem features for robust ASR publication-title: Proceedings of the 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) – start-page: 785 year: 2016 end-page: 794 ident: bib0013 article-title: XGBoost: A scalable tree boosting system publication-title: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining – start-page: 5060 year: 2011 end-page: 5063 ident: bib0050 article-title: Deep belief networks using discriminative features for phone recognition. publication-title: Proceedings of the ICASSP – volume: 3 year: 2014 ident: bib0016 article-title: A tutorial survey of architectures, algorithms, and applications for deep learning publication-title: APSIPA Transactions on Signal and Information Processing – volume: 237 start-page: 335 year: 2014 end-page: 348 ident: bib0077 article-title: Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors publication-title: European Journal of Operational Research – volume: 39 start-page: 10916 year: 2012 end-page: 10922 ident: bib0046 article-title: Two-level classifier ensembles for credit risk assessment publication-title: Expert Systems with Applications – start-page: 205 year: 2018 end-page: 208 ident: bib0079 article-title: A hybrid deep learning model for consumer credit scoring publication-title: Proceedings of the 2018 international conference on artificial intelligence and big data (ICAIBD) – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0011 article-title: Random forests publication-title: Machine Learning – volume: 30 start-page: 1145 year: 1997 end-page: 1159 ident: bib0010 article-title: The use of the area under the ROC curve in the evaluation of machine learning algorithms publication-title: Pattern Recognition – start-page: 599 year: 2012 end-page: 619 ident: bib0025 article-title: A practical guide to training restricted Boltzmann machines publication-title: Neural networks: Tricks of the trade – volume: 4 year: 2014 ident: bib0029 article-title: A comparative study of decision tree ID3 and C4.5 publication-title: International Journal of Advanced Computer Science and Applications – year: 2020 ident: bib0012 article-title: Predicting mortgage early delinquency with machine learning methods publication-title: European Journal of Operational Research – start-page: 407 year: 2019 end-page: 419 ident: bib0051 article-title: Advanced neural network approach, its explanation with lime for credit scoring application publication-title: Proceedings of the Asian conference on intelligent information and database systems – volume: 7 start-page: 2161 year: 2018 end-page: 2168 ident: bib0070 article-title: A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM publication-title: IEEE Access – volume: 222 start-page: 168 year: 2012 end-page: 178 ident: bib0003 article-title: An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: The case of turkish credit card data publication-title: European Journal of Operational Research – start-page: 39 year: 2009 ident: bib0048 article-title: Deep belief networks for phone recognition publication-title: Proceedings of the NIPS workshop on deep learning for speech recognition and related applications – reference: Dua, D., & Graff, C. (2017). UCI machine learning repository. – volume: 65 start-page: 465 year: 2017 end-page: 470 ident: bib0043 article-title: A deep learning approach for credit scoring using credit default swaps publication-title: Engineering Applications of Artificial Intelligence – volume: 122 start-page: 100 year: 2013 end-page: 115 ident: bib0056 article-title: A granular computing-based approach to credit scoring modeling publication-title: Neurocomputing – volume: 751 year: 2014 ident: bib0027 article-title: Nonparametric statistical methods – reference: Mancisidor, R. A., Kampffmeyer, M., Aas, K., & Jenssen, R. (2019). Deep generative models for reject inference in credit scoring. arXiv preprint arXiv: – reference: . [Online; accessed 28-February-2019]. – volume: 15 start-page: 419 year: 2006 end-page: 435 ident: bib0074 article-title: A comparative study of data mining methods in consumer loans credit scoring management publication-title: Journal of Systems Science and Systems Engineering – volume: 15 start-page: 722 year: 2012 end-page: 752 ident: bib0036 article-title: The time has come: Bayesian methods for data analysis in the organizational sciences publication-title: Organizational Research Methods – reference: Spanoudes, P., & Nguyen, T. (2017). Deep learning in customer churn prediction: Unsupervised feature learning on abstract company independent feature vectors. arXiv preprint arXiv: – volume: 74 start-page: 26 year: 2019 end-page: 39 ident: bib0053 article-title: The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics publication-title: Applied Soft Computing – year: 2016 ident: bib0005 article-title: Credit risk analytics: Measurement techniques, applications, and examples in SAS – volume: 82 year: 2013 ident: bib0058 article-title: Classification through machine learning technique: C4.5 algorithm based on various entropies publication-title: International Journal of Computer Applications – volume: 11 start-page: 12 year: 2018 ident: bib0022 article-title: Ensemble learning or deep learning? Application to default risk analysis publication-title: Journal of Risk and Financial Management – year: 2014 ident: bib0004 article-title: Analytics in a big data world: The essential guide to data science and its applications – reference: Board of Governors of the Federal Reserve System (2019). Federal reserve statistical release. – volume: 31 start-page: 337 year: 2016 end-page: 350 ident: bib0021 article-title: Statistical tests, publication-title: European Journal of Epidemiology – volume: 281 start-page: 628 year: 2020 end-page: 641 ident: bib0034 article-title: Deep learning in business analytics and operations research: Models, applications and managerial implications publication-title: European Journal of Operational Research – start-page: 1 year: 2015 end-page: 4 ident: bib0030 article-title: Deep belief networks and deep learning publication-title: Proceedings of the 2014 international conference on intelligent computing and internet of things (ICIT) – year: 2002 ident: bib0063 article-title: Credit scoring and its applications – volume: 104 start-page: 113 year: 2017 end-page: 121 ident: bib0044 article-title: Integrated framework for profit-based feature selection and SVM classification in credit scoring publication-title: Decision Support Systems – year: 2012 ident: bib0039 article-title: Bayesian biostatistics – volume: 54 year: 2016 ident: bib0067 article-title: Credit scoring with a feature selection approach based deep learning publication-title: Proceedings of the MATEC web of conferences – start-page: 1026 year: 2014 end-page: 1034 ident: bib0008 article-title: A Bayesian Wilcoxon signed-rank test based on the Dirichlet process publication-title: Proceedings of the international conference on machine learning – volume: 180 start-page: 2044 year: 2010 end-page: 2064 ident: bib0019 article-title: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power publication-title: Information Sciences – volume: 313 start-page: 504 year: 2006 end-page: 507 ident: bib0026 article-title: Reducing the dimensionality of data with neural networks publication-title: Science – volume: 6 start-page: 38 year: 2018 ident: bib0002 article-title: Credit risk analysis using machine and deep learning models publication-title: Risks – volume: 398 year: 2013 ident: bib0028 article-title: Applied logistic regression – volume: 4 start-page: 26 year: 2012 end-page: 31 ident: bib0064 article-title: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude publication-title: COURSERA: Neural Networks for Machine Learning – volume: 70 start-page: 129 year: 2016 end-page: 133 ident: bib0072 article-title: The ASA’s statement on publication-title: The American Statistician – volume: 36 start-page: 2473 year: 2009 end-page: 2480 ident: bib0075 article-title: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients publication-title: Expert Systems with Applications – volume: 1 year: 2005 ident: bib0066 article-title: Linear and nonlinear credit scoring by combining logistic regression and support vector machines publication-title: Journal of Credit Risk – volume: 238 start-page: 505 year: 2014 end-page: 513 ident: bib0068 article-title: Development and application of consumer credit scoring models using profit-based classification measures publication-title: European Journal of Operational Research – volume: 106 start-page: 1817 year: 2017 end-page: 1837 ident: bib0014 article-title: Statistical comparison of classifiers through Bayesian hierarchical modelling publication-title: Machine Learning – volume: 20 start-page: 14 year: 2012 end-page: 22 ident: bib0049 article-title: Acoustic modeling using deep belief networks publication-title: IEEE Transactions on Audio, Speech, and Language Processing – volume: 54 start-page: 627 year: 2003 end-page: 635 ident: bib0006 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring publication-title: Journal of the Operational Research Society – year: 1941 ident: bib0018 article-title: Risk elements in consumer installment financing – year: 2011 ident: bib0035 article-title: Doing Bayesian data analysis: A tutorial with R and BUGS – volume: 61 start-page: 85 year: 2015 end-page: 117 ident: bib0057 article-title: Deep learning in neural networks: An overview publication-title: Neural Networks – volume: 277 start-page: 20 year: 2019 end-page: 31 ident: bib0033 article-title: A prediction-driven mixture cure model and its application in credit scoring publication-title: European Journal of Operational Research – volume: 38 start-page: 15392 year: 2011 end-page: 15399 ident: bib0076 article-title: Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection publication-title: Expert Systems with Applications – volume: 25 start-page: 174 year: 2018 end-page: 189 ident: bib0061 article-title: Predicting credit card delinquencies: An application of deep neural networks publication-title: Intelligent Systems in Accounting, Finance and Management – volume: 118 start-page: 33 year: 2019 end-page: 45 ident: bib0054 article-title: Two-stage consumer credit risk modelling using heterogeneous ensemble learning publication-title: Decision Support Systems – reference: . – volume: 247 start-page: 124 year: 2015 end-page: 136 ident: bib0040 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research publication-title: European Journal of Operational Research – volume: 7 start-page: 720 year: 2006 end-page: 747 ident: bib0031 article-title: Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem publication-title: Nonlinear Analysis: Real World Applications – volume: 42 start-page: 1131 year: 2006 end-page: 1151 ident: bib0065 article-title: A process model to develop an internal rating system: Sovereign credit ratings publication-title: Decision Support Systems – volume: 5 start-page: 115 year: 1943 end-page: 133 ident: bib0047 article-title: A logical calculus of the ideas immanent in nervous activity publication-title: The Bulletin of Mathematical Biophysics – volume: 25 start-page: 178 year: 2018 end-page: 206 ident: bib0037 article-title: The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective publication-title: Psychonomic Bulletin & Review – volume: 521 start-page: 436 year: 2015 ident: bib0038 article-title: Deep learning publication-title: Nature – volume: 98 start-page: 105 year: 2018 end-page: 117 ident: bib0024 article-title: A novel ensemble method for credit scoring: Adaption of different imbalance ratios publication-title: Expert Systems with Applications – volume: 78 start-page: 225 year: 2017 end-page: 241 ident: bib0073 article-title: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring publication-title: Expert Systems with Applications – year: 2016 ident: bib0020 article-title: Deep learning – volume: 18 start-page: 2653 year: 2017 end-page: 2688 ident: bib0007 article-title: Time for a change: A tutorial for comparing multiple classifiers through Bayesian analysis publication-title: The Journal of Machine Learning Research – start-page: 328 year: 2018 end-page: 333 ident: bib0071 article-title: Personal credit risk assessment based on stacking ensemble model publication-title: Proceedings of the international conference on intelligent information processing – start-page: 4765 year: 2017 end-page: 4774 ident: bib0042 article-title: A unified approach to interpreting model predictions publication-title: Proceedings of the advances in neural information processing systems – volume: 39 start-page: 43 year: 1997 end-page: 62 ident: bib0062 article-title: Introduction to multi-layer feed-forward neural networks publication-title: Chemometrics and Intelligent Laboratory Systems – volume: 37 start-page: 127 year: 2010 end-page: 133 ident: bib0078 article-title: Least squares support vector machines ensemble models for credit scoring publication-title: Expert Systems with Applications – volume: 7 start-page: 1 year: 2006 end-page: 30 ident: bib0015 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: Journal of Machine Learning Research – year: 2015 ident: bib0041 article-title: Machine learning for adaptive many-core machines – A practical approach – volume: 506 start-page: 150 year: 2014 ident: bib0052 article-title: Scientific method: Statistical errors publication-title: Nature News – year: 1941 ident: 10.1016/j.ejor.2021.03.006_bib0018 – volume: 122 start-page: 100 year: 2013 ident: 10.1016/j.ejor.2021.03.006_bib0056 article-title: A granular computing-based approach to credit scoring modeling publication-title: Neurocomputing doi: 10.1016/j.neucom.2013.05.020 – start-page: 407 year: 2019 ident: 10.1016/j.ejor.2021.03.006_bib0051 article-title: Advanced neural network approach, its explanation with lime for credit scoring application – volume: 25 start-page: 174 issue: 4 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0061 article-title: Predicting credit card delinquencies: An application of deep neural networks publication-title: Intelligent Systems in Accounting, Finance and Management doi: 10.1002/isaf.1437 – volume: 222 start-page: 168 issue: 1 year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0003 article-title: An empirical comparison of conventional techniques, neural networks and the three stage hybrid adaptive neuro fuzzy inference system (ANFIS) model for credit scoring analysis: The case of turkish credit card data publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2012.04.009 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.ejor.2021.03.006_bib0011 article-title: Random forests publication-title: Machine Learning doi: 10.1023/A:1010933404324 – volume: 25 start-page: 178 issue: 1 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0037 article-title: The Bayesian new statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective publication-title: Psychonomic Bulletin & Review doi: 10.3758/s13423-016-1221-4 – volume: 70 start-page: 129 issue: 2 year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0072 article-title: The ASA’s statement on p-values: Context, process, and purpose publication-title: The American Statistician doi: 10.1080/00031305.2016.1154108 – ident: 10.1016/j.ejor.2021.03.006_bib0009 – volume: 237 start-page: 335 issue: 1 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0077 article-title: Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2014.01.044 – volume: 238 start-page: 505 issue: 2 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0068 article-title: Development and application of consumer credit scoring models using profit-based classification measures publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2014.04.001 – volume: 4 issue: 2 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0029 article-title: A comparative study of decision tree ID3 and C4.5 publication-title: International Journal of Advanced Computer Science and Applications doi: 10.14569/SpecialIssue.2014.040203 – start-page: 205 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0079 article-title: A hybrid deep learning model for consumer credit scoring – ident: 10.1016/j.ejor.2021.03.006_bib0045 doi: 10.1016/j.knosys.2020.105758 – volume: 98 start-page: 105 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0024 article-title: A novel ensemble method for credit scoring: Adaption of different imbalance ratios publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2018.01.012 – volume: 7 start-page: 1 issue: Jan year: 2006 ident: 10.1016/j.ejor.2021.03.006_bib0015 article-title: Statistical comparisons of classifiers over multiple data sets publication-title: Journal of Machine Learning Research – volume: 4 start-page: 26 issue: 2 year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0064 article-title: Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude publication-title: COURSERA: Neural Networks for Machine Learning – volume: 3 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0016 article-title: A tutorial survey of architectures, algorithms, and applications for deep learning publication-title: APSIPA Transactions on Signal and Information Processing – year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0039 – start-page: 328 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0071 article-title: Personal credit risk assessment based on stacking ensemble model – volume: 5 start-page: 115 issue: 4 year: 1943 ident: 10.1016/j.ejor.2021.03.006_bib0047 article-title: A logical calculus of the ideas immanent in nervous activity publication-title: The Bulletin of Mathematical Biophysics doi: 10.1007/BF02478259 – volume: 6 start-page: 52138 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0001 article-title: Peeking inside the black-box: A survey on explainable artificial intelligence (XAI) publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2870052 – year: 2002 ident: 10.1016/j.ejor.2021.03.006_bib0063 – start-page: 4596 year: 2011 ident: 10.1016/j.ejor.2021.03.006_bib0069 article-title: Comparing multilayer perceptron to deep belief network tandem features for robust ASR – volume: 6 start-page: 38 issue: 2 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0002 article-title: Credit risk analysis using machine and deep learning models publication-title: Risks doi: 10.3390/risks6020038 – year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0004 – start-page: 599 year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0025 article-title: A practical guide to training restricted Boltzmann machines – year: 2015 ident: 10.1016/j.ejor.2021.03.006_bib0041 – volume: 104 start-page: 113 year: 2017 ident: 10.1016/j.ejor.2021.03.006_bib0044 article-title: Integrated framework for profit-based feature selection and SVM classification in credit scoring publication-title: Decision Support Systems doi: 10.1016/j.dss.2017.10.007 – volume: 7 start-page: 2161 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0070 article-title: A deep learning approach for credit scoring of peer-to-peer lending using attention mechanism LSTM publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2887138 – volume: 65 start-page: 465 year: 2017 ident: 10.1016/j.ejor.2021.03.006_bib0043 article-title: A deep learning approach for credit scoring using credit default swaps publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2016.12.002 – volume: 180 start-page: 2044 issue: 10 year: 2010 ident: 10.1016/j.ejor.2021.03.006_bib0019 article-title: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power publication-title: Information Sciences doi: 10.1016/j.ins.2009.12.010 – volume: 36 start-page: 2473 issue: 2 year: 2009 ident: 10.1016/j.ejor.2021.03.006_bib0075 article-title: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2007.12.020 – year: 2020 ident: 10.1016/j.ejor.2021.03.006_bib0012 article-title: Predicting mortgage early delinquency with machine learning methods publication-title: European Journal of Operational Research – volume: 37 start-page: 127 issue: 1 year: 2010 ident: 10.1016/j.ejor.2021.03.006_bib0078 article-title: Least squares support vector machines ensemble models for credit scoring publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2009.05.024 – volume: 521 start-page: 436 issue: 7553 year: 2015 ident: 10.1016/j.ejor.2021.03.006_bib0038 article-title: Deep learning publication-title: Nature doi: 10.1038/nature14539 – ident: 10.1016/j.ejor.2021.03.006_bib0017 – start-page: 4765 year: 2017 ident: 10.1016/j.ejor.2021.03.006_bib0042 article-title: A unified approach to interpreting model predictions – year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0020 – volume: 11 start-page: 12 issue: 1 year: 2018 ident: 10.1016/j.ejor.2021.03.006_bib0022 article-title: Ensemble learning or deep learning? Application to default risk analysis publication-title: Journal of Risk and Financial Management doi: 10.3390/jrfm11010012 – start-page: 1026 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0008 article-title: A Bayesian Wilcoxon signed-rank test based on the Dirichlet process – volume: 31 start-page: 337 issue: 4 year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0021 article-title: Statistical tests, p values, confidence intervals, and power: A guide to misinterpretations publication-title: European Journal of Epidemiology doi: 10.1007/s10654-016-0149-3 – year: 2011 ident: 10.1016/j.ejor.2021.03.006_bib0035 – volume: 54 year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0067 article-title: Credit scoring with a feature selection approach based deep learning – volume: 54 start-page: 627 issue: 6 year: 2003 ident: 10.1016/j.ejor.2021.03.006_bib0006 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring publication-title: Journal of the Operational Research Society doi: 10.1057/palgrave.jors.2601545 – volume: 118 start-page: 33 year: 2019 ident: 10.1016/j.ejor.2021.03.006_bib0054 article-title: Two-stage consumer credit risk modelling using heterogeneous ensemble learning publication-title: Decision Support Systems doi: 10.1016/j.dss.2019.01.002 – volume: 1 issue: 4 year: 2005 ident: 10.1016/j.ejor.2021.03.006_bib0066 article-title: Linear and nonlinear credit scoring by combining logistic regression and support vector machines publication-title: Journal of Credit Risk doi: 10.21314/JCR.2005.025 – volume: 18 start-page: 2653 issue: 1 year: 2017 ident: 10.1016/j.ejor.2021.03.006_bib0007 article-title: Time for a change: A tutorial for comparing multiple classifiers through Bayesian analysis publication-title: The Journal of Machine Learning Research – start-page: 785 year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0013 article-title: XGBoost: A scalable tree boosting system – ident: 10.1016/j.ejor.2021.03.006_bib0059 – volume: 61 start-page: 85 year: 2015 ident: 10.1016/j.ejor.2021.03.006_bib0057 article-title: Deep learning in neural networks: An overview publication-title: Neural Networks doi: 10.1016/j.neunet.2014.09.003 – volume: 15 start-page: 722 issue: 4 year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0036 article-title: The time has come: Bayesian methods for data analysis in the organizational sciences publication-title: Organizational Research Methods doi: 10.1177/1094428112457829 – volume: 15 start-page: 419 issue: 4 year: 2006 ident: 10.1016/j.ejor.2021.03.006_bib0074 article-title: A comparative study of data mining methods in consumer loans credit scoring management publication-title: Journal of Systems Science and Systems Engineering doi: 10.1007/s11518-006-5023-5 – volume: 2 year: 1994 ident: 10.1016/j.ejor.2021.03.006_bib0023 – start-page: 5060 year: 2011 ident: 10.1016/j.ejor.2021.03.006_bib0050 article-title: Deep belief networks using discriminative features for phone recognition. – volume: 247 start-page: 124 issue: 1 year: 2015 ident: 10.1016/j.ejor.2021.03.006_bib0040 article-title: Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2015.05.030 – volume: 277 start-page: 20 issue: 1 year: 2019 ident: 10.1016/j.ejor.2021.03.006_bib0033 article-title: A prediction-driven mixture cure model and its application in credit scoring publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2019.01.072 – volume: 281 start-page: 628 issue: 3 year: 2020 ident: 10.1016/j.ejor.2021.03.006_bib0034 article-title: Deep learning in business analytics and operations research: Models, applications and managerial implications publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2019.09.018 – start-page: 1 year: 2015 ident: 10.1016/j.ejor.2021.03.006_bib0030 article-title: Deep belief networks and deep learning – volume: 74 start-page: 26 year: 2019 ident: 10.1016/j.ejor.2021.03.006_bib0053 article-title: The value of big data for credit scoring: Enhancing financial inclusion using mobile phone data and social network analytics publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2018.10.004 – ident: 10.1016/j.ejor.2021.03.006_bib0060 doi: 10.1016/j.ejor.2021.03.008 – start-page: 39 year: 2009 ident: 10.1016/j.ejor.2021.03.006_sbref0048 article-title: Deep belief networks for phone recognition – volume: 39 start-page: 43 issue: 1 year: 1997 ident: 10.1016/j.ejor.2021.03.006_bib0062 article-title: Introduction to multi-layer feed-forward neural networks publication-title: Chemometrics and Intelligent Laboratory Systems doi: 10.1016/S0169-7439(97)00061-0 – volume: 313 start-page: 504 issue: 5786 year: 2006 ident: 10.1016/j.ejor.2021.03.006_bib0026 article-title: Reducing the dimensionality of data with neural networks publication-title: Science doi: 10.1126/science.1127647 – year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0005 – volume: 20 start-page: 14 issue: 1 year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0049 article-title: Acoustic modeling using deep belief networks publication-title: IEEE Transactions on Audio, Speech, and Language Processing doi: 10.1109/TASL.2011.2109382 – start-page: 1135 year: 2016 ident: 10.1016/j.ejor.2021.03.006_bib0055 article-title: “Why should I trust you?” Explaining the predictions of any classifier – volume: 506 start-page: 150 issue: 7487 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0052 article-title: Scientific method: Statistical errors publication-title: Nature News doi: 10.1038/506150a – volume: 398 year: 2013 ident: 10.1016/j.ejor.2021.03.006_bib0028 – volume: 106 start-page: 1817 issue: 11 year: 2017 ident: 10.1016/j.ejor.2021.03.006_bib0014 article-title: Statistical comparison of classifiers through Bayesian hierarchical modelling publication-title: Machine Learning doi: 10.1007/s10994-017-5641-9 – volume: 39 start-page: 10916 issue: 12 year: 2012 ident: 10.1016/j.ejor.2021.03.006_bib0046 article-title: Two-level classifier ensembles for credit risk assessment publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2012.03.033 – volume: 30 start-page: 1145 issue: 7 year: 1997 ident: 10.1016/j.ejor.2021.03.006_bib0010 article-title: The use of the area under the ROC curve in the evaluation of machine learning algorithms publication-title: Pattern Recognition doi: 10.1016/S0031-3203(96)00142-2 – volume: 82 issue: 16 year: 2013 ident: 10.1016/j.ejor.2021.03.006_bib0058 article-title: Classification through machine learning technique: C4.5 algorithm based on various entropies publication-title: International Journal of Computer Applications doi: 10.5120/14249-2444 – volume: 38 start-page: 15392 issue: 12 year: 2011 ident: 10.1016/j.ejor.2021.03.006_bib0076 article-title: Credit risk evaluation using a weighted least squares SVM classifier with design of experiment for parameter selection publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.06.023 – volume: 751 year: 2014 ident: 10.1016/j.ejor.2021.03.006_bib0027 – volume: 42 start-page: 1131 issue: 2 year: 2006 ident: 10.1016/j.ejor.2021.03.006_bib0065 article-title: A process model to develop an internal rating system: Sovereign credit ratings publication-title: Decision Support Systems doi: 10.1016/j.dss.2005.10.001 – volume: 78 start-page: 225 year: 2017 ident: 10.1016/j.ejor.2021.03.006_bib0073 article-title: A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.02.017 – volume: 7 start-page: 720 issue: 4 year: 2006 ident: 10.1016/j.ejor.2021.03.006_bib0031 article-title: Evaluation of neural networks and data mining methods on a credit assessment task for class imbalance problem publication-title: Nonlinear Analysis: Real World Applications – ident: 10.1016/j.ejor.2021.03.006_bib0032 |
| SSID | ssj0001515 |
| Score | 2.6843553 |
| Snippet | •Deep learning techniques are compared to both conventional methods and ensemble methods for credit scoring.•This comparison is executed over a significant... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 292 |
| SubjectTerms | Bayesian statistical testing Credit scoring Decision support systems Deep learning Risk analysis |
| Title | Deep learning for credit scoring: Do or don’t? |
| URI | https://dx.doi.org/10.1016/j.ejor.2021.03.006 |
| Volume | 295 |
| WOSCitedRecordID | wos000661773000019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1872-6860 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001515 issn: 0377-2217 databaseCode: AIEXJ dateStart: 19950105 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NjtMwELZKFyE48FMWsfwpB25VVokdxzYXtEuXP6EVgkXqLUocW2pZJVEaqj3yGjwGr8STMI7tbOmiFSBxiSqrdhLPF894PN8MQk9FoVIGwg5pXCYhaAgSFoTIkJdMQ4dEK9WnzH_Hjo_5fC7ej0bfPRdmfcqqip-diea_ihraQNiGOvsX4h4GhQb4DUKHK4gdrn8k-JlSjS8GYYMkTVLQRTddyT7YzvgAZrWJQzd1PFysg-i2I_w2vfTOYoWG1vsOXZKgwZn8Cgxh2CM7_tbh0hzAH6ZtNf2Q6wF_a-OxrgBPtYnm6B2vqmnOz_VzQ4VauWOQIR7HDCXI6jMMX_bDkq5btI5o1B_0z_JN5wWODYvPciutR-0Cq8YyuRgLMbaczn1lF2bOcJhyW3vAr9zY1uf8BaJuHbYF9pxKJz2z-6K2sI6L5b5a1iY1LI5tvtut1Ny9sv9oHso8E477lELzK2gHMyr4GO0cvDmavx3Uv7EQ-6Mr9xKOqWWDCrfv9HtraMPCObmNbrqtSXBgIXUHjVQ1Qdc8M2KCbvkKIIFTCBN0YyOd5V0UGegFHnoBQC-w0Asc9J4FszqAVgDej6_fuue76NPLo5MXr0NXkSOUJKVdmEZaR6ksY5WnpUyo1pKqUnChMZWE6bSguU7zSEpc5EJysAUVNBAJ-4ycFZTcQ-OqrtR9FBTK2LKSRnFBwUBK8pzzIokV2A5cCUH3UOxnJpMuXb2pmnKa-bjEZWZmMzOzmUUkg9ncQ9OhT2OTtVz6b-onPHPmpjUjM8DHJf0e_GO_h-j6-SfwCI279ot6jK7KdbdYtU8cjH4C5Ayf-A |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+learning+for+credit+scoring%3A+Do+or+don%E2%80%99t%3F&rft.jtitle=European+journal+of+operational+research&rft.au=Gunnarsson%2C+Bj%C3%B6rn+Rafn&rft.au=vanden+Broucke%2C+Seppe&rft.au=Baesens%2C+Bart&rft.au=%C3%93skarsd%C3%B3ttir%2C+Mar%C3%ADa&rft.date=2021-11-16&rft.pub=Elsevier+B.V&rft.issn=0377-2217&rft.eissn=1872-6860&rft.volume=295&rft.issue=1&rft.spage=292&rft.epage=305&rft_id=info:doi/10.1016%2Fj.ejor.2021.03.006&rft.externalDocID=S037722172100196X |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0377-2217&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0377-2217&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0377-2217&client=summon |