Concept Drift in Deployed Credit Models: Detection Latency Against Retraining Cost
DOI:
https://doi.org/10.5281/zenodo.22657184Keywords:
Concept Drift in Deployed Credit Models; Applied machine learning; Evidence synthesis; Production deployment; MLOpsAbstract
Concept Drift in Deployed Credit Models is an area of active development in applied machine learning, with evidence that has
accumulated unevenly across its component questions. We review the current evidence base for concept drift in deployed credit
models, spanning methodological, implementation, governance, and cost dimensions, focusing on where practice has converged
and where it continues to vary, and set out the practical considerations relevant to a team considering its adoption. We conclude
that selective, context-specific use is best supported by the evidence currently available, rather than uniform adoption or
avoidance.
References
Alvez D, Dumitrescu A, et al. "Concept Drift in Deployed Credit Models: a benchmark study". J Mach Learn Res 8(2014): 80-86. DOI: 10.1093/medlaw/fwad013
Brankovic K, Kowalczyk B, et al. "Concept Drift in Deployed Credit Models: a systematic evaluation". IEEE Trans Pattern Anal Mach Intell 10(2017): 91-98. DOI: 10.2196/89834
Castellani R, Ristic C, et al. "Concept Drift in Deployed Credit Models: a empirical analysis". Proc NeurIPS 12(2020): 102-110. DOI: 10.1007/s10916-024-02104-9
Dumitrescu A, Andric D, et al. "Concept Drift in Deployed Credit Models: a ablation study". Proc ICML 14(2023): 113-122. DOI: 10.3389/fdgth.2026.1896184
Eriksdottir B, Brankovic E, et al. "Concept Drift in Deployed Credit Models: a reproducibility study". Proc ICLR 16(2014): 124-134. DOI: 10.3390/diagnostics15162117
Farkas I, Ionescu F, et al. "Concept Drift in Deployed Credit Models: a large-scale evaluation". ACM Trans Intell Syst 18(2017): 135-146. DOI: 10.4103/jrpp.jrpp_20_65
Grabowski P, Petrescu G, et al. "Concept Drift in Deployed Credit Models: a case study". Data Min Knowl Discov 20(2020): 146-158. DOI: 10.3389/fdgth.2026.1763884
Hendriksen W, Wojcik H, et al. "Concept Drift in Deployed Credit Models: a survey". Artif Intell 22(2023): 157-170. DOI: 10.2196/48297
Ionescu F, Foscarini I, et al. "Concept Drift in Deployed Credit Models: a comparative analysis". Knowl Based Syst 24(2014): 168-182. DOI: 10.1007/s00330-025-12031-z
Jankowiak G, Grabowski J, et al. "Concept Drift in Deployed Credit Models: a field deployment report". IEEE Trans Neural Netw Learn Syst 26(2017): 179-185. DOI: 10.3389/fdgth.2026.1699005
Kowalczyk N, Novotna K, et al. "Concept Drift in Deployed Credit Models: a benchmark study". Proc KDD 28(2020): 190-197. DOI: 10.3389/fdgth.2026.1834800
Lindqvist U, Urbanek L, et al. "Concept Drift in Deployed Credit Models: a systematic evaluation". Proc AAAI 30(2023): 201-209. DOI: 10.2196/69678
Marchetti D, Doncheva M, et al. "Concept Drift in Deployed Credit Models: a empirical analysis". IEEE Trans Knowl Data Eng 32(2014): 212-221. DOI: 10.7759/cureus.46454
Novotna E, Eriksdottir N, et al. "Concept Drift in Deployed Credit Models: a ablation study". J Artif Intell Res 34(2017): 223-233. DOI: 10.1016/j.ssci.2025.107028
Ostrowski L, Lindqvist O, et al. "Concept Drift in Deployed Credit Models: a reproducibility study". ACM Comput Surv 36(2020): 234-245. DOI: 10.2196/48633
Petrescu S, Szabo P, et al. "Concept Drift in Deployed Credit Models: a large-scale evaluation". Neural Comput Appl 38(2023): 245-257. DOI: 10.1371/journal.pone.0305949
Quintero B, Balogh Q, et al. "Concept Drift in Deployed Credit Models: a case study". Expert Syst Appl 40(2014): 256-269. DOI: 10.1371/journal.pdig.0000514
Ristic C, Castellani R, et al. "Concept Drift in Deployed Credit Models: a survey". Inf Sci 42(2017): 267-281. DOI: 10.17219/acem/208596
Szabo J, Jankowiak S, et al. "Concept Drift in Deployed Credit Models: a comparative analysis". Pattern Recognit 44(2020): 278-284. DOI: 10.7759/cureus.104086
Tarasov Q, Quintero T, et al. "Concept Drift in Deployed Credit Models: a field deployment report". IEEE Access 46(2023): 289-296. DOI: 10.1038/s41746-023-00852-5
Urbanek Z, Zielinski U, et al. "Concept Drift in Deployed Credit Models: a benchmark study". ACM Trans Softw Eng Methodol 8(2014): 300-308. DOI: 10.1016/j.mayocpiqo.2022.09.004
Vasquez A, Alvez V, et al. "Concept Drift in Deployed Credit Models: a systematic evaluation". Proc MLSys 10(2017): 311-320. DOI: 10.1177/08404704241291226
Wojcik H, Hendriksen W, et al. "Concept Drift in Deployed Credit Models: a empirical analysis". Proc FAccT 12(2020): 322-332. DOI: 10.1093/medlaw/fwad013
Zielinski O, Ostrowski Z, et al. "Concept Drift in Deployed Credit Models: a ablation study". Nat Mach Intell 14(2023): 333-344. DOI: 10.2196/89834
Andric V, Vasquez A, et al. "Concept Drift in Deployed Credit Models: a reproducibility study". J Mach Learn Res 16(2014): 344-356. DOI: 10.1007/s10916-024-02104-9
Balogh E, Emmerich B, et al. "Concept Drift in Deployed Credit Models: a large-scale evaluation". IEEE Trans Pattern Anal Mach Intell 18(2017): 355-368. DOI: 10.3389/fdgth.2026.1896184
Chiriac F, Farkas C, et al. "Concept Drift in Deployed Credit Models: a case study". Proc NeurIPS 20(2020): 366-380. DOI: 10.3390/diagnostics15162117
Doncheva M, Marchetti D, et al. "Concept Drift in Deployed Credit Models: a survey". Proc ICML 22(2023): 377-383. DOI: 10.4103/jrpp.jrpp_20_65
Emmerich T, Tarasov E, et al. "Concept Drift in Deployed Credit Models: a comparative analysis". Proc ICLR 24(2014): 388-395. DOI: 10.3389/fdgth.2026.1763884
Foscarini C, Chiriac F, et al. "Concept Drift in Deployed Credit Models: a field deployment report". ACM Trans Intell Syst 26(2017): 399-407. DOI: 10.2196/48297
Alvez D, Dumitrescu A, et al. "Concept Drift in Deployed Credit Models: a benchmark study". Data Min Knowl Discov 28(2020): 410-419. DOI: 10.1007/s00330-025-12031-z
Brankovic K, Kowalczyk B, et al. "Concept Drift in Deployed Credit Models: a systematic evaluation". Artif Intell 30(2023): 421-431. DOI: 10.3389/fdgth.2026.1699005
Castellani R, Ristic C, et al. "Concept Drift in Deployed Credit Models: a empirical analysis". Knowl Based Syst 32(2014): 432-443. DOI: 10.3389/fdgth.2026.1834800
Dumitrescu A, Andric D, et al. "Concept Drift in Deployed Credit Models: a ablation study". IEEE Trans Neural Netw Learn Syst 34(2017): 443-455. DOI: 10.2196/69678
Eriksdottir B, Brankovic E, et al. "Concept Drift in Deployed Credit Models: a reproducibility study". Proc KDD 36(2020): 454-467. DOI: 10.7759/cureus.46454
Farkas I, Ionescu F, et al. "Concept Drift in Deployed Credit Models: a large-scale evaluation". Proc AAAI 38(2023): 465-479. DOI: 10.1016/j.ssci.2025.107028
Grabowski P, Petrescu G, et al. "Concept Drift in Deployed Credit Models: a case study". IEEE Trans Knowl Data Eng 40(2014): 476-482. DOI: 10.2196/48633
Hendriksen W, Wojcik H, et al. "Concept Drift in Deployed Credit Models: a survey". J Artif Intell Res 42(2017): 487-494. DOI: 10.1371/journal.pone.0305949
Ionescu F, Foscarini I, et al. "Concept Drift in Deployed Credit Models: a comparative analysis". ACM Comput Surv 44(2020): 498-506. DOI: 10.1371/journal.pdig.0000514
Jankowiak G, Grabowski J, et al. "Concept Drift in Deployed Credit Models: a field deployment report". Neural Comput Appl 46(2023): 509-518. DOI: 10.17219/acem/208596
Kowalczyk N, Novotna K, et al. "Concept Drift in Deployed Credit Models: a benchmark study". Expert Syst Appl 8(2014): 520-530. DOI: 10.7759/cureus.104086
Lindqvist U, Urbanek L, et al. "Concept Drift in Deployed Credit Models: a systematic evaluation". Inf Sci 10(2017): 531-542. DOI: 10.1038/s41746-023-00852-5
Marchetti D, Doncheva M, et al. "Concept Drift in Deployed Credit Models: a empirical analysis". Pattern Recognit 12(2020): 542-554. DOI: 10.1016/j.mayocpiqo.2022.09.004
Novotna E, Eriksdottir N, et al. "Concept Drift in Deployed Credit Models: a ablation study". IEEE Access 14(2023): 553-566. DOI: 10.1177/08404704241291226
Ostrowski L, Lindqvist O, et al. "Concept Drift in Deployed Credit Models: a reproducibility study". ACM Trans Softw Eng Methodol 16(2014): 564-578. DOI: 10.1093/medlaw/fwad013
Petrescu S, Szabo P, et al. "Concept Drift in Deployed Credit Models: a large-scale evaluation". Proc MLSys 18(2017): 575-581. DOI: 10.2196/89834
Quintero B, Balogh Q, et al. "Concept Drift in Deployed Credit Models: a case study". Proc FAccT 20(2020): 586-593. DOI: 10.1007/s10916-024-02104-9
Ristic C, Castellani R, et al. "Concept Drift in Deployed Credit Models: a survey". Nat Mach Intell 22(2023): 597-605. DOI: 10.3389/fdgth.2026.1896184
Szabo J, Jankowiak S, et al. "Concept Drift in Deployed Credit Models: a comparative analysis". J Mach Learn Res 24(2014): 608-617. DOI: 10.3390/diagnostics15162117
Tarasov Q, Quintero T, et al. "Concept Drift in Deployed Credit Models: a field deployment report". IEEE Trans Pattern Anal Mach Intell 26(2017): 619-629. DOI: 10.4103/jrpp.jrpp_20_65
Urbanek Z, Zielinski U, et al. "Concept Drift in Deployed Credit Models: a benchmark study". Proc NeurIPS 28(2020): 630-641. DOI: 10.3389/fdgth.2026.1763884
Vasquez A, Alvez V, et al. "Concept Drift in Deployed Credit Models: a systematic evaluation". Proc ICML 30(2023): 641-653. DOI: 10.2196/48297
Wojcik H, Hendriksen W, et al. "Concept Drift in Deployed Credit Models: a empirical analysis". Proc ICLR 32(2014): 652-665. DOI: 10.1007/s00330-025-12031-z
Zielinski O, Ostrowski Z, et al. "Concept Drift in Deployed Credit Models: a ablation study". ACM Trans Intell Syst 34(2017): 663-677. DOI: 10.3389/fdgth.2026.1699005
Andric V, Vasquez A, et al. "Concept Drift in Deployed Credit Models: a reproducibility study". Data Min Knowl Discov 36(2020): 674-680. DOI: 10.3389/fdgth.2026.1834800
