Please use this identifier to cite or link to this item:
https://doi.org/10.21256/zhaw-22801Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Gramespacher, Thomas | - |
| dc.contributor.author | Posth, Jan-Alexander | - |
| dc.date.accessioned | 2021-07-09T08:16:46Z | - |
| dc.date.available | 2021-07-09T08:16:46Z | - |
| dc.date.issued | 2021-06-15 | - |
| dc.identifier.issn | 2624-8212 | de_CH |
| dc.identifier.uri | https://digitalcollection.zhaw.ch/handle/11475/22801 | - |
| dc.description.abstract | In the recent years, data science methods have been developed considerably and have consequently found their way into many business processes in banking and finance. One example is the review and approval process of credit applications where they are employed with the aim to reduce rare but costly credit defaults in portfolios of loans. But there are challenges. Since defaults are rare events, it is-even with machine learning (ML) techniques-difficult to improve prediction accuracy and improvements are often marginal. Furthermore, while from an event prediction point of view, a non-default is the same as a default, from an economic point of view much more relevant to the end user it is not due to the high asymmetry in cost. Last, there are regulatory constraints when it comes to the adoption of advanced ML, hence the call for explainable artificial intelligence (XAI) issued by regulatory bodies like FINMA and BaFin. In our study, we will address these challenges. In particular, based on an exemplary use case, we show how ML methods can be adapted to the specific needs of credit assessment and how, in the case of strongly asymmetric costs of wrong forecasts, it makes sense to optimize not for accuracy but for an economic target function. We showcase this for two simple and ad hoc explainable ML algorithms, finding that in the case of credit approval, surprisingly high rejection rates contribute to maximizing profit. | de_CH |
| dc.language.iso | en | de_CH |
| dc.publisher | Frontiers Research Foundation | de_CH |
| dc.relation.ispartof | Frontiers in Artificial Intelligence | de_CH |
| dc.rights | http://creativecommons.org/licenses/by/4.0/ | de_CH |
| dc.subject | FinTech | de_CH |
| dc.subject | XAI | de_CH |
| dc.subject | Credit default | de_CH |
| dc.subject | Machine learning | de_CH |
| dc.subject.ddc | 006: Spezielle Computerverfahren | de_CH |
| dc.subject.ddc | 332.6: Investition | de_CH |
| dc.title | Employing explainable AI to optimize the return target function of a loan portfolio | de_CH |
| dc.type | Beitrag in wissenschaftlicher Zeitschrift | de_CH |
| dcterms.type | Text | de_CH |
| zhaw.departement | School of Management and Law | de_CH |
| zhaw.organisationalunit | Institut für Wealth & Asset Management (IWA) | de_CH |
| dc.identifier.doi | 10.3389/frai.2021.693022 | de_CH |
| dc.identifier.doi | 10.21256/zhaw-22801 | - |
| dc.identifier.pmid | 34212133 | de_CH |
| zhaw.funding.eu | info:eu-repo/grantAgreement/EC/H2020/825215//A FINancial supervision and TECHnology compliance training programme/FIN-TECH | de_CH |
| zhaw.issue | 693022 | de_CH |
| zhaw.originated.zhaw | Yes | de_CH |
| zhaw.publication.status | publishedVersion | de_CH |
| zhaw.volume | 4 | de_CH |
| zhaw.publication.review | Peer review (Publikation) | de_CH |
| zhaw.author.additional | No | de_CH |
| zhaw.display.portrait | Yes | de_CH |
| zhaw.monitoring.costperiod | 2021 | de_CH |
| Appears in collections: | Publikationen School of Management and Law | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2021_Gramespacher-Posth_AI-optimization-return-taget-function-loan-portfolio.pdf | 1.48 MB | Adobe PDF |  View/Open |
Show simple item record
Gramespacher, T., & Posth, J.-A. (2021). Employing explainable AI to optimize the return target function of a loan portfolio. Frontiers in Artificial Intelligence, 4(693022). https://doi.org/10.3389/frai.2021.693022
Gramespacher, T. and Posth, J.-A. (2021) ‘Employing explainable AI to optimize the return target function of a loan portfolio’, Frontiers in Artificial Intelligence, 4(693022). Available at: https://doi.org/10.3389/frai.2021.693022.
T. Gramespacher and J.-A. Posth, “Employing explainable AI to optimize the return target function of a loan portfolio,” Frontiers in Artificial Intelligence, vol. 4, no. 693022, Jun. 2021, doi: 10.3389/frai.2021.693022.
GRAMESPACHER, Thomas und Jan-Alexander POSTH, 2021. Employing explainable AI to optimize the return target function of a loan portfolio. Frontiers in Artificial Intelligence. 15 Juni 2021. Bd. 4, Nr. 693022. DOI 10.3389/frai.2021.693022
Gramespacher, Thomas, and Jan-Alexander Posth. 2021. “Employing Explainable AI to Optimize the Return Target Function of a Loan Portfolio.” Frontiers in Artificial Intelligence 4 (693022). https://doi.org/10.3389/frai.2021.693022.
Gramespacher, Thomas, and Jan-Alexander Posth. “Employing Explainable AI to Optimize the Return Target Function of a Loan Portfolio.” Frontiers in Artificial Intelligence, vol. 4, no. 693022, June 2021, https://doi.org/10.3389/frai.2021.693022.
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