Trustworthy Residual Vehicle Value Prediction for Auto Finance

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dc.contributor.authorKim, Mihyeko
dc.contributor.authorChoi, Jimyungko
dc.contributor.authorKim, Jaehyunko
dc.contributor.authorKim, Wooyoungko
dc.contributor.authorBaek, Yeonungko
dc.contributor.authorBang, Gisukko
dc.contributor.authorSon, Kwangwoonko
dc.contributor.authorRyou, Yeonmanko
dc.contributor.authorKim, Kee-Eungko
dc.date.accessioned2023-12-08T02:03:35Z-
dc.date.available2023-12-08T02:03:35Z-
dc.date.created2023-12-07-
dc.date.created2023-12-07-
dc.date.issued2022-10-07-
dc.identifier.citation37th AAAI Conference on Artificial Intelligence, AAAI 2023, pp.15537 - 15544-
dc.identifier.issn2374-3468-
dc.identifier.urihttp://hdl.handle.net/10203/316056-
dc.description.abstractThe residual value (RV) of a vehicle refers to its estimated worth at some point in the future. It is a core component in every auto finance product, used to determine the credit lines and the leasing rates. As such, an accurate prediction of RV is critical for the auto finance industry, since it can pose a risk of revenue loss by over-prediction or make the financial product incompetent through under-prediction. Although there are a number of prior studies on training machine learning models on a large amount of used car sales data, we had to cope with real-world operational requirements such as compliance with regulations (i.e. monotonicity of output with respect to a subset of features) and generalization to unseen input (i.e. new and rare car models). In this paper, we describe how we addressed these practical challenges and created value for our business at Hyundai Capital Services, the top auto financial service provider in Korea.-
dc.languageEnglish-
dc.publisherAssociation for the Advancement of Artificial Intelligence-
dc.titleTrustworthy Residual Vehicle Value Prediction for Auto Finance-
dc.typeConference-
dc.identifier.scopusid2-s2.0-85168236000-
dc.type.rimsCONF-
dc.citation.beginningpage15537-
dc.citation.endingpage15544-
dc.citation.publicationname37th AAAI Conference on Artificial Intelligence, AAAI 2023-
dc.identifier.conferencecountryUS-
dc.identifier.conferencelocationWashington-
dc.contributor.localauthorKim, Kee-Eung-
dc.contributor.nonIdAuthorKim, Mihye-
dc.contributor.nonIdAuthorChoi, Jimyung-
dc.contributor.nonIdAuthorKim, Jaehyun-
dc.contributor.nonIdAuthorKim, Wooyoung-
dc.contributor.nonIdAuthorBaek, Yeonung-
dc.contributor.nonIdAuthorBang, Gisuk-
dc.contributor.nonIdAuthorSon, Kwangwoon-
dc.contributor.nonIdAuthorRyou, Yeonman-
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