Trustworthy Residual Vehicle Value Prediction for Auto Finance

Mi-hyung Kim, Jimyung Choi, Jaehyun Kim, Wooyoung Kim, Yeonung Baek, Gisuk Bang, Kwangwoon Son, Yeonman Ryou, Kee-Eung Kim
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引用次数: 1

Abstract

The 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 financial 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 by 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 coped with these practical challenges and created value for our business at Hyundai Capital Services, the top auto financial service provider in Korea.
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汽车金融的可信赖剩余价值预测
车辆的残值(RV)是指其在未来某一时刻的估计价值。它是每个汽车金融产品的核心组成部分,用于确定信贷额度和租赁利率。因此,准确的RV预测对于汽车金融行业至关重要,因为它可能因过度预测而造成收入损失或因预测不足而使金融产品无法胜任。尽管之前有很多关于在大量二手车销售数据上训练机器学习模型的研究,但我们必须应对现实世界的操作需求,例如遵守规则(即相对于特征子集的输出单调性)和对未见输入(即新车和稀有车型)的泛化。在本文中,我们描述了我们如何应对这些实际挑战,并为韩国顶级汽车金融服务提供商现代资本服务公司的业务创造价值。
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