Trustworthy Residual Vehicle Value Prediction for Auto Finance


  • Mihye Kim Hyundai Capital Services
  • Jimyung Choi Hyundai Capital Services
  • Jaehyun Kim Hyundai Capital Services
  • Wooyoung Kim Hyundai Capital Services
  • Yeonung Baek Hyundai Capital Services
  • Gisuk Bang Hyundai Capital Services
  • Kwangwoon Son Hyundai Capital Services
  • Yeonman Ryou Hyundai Capital Services
  • Kee-Eung Kim Kim Jaechul Graduate School of AI, KAIST



Auto Finance, Vehicle Residual Value, Used Car Market, Predictive Modeling, Anomaly Detection, DNN, XGBoost, CatBoost


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.




How to Cite

Kim, M., Choi, J., Kim, J., Kim, W., Baek, Y., Bang, G., Son, K., Ryou, Y., & Kim, K.-E. (2023). Trustworthy Residual Vehicle Value Prediction for Auto Finance. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15537-15544.



IAAI Technical Track on deployed Highly Innovative Applications of AI