A.I. and Data-Driven Mobility at Volkswagen Financial Services AG

TL;DR

This paper discusses how Volkswagen Financial Services leverages machine learning and proprietary data to improve vehicle leasing processes through recommender systems, object detection, and forecasting methods.

Contribution

It introduces new machine learning methods tailored for vehicle lifecycle management in the financial services industry.

Findings

Enhanced decision-making in vehicle leasing processes

Improved accuracy in vehicle lifecycle forecasting

Integration of recommender systems and object detection in finance

Abstract

Machine learning is being widely adapted in industrial applications owing to the capabilities of commercially available hardware and rapidly advancing research. Volkswagen Financial Services (VWFS), as a market leader in vehicle leasing services, aims to leverage existing proprietary data and the latest research to enhance existing and derive new business processes. The collaboration between Information Systems and Machine Learning Lab (ISMLL) and VWFS serves to realize this goal. In this paper, we propose methods in the fields of recommender systems, object detection, and forecasting that enable data-driven decisions for the vehicle life-cycle at VWFS.