Predicting Car Residual Value

Success story

Our team supported the development of a residual value prediction system to enhance forecasting accuracy and contract portfolio management. By integrating data from multiple sources, including legacy systems, and automating key processes, we helped streamline model training and residual value estimation.

This solution enabled data-driven decision-making, reducing financial risk and improving operational efficiency.

Challenges

The project required integrating data from multiple systems, including legacy infrastructures, to ensure accurate forecasting. Developing precise predictive models was crucial for estimating residual values, enabling data-driven decision-making in contract portfolio management.

Solutions

With the use of Java 21, Spring Boot, PostgreSQL, Kubernetes, Jenkins, and Angular, we streamlined the entire forecasting process. By automating key tasks such as data collection, model training, and residual value prediction, we enhanced the efficiency of leasing contract management across multiple business areas.

Results

The implementation of advanced technologies and automation improved the accuracy and efficiency of residual value forecasting. Automating repetitive processes saved time and resources, boosting overall operational performance.

Continuous monitoring and model updates ensure forecasts remain reliable and high-quality, supporting long-term business success.

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