Predictive Risk Analysis for Yelo Car Rental
Using advanced analytics to identify customer segments with a high probability of accidents, enabling proactive risk management.
The Challenge
Yelo Car Rental in KSA wanted to reduce financial losses associated with vehicle accidents. To do this, they needed to move beyond reactive measures and proactively identify which customer segments were most likely to be involved in accidents. The challenge was to analyze historical rental and accident data to find meaningful patterns and build a predictive model for risk.
Our Solution
Our data science team applied advanced analytical techniques to Yelo's historical data to uncover key risk factors.
Data Exploration & Feature Engineering:
We cleaned and enriched historical data, creating new features from customer demographics, rental behavior, and vehicle information to prepare it for analysis.
Statistical Modeling:
Using statistical analysis and machine learning classification models, we identified the key variables that had the strongest correlation with accident probability.
Customer Segmentation:
We grouped customers into distinct risk segments based on the model's output, providing Yelo with a clear profile of their highest-risk renters.
The Impact
The analysis provided Yelo with actionable, data-driven insights into their customer risk profiles. Armed with this information, they could implement targeted strategies—such as adjusted insurance deposits or specialized awareness campaigns—for high-risk segments. This proactive approach to risk management empowered them to reduce accident-related costs and improve overall profitability.
Project Overview
Key details about the engagement.
Client
Yelo Car Rental (KSA)
Services
Advanced Analytics, Predictive Modeling, Data Science
Technologies
Python (Pandas, Scikit-learn), SQL