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Real-time Driver Profiling & Risk Assessment For usage-based Insurance with Gathr
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Automotive
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Predictive Maintenance
- Usage-Based Insurance
- Vehicle Telematics
Services
- Data Science Services
The Challenge
The auto insurance industry is increasingly investing in connected car solutions to offer simplified, transparent, and flexible products and pricing options. Usage-based insurance is a voluntary, behavior-based insurance program that uses analytics to create highly personalized and dynamic plans based not only on the driver’s age and other demographics, but also accounts for the driver’s behavior, risks related to a vehicle, and external factors such as driving conditions and weather. A leading auto insurance provider chose Gathr to ingest, transform, enrich, analyze and store automotive telematics data in real-time to build an end-to-end analytics application for driver profiling & individual risk assessment, and subsequently offer dynamic, usage-based, plans to its customers.
About The Customer
The customer is a leading auto insurance provider. To keep up with the new digital consumer and remain competitive, the company is increasingly investing in connected car solutions. The company uses a telematics device to capture and transmit vehicle performance, usage, and driver behavior data from various sensors in the car. The company chose Gathr to ingest, transform, enrich, analyze and store automotive telematics data in real-time to build an end-to-end analytics application for driver profiling & individual risk assessment, and subsequently offer dynamic, usage-based, plans to its customers.
The Solution
The solution involves real-time ingestion of telematics and sensor data using an AWS IoT gateway. The device captures data points such as driver behavior, vehicle sensor data, and usage data. In-memory data transformation, data blending, and data enrichment are performed as driver behavior, usage, and vehicle data arrives. The ingestion and enrichment stages provide a rich array of key attributes needed for the predictive machine learning models running on Apache Spark. These stages assess and predict individual risk scores. Classifying drivers as safe or risky and quantifying risk scores are based on current driving behavior, historical behavior, and supplemental data flows such as usage data, geographic location, vehicle type, vehicle performance, and third-party data. The application creates alerts to flag risks based on altered behavior patterns as well as anomalies in vehicle performance.
Operational Impact
Quantitative Benefit
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