Gathr > Case Studies > Real-time Driver Profiling & Risk Assessment For usage-based Insurance with Gathr

Real-time Driver Profiling & Risk Assessment For usage-based Insurance with Gathr

Product

Gathr

Tech Stack

AWS IoT Gateway
Apache Spark

Impact Metrics

Customer Satisfaction
Cost Savings

Technology Category

Analytics & Modeling - Real Time Analytics
Analytics & Modeling - Machine Learning

Applicable Industries

Automotive

Applicable Functions

Sales & Marketing
Business Operation

Use Cases

Vehicle Telematics
Predictive Maintenance
Usage-Based Insurance

Services

Data Science Services

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 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.

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

An end-to-end, real-time analytics application for driver profiling & risk assessment to enable personalized, usage-based insurance plans
Through driver profiling and individual risk scores, the auto insurer is now offering data-driven, and highly personalized, insurance policies and pricing plans
The insurance giant is now also offering predictive maintenance services that prevent vehicle breakdowns and repairs
The application makes it easy to track driver activity and vehicle data in real-time through a custom web UI and interactive real-time dashboards

Quantitative Benefit

Highly personalized premium pricing options are based on individual scores, vehicle type and make, and geography
Increased customer loyalty and claims reduction from value-added services
Customers can also easily track their own driving behavior and vehicle performance in real-time and take corrective action that can affect their insurance premiums