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Automotive Risk Mitigation Business Case

Continuous Intelligence for Car Rentals & Insurance to reduce accidents and optimize maintenance


Automotive, Car Rental, Car Insurance.


To reduce the risk of car accident by warning the driver and car control center about increased danger due to internal and external factors, such as speed, weather, maintenance conditions of vehicle, road conditions, etc.


The Financial Crisis of the last decade has helped the rise of the “Subscription Economy” or “Usership Economy”. This means that people now prefer to pay for using a good or a service instead of buying it and immobilizing capital – like companies that prefer OPEX to CAPEX. This is even more so in the automotive industry, where the investment required to purchase a car is significant, especially for middle – and lower – income buyers. 

The growth of fuel, taxes and insurance costs has further favored the spread of short-term rental services, i.e. pay-per-use cars, and long term rental services, mainly offered by car manufacturers. 

Near future forecasts show that the number of car owners will decrease rapidly to a small percentage of all drivers in the next decade. 

This trend has shifted responsibility and costs of maintenance to car rentals and insurance companies. These companies are thus accountable for the most expensive events in a car’s life, like road accidents, which imply repairment, insurance reimbursement and out of service costs. 

Technology comes to support these players in the management and the maintenance of increasingly more sophisticated vehicles. In the last ten years, technology applied to automotive has radically changed the industry, transforming cars into connected systems, able to provide in real time position, speed, maintenance status, mechanical parameters, and to receive information and alerts while assisting drivers in risky situations.

Business Case

Car Rain

Real-time analysis of events is based on gathering information from different sources (i.e. credit card payments, geo location of customers app, etc.) and rising an alert when complex correlated events happen. For example, a British customer withdraws cash from an ATM located in UK, and their last mobile banking app login happened abroad; a 70-year-old customer buys a skateboard online, spending 200£ while their average ecommerce purchase is 50£.

In both examples, the alert triggered by the anomalous event demonstrates that applying Event-Driven Architecture (EDA) to real time events can greatly reduce time to reaction and preempt fraud.

Long Exposure

A car rental and its insurance company get extensive data from the maintenance of their car fleet. This means that AI can automatically detect event correlations in the historical data, e.g., after how many km the brake pads are replaced for cars used in the city vs. in the suburbs. This is the conventional, static way to use AI/ML with historical data.  

A Continuous Intelligence platform allows a new dynamic approach to Data Analytics, enabling real-time decision making & automation. In this scenario, a car renter who lives in the suburbs starts daily commuting to the city and modifies their profile on the rental company website. The CI platform notices the profile modification, verifies the car geolocation and location history, infers that the commute has shortened the brake pads lifespan, and decides to notify the renter and maintenance team. This both produces savings for the rental company – brake pads are less expensive than discs – and increases the driver’s security.

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