Commercial fleet insurance from Antilo

Harnessing big data and machine learning

Antilo is an MGA that has fully integrated their data pipeline with key stakeholders to remove all barriers to operational efficiency, whilst leveraging existing telematics to develop a new commercial automotive insurance product.


Changing risk landscape

Traditional insurance underwriting models use static datasets and historic claims to price premiums. This method does not allow for changes over time to exposure and limits the products and services that can be offered by the market.  

Reinventing commercial automotive insurance

Data can augment client relationships by helping to:


Automate standard risk placement for faster service


Provide real-time risk management services to reduce losses


Create new products and services to better protect clients

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Connected automotive insurance products

The use of real-time data such as telematics in insurance has given rise to new automotive insurance products tailored to individual drivers. 

Using real-time data the insurance community can:


Automate alerts to let drivers know when their risk profile changes


Provide immediate support to drivers in the event of a collision


Offer cover to new segments of the market that may have been categorised as high risk using traditional underwriting models

New era of automotive insurance

'We've invested in technologies that derive detailed insight from telematics data and digitally integrate with our supply chain. We now have a shared view of the market with our Partners. It allows us to deliver smarter products, assess risk more accurately and improve the customer experience.'

Viv Gilroy, Managing Director, Antilo

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Download the Antilo case study

This resource includes information on:

  • The need for change within automotive insurance
  • Overcoming existing barriers to entry
  • A breakdown of the new data pipeline
  • An overview of new First Notification of Loss (FNOL) capabilities
  • Q&A’s with partners throughout the value chain 



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