Learn more about how MGA Antilo are harnessing big data and machine learning to overcome the latest market pressures.
Underwriting models within commercial automotive insurance lack a view of changing risk. They are reliant on static datasets, providing a snapshot of exposure at a single point in time. Telematics were introduced to remedy the situation but have not delivered on the scope promised, despite heavy investment. Devices currently send a significant number of false positive alerts as first notification of loss (FNOL) that consume the time of claims handlers. The format of FNOL data received is also not consistent amongst suppliers. Handlers must therefore manually separate false positives from actual incidents before transcribing the data for others in the value chain.
These limitations mean that adaptation to market pressures can be difficult, impacting customer experience in what is already a very competitive market. Overcoming each hurdle will improve market understanding and segmentation, unify datasets throughout the supply chain and optimize the handling of false positive FNOL alerts. Such a transformation will no doubt improve organizational performance and translate to a better customer experience. This makes it more competitive for new business, whilst also increasing retention.
Antilo is a Managing General Agency (MGA) that offers commercial insurance to Brokers operating throughout the UK. Alongside existing market trends, Antilo recognised an increase in short-term contracts and a greater frequency of fraudulent claims. With legacy systems struggling to cater for the sharing economy, and the need for a deeper understanding of events around a collision, Antilo expanded its Partner Network to implement new systems. It is one of the first within the sector to adopt a data aggregation and insights platform, leveraging big data and machine learning to overcome the latest market pressures.
The platform unifies all data formats into one digital environment, combining it with vast datasets and machine learning algorithms to identify behaviours that correlate to claims. Existing vehicle infrastructure investments remain relevant, whist the added intelligence allows for further segmentation and identification of potential business avenues. The real-time nature of data aggregation allows for an always-on approach to protecting the Insured and ensuring cover remains relevant. This is particularly effective for on-demand business models.
Antilo have coupled the platform with video telematics, introducing a new product for taxi drivers who are either considered high risk or operate in high risk areas. Adding a visual feed to telematics data provides a holistic view of an incident, protecting the Insured from fraudulent activity. The real-time nature of data collection ensures the FNOL is received instantly so that claims handlers can resolve incidents quickly and effectively. The reduction in the resolution of FNOL’s lowers the overall cost of claims handling whilst further improving customer experience.
The unification of a data pipeline with Partners in the value chain allows for shared insight and the operation of automated workflows to improve efficiency. The collective approach to digitalisation makes for a highly effective solution to the challenges facing the industry.
“Big data and Machine Learning allow us to accurately understand the market. Automation allows us to apply our knowledge in a manner that improves results. We can identify new segments and develop specific products that better serve the market. Specifically, higher risk areas which we wouldn’t typically operate in. Our new software platform gives us insight into the behavioural factors behind risk. We don’t just understand new trends, we can implement initiatives to control their influence.” – Viv Gilroy, Managing Director, Antilo
For an in-depth look at Antilo’s innovative deployment, download the white paper below.