Mark Phillips

By: Mark Phillips

As a strategic sales specialist and innovator Mark has experience in catastrophe risk modelling. Between 2011-2016 he worked at RMS with the London, Lloyd’s and European insurance markets to help insurers develop more sophisticated risk models and adopt complex new technologies. His recent expertise also includes working with technology and insurtech startups, helping them to develop more powerful and compelling value propositions.

24 May, 2018

ANALYTICS, Quest, Marine, Blog

Digitalisation in Marine Insurance - Part two

Part two - Technology to optimise your existing portfolio

In my last blog post, I talked about the pressures being faced by the marine insurance industry and how it’s essential that companies use data and analytics technology, alongside blockchain, to address loss ratios and innovate. In particular, with the availability of historical and real-time marine risk data increasing exponentially, it is now possible to combine this data, with historical loss and exposure information to:     

    1.    Segment and optimise the portfolio
    2.    Identify new sources of risk and opportunity
    3.    Innovate with new products targeted to specific segments

In this post, I will focus on how data and analytics can be used to address the first of these outcomes. And in the following two posts I’ll look at two and three. 

A word on the state of the market… 

In other lines of business such as P&C, we have seen investment in modelling and data resulting in a change in market dynamics. Transaction conversations and risk transfer become data driven and, over time, technical modeled loss ratios and the release of new views of risk, impact pricing and shift authority towards those with the greatest insights.

Catastrophe events cause intermittent hardening of rates, but lead to the release of new views of risk, continuing to fuel the conversation in the minds of the market and reinforce the need for data and analytics.

Marine underwriters are facing an entirely different dynamic. With the exception of events such as Sandy, Marine insurance doesn't face regular catastrophic losses on the same scale as other lines of business. In addition, with most actors using the same methodologies for measuring and rating, the market has settled on a standardised approach to pricing and competition which has led to coverages that are global in nature with few exclusions.

 When we consider the impact of data and analytics on the marine insurance market therefore, it’s lazy logic to conclude that insurers can simply make a better argument for an increase in pricing or risk selection. The likely outcome of such an action would see the business simply being taken to another market. Indeed some major hull accounts have recently been lost to other markets as Lloyd’s attempts to harden its pricing.


A better application of this technology, in the medium term, is in leveraging data-driven insights to optimise your existing portfolio.


Segmenting and optimising your existing portfolio 

Most insurers assess portfolios and allocate capital today based on a mixture of underwriting experience, claims history and weighting factors such as vessel type, tonnage, year built, yard built. In effect, this means that portfolios are segmented arbitrarily, based on factors that do not adequately reflect the nature of the underlying risk. For example, passenger vessels would carry a particular weighting, but in reality passenger vessels are made up of large cruise liners, as well as smaller craft. 

In effect, this means that insurers can only ever have a limited understanding on the nature of risk in their portfolios, creating the risk of unknowns and leading to allocation methodologies that are inefficient.

By its very nature, marine risk is behavioural, it moves around and claims are caused by a range of real-time incidents such as where the vessel has been, crew negligence, collision, machinery breakdown, piracy etc.

With big data analytics platforms, insurers can now combine existing methodologies with claims history and vast third party data sets to identify new correlations that better reflect the nature of the underlying risk.

As well as considering every vessel type, it is now possible to analyse individual vessel and fleet behaviour over time to identify the impact of these behaviours on claims frequency and severity. For example, companies can identify the relative weighting of factors such as vessel area of operation, number of port visits, days at sea, high-risk zone incursion, mileage, time laid up etc. 

Using these new correlations, insurers can build new views of marine risk encompassing specific, detailed parameters and ultimately identify what characteristics indicate low risk or high risk.


Applying this new methodology at the portfolio level, new segments can be identified and their relative risk weighting calculated, leading to more efficient capital allocation and a portfolio-first approach to underwriting strategy. Moreover, with increased segmentation of the portfolio and a deeper understanding of loss behaviour, companies can take a more targeted approach to outwards reinsurance, potentially resulting in an increase in Fac for certain segments. 

Tune into the next blog in this series to find out how technology can help you move from optimising your existing portfolio, to identifying brand new sources of risk and opportunity.
In the meantime, if you’re interested to find out more about Concirrus and our work in the Marine Market, take a look at this case study which directly illustrates the bottom line impact of our software Quest Marine.

Also, don’t hesitate to get in touch, via our website or connect with me on LinkedIn.


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