Data Analytics in a New Era

Andy Yeoman, CEO of Concirrus, is an expert at harnessing the power of advanced data analytics. Here, he considers the future of data analysis in a new era of connectivity.

Data analytics is a means goal, not an end goal

QHow would you describe the Concirrus business model? Do you think this has been a factor in attracting young talent, given 60% of Concirrus employees are under 35?

AOur business model as it faces the customer, and our business model that faces our employees are slightly different. As we face the customer, our business model is very much geared around ensuring that we add value to our customers. So, where we charge them, we do so on the basis that we add significant value. I think the industry statistics - which are consistent with our own insight – are that for every $1 we charge we should be adding $10 of value.

But the model, as it relates to our employees, is different. Attracting young talent in London is a challenge in itself. We believe we are in a privileged position where we’re bringing about change in a global industry. We are supporting a 300 year-old industry, which has been operating in a certain way, through cutting edge technology. People are recognising that we are securing strong customer traction in the market, and they undoubtedly find this attractive. In normal times, our working environment is also appealing, we are a very sociable team and encourage activities such as running, pub quizzes and a collaborative approach, both inside and outside of work. With our team currently working remotely, we have implemented an extensive program of team-building activities. We hosted our first live workout yesterday and we’re encouraging the team to exercise and get some fresh air. I also hosted our first remote meeting with the entire 80-strong team all joining via video/voice. We’re running quizlets (mini-quizzes) throughout the week and so much more.

QWe’ve discussed earlier in this issue who is the modern day ‘expert’, i.e. human vs technology, do you believe the need for highly experienced individuals in the insurance industry is being overshadowed by the power of data and the use of tech?

ANo, I do not. I think the technology we are seeing is enhancing the experience of the experts, rather than diminishing it. For example, if you

We are entering an era where it is no longer a competitive advantage to have tech and data in the underwriting process

were to play a game of chess against a computer, you would probably lose. If a grandmaster plays against a computer, they may well lose. But, if you have a grandmaster with a chess computer playing against another computer, they will likely win. So, it’s not so much human versus tech; it’s humans supplemented by technology. With the current pandemic, it’s never been more important to have a set of digital tools to support remote working and team collaboration.

QWhat is Quest?

AQuest is the name of our product which, at its broadest level, is a digital underwriting platform powered by data analytics. Facilitating a remoteworking and collaborative approach, we supplement traditional underwriting decisions using static factors, such as - from a marine perspective – how old a vessel is, where it was built, what type of vessel, etc; and allow decisions to be based on behavioural factors, i.e. how the asset is used. Quest combines more than two trillion data points with deep analytics of what behaviours that drive claims, and wraps it up into a very easy to use interface to improve underwriting results.

QWhat is the most effective method to implement data analytics?

AWell, I would say that is via Quest! But beyond that, the method is more about having clarity of purpose. The challenge with data analytics it that it’s a bit like decorating a house – you can decorate endlessly, but there will always be something else to improve. The same is true of analytics. There will always be something else to analyse. We live in an age where there will always be more data, which will be more varied, more frequent, and more accurate. Data analytics is a means goal, not an end goal – you could keep going continuously. The best way to use data is to start with the end in mind, and have a very specific purpose. You could say, I’m going to have a sprint of activity to see if I can identify insight that would look into how better to segment my book of risk, or how better to analyse a given behaviour, or how better to take that behaviour and drive that into an underwriting process, etc. Having clarity and purpose, for me, is one of the clearest and most effective goals for any data analysis project.

QDo you think the differential in outcomes between the use of tech and data, compared to traditional risk assessment and underwriting techniques, is broadening at a significant speed?

AThe short answer is yes. We are entering an era where it is no longer a competitive advantage to have tech and data in the underwriting process. If you don’t have it, you are at a significant disadvantage. If we consider the marine sector - if someone were to present you with a fleet of business that you’ve never seen before, traditionally that could take anywhere between 24 hours and a week to produce a price. But with our tech, we can do that in less than five seconds. We’re at a stage where it’s now going to be impossible to compete without having one of these tools available in your portfolio.

It’s not so much human versus tech; it’s humans supplemented by technology

QAggregating data is clearly important, but how do you ensure the data’s quality and accuracy?

AAt Concirrus, we have an entire team that is focused on data acquisition – the accuracy is of paramount importance. If you’re going to have a system that will guide underwriting decisions based on data, then it must be correct. Our team curate the data, finding new data sources, then spend time looking at the quality of that data. So, for AIS data, the data may be 65 or 70% accurate. Once this is presented to us, we have a whole series of steps to scrub and clean up data, getting it up to the 90%+ marks. These steps include: defining clear goals and metrics about what you’re trying to achieve in the cleaning process; ensuring you have good systems in place for cleaning the data; and testing the data against a model to ensure you identify any outliers before the data is put into production.

QWhy is real-time data collection and interpretation significant?

AThe reason this is significant, is because we live in an environment where risk is ever changing. If you think about how the insurance industry has worked for many years: people will write a policy on the 1 st of January, for example, and then won’t look at it until there is a claim, or a renewal twelve months later. But in reality, the whole landscape of risk is changing. For marine, all of the vessels you cover could be in one port at any given time, and now you have an accumulation of risk that is unknown to you. Having real-time data about what your accumulations of risk are, what the interpretation of them should be, and what this means to your business, is important. Not having this is an existential risk to the insurance industry. The companies that can’t do this going forward, will find themselves in very difficult situations.

I think the technology we are seeing is enhancing the experience of the experts, rather than diminishing it

QTo what extent do connected policies advance the industry?

AI think they are a massive change for the industry as a whole. We are in very interesting times, with Covid-19 having an impact on the industry. As I have said previously, the nature of risk is changing. A connected policy allows the policy to flex on the underlying behaviour. For example, with marine, if many people stop using their vessels and they start holding up ports, a connected policy will allow insurers to react to this changing utilisation. Automation means that the policy is more effective in covering risk – with more detail and accuracy – and it’s done more efficiently. There is little need for manual interaction, so the customer receives a better, tailored, and more efficient policy. Conversely, the insurer knows more about the risk they are writing, allowing them to deal with changes and do do so with a low administrative cost.

QConcirrus has seen rapid growth since launching as a start-up - what do you think has been the key to this and what does the future hold?

AWe’re asked this question a lot and it’s always quite difficult to answer. We’re only doing what we’re doing, so it’s difficult to compare and contrast to others. We’ve had a meticulous focus on our customers’ businesses, we’re spending a lot of time trying to understand the issues they face on a day to day basis, and looking at how tech can have a positive impact. I think too many start-ups spend time thinking about their own business. But when you’re focused on your customers, that makes for a much more interesting conversation. In terms of what the future holds, we are focusing on converting those customer wins and extending our product across their organisations – underwriting through to claims – as well as across new and emerging business lines: marine cargo, automotive, and transportation.

Of course, we have the short-term challenge around Covid-19 in terms of how we respond and help our customers through this difficult situation. Interestingly, with the new homeworking policy, it’s really going to challenge marketplaces like Lloyd’s, who have been operating as a face-to-face business Having a technology like ours, which is essentially a web-based tech and facilitates collaboration for a virtual result, is very timely in the market. Strangely enough, I believe that loss will be a short-term impact for businesses. The long-term situation will be challenging working practices. Overall, I think it’s going to be very positive for the industry.

Andy Yeoman is the CEO of Concirrus.