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What is data driven?

We think it’s where your business model comprehends the importance and value of data, and as a result you take extra steps to ensure data integrity is part of the model that is at the centre of your business. 

As we evolve as a claim management business, and we understand the possibilities new technology can bring, strong data becomes central to:

  • Identify risks
  • Provide unique insights into portfolio performance, and
  • Assist with eradicating future claims and predicting risk values that arise

So as a data driven company we gather data to assist us to look back at risks and claim performance, but we also look forward to assist you in eradicating risks and in predicting value of risks that may materialise.   So data can enhance our performance in managing risks that have revealed themselves as claims, and provide insights to our clients to help them reduce the risks of claims in future.      

What of Covid?

If nothing else Covid has demonstrated the importance of data – daily numbers, flattening the curve, etc. All restrictions and lockdowns have been based on data science and models for the future. But it has also accelerated digital transformation – and the importance of measuring performance through data. Remote working increases the dependency on data to measure performance. Blame Amazon, blame Covid….it doesn’t matter, data is the new oil, and you need to understand your data. Be curious – ask the right questions – and your data can yield results that can increase the value you provide to your customers.     

Our history of data driving

I visited a new breed of insurance company, Simply Business, in London last year. Simply Business is an on-line insurance play, a broker of sorts, perched on the outskirts of the London insurance market. If I had needed convincing this tech movement was real, this visit would have done so a thousand times over.

Simply Business has over 800 people, but only something like 12 actual underwriters on 500,000 business policies. It is a tech play in the insurance industry and has very much disrupted the small business commercial space. It looks like Google, but it uses that tech to do what insurance companies do. By utilising advanced tech and a disruptive model, Simply Business is simply killing it.

The one word of warning I got, though, was that while tech-enabled better solutions – it wasn’t an end in itself. You still need a great business model that can succeed in utilising superior tech. Technology, when deployed well, can enhance human capability. Maybe the reason why it is taking so long for tech to have the impact we expected is because of the current disconnect between tech and human capability – the more connected tech is to the human side, the better the solution.

In 2018 Proclaim set ourselves a new, and slightly divergent path to become more than a claims company – a claims and data company. We know claims expertise and service delivery will remain the key to our success, so we are more than conscious of our people power, but we equally understand we need to harness the latest technology to deliver superior service and insights to our clients. In the past we had given our clients some interesting and valuable insights into their risks via SQL dashboards and ad hoc reporting. Now, though, we understand that to convert our business to a claim and data specialist means turning the data side of things upside down.

So we’ve had to do a number of things to reinvent the business, to set us on the data path, while ensuring enduring excellence in our claims management services and adherence to our successful business model. First, we recruited an experienced data scientist to ensure we had the knowledge to proceed in the most effective way possible. Second, we analysed our almost 20 years of existing data to understand it’s strengths and weaknesses, how we can cleanse historical data to strengthen any weaknesses, and also how to create more discipline going forwards in our data collection. Third, we invested in a new data visualisation platform, Power BI, so we could create powerful graphic and dashboard style insights for our clients.

This process has already garnered some interesting insights. For instance, we kind of suspected we had stretched our data fields to accommodate client requests over the years, but the exercise confirmed we should have insisted on narrower or more sensible filters for some data fields. An example is the number of different types of slips and falls we found in our data – we had got so granular that fine detail (like slipped on vomit, slipped on soft drink or grape or ice cream) was being registered, which was reducing the effectiveness of the insights we could offer across our customer base. You do have to keep the data fields to a working number that responds better across a variety of risks if you want to ensure value in your data. In hindsight, and with our new understanding of data tech, the lesson is that we should have pushed back on some of the early client requests that created multiple versions of what is, essentially, the same or a similar thing. As a result of this we have mapped a number of multiple cause codes into one code to give the data more meaning.   

We also realised that going forward we needed to be much more disciplined in our data capture and entry. We needed more structure around first notice of loss, which is generally the domain of our support team, but we also needed our Account Managers to see the importance of the data we enter (and which we had been doing sporadically in the past) and embrace the opportunity to create deeper insights around things our clients and underwriters would find of interest. Examples? Many of our clients require minimum levels of information in their reporting – such as the fields in the Lloyd’s bordereaux or APRA code reporting – but there are a bunch more useful insights we can share with our clients if we are capturing additional information. For liability claims this may extend to whether the claim is a workers recovery, the litigation status, and frequency of particular legal firms involvement in claims. This added detail can not only help give additional insights to our clients but can also help with predictive models in future.

Last year we rolled  out our new reporting for clients. We figure, for our corporate clients, data and risk insights will be key to managing the hard market and for our underwriting and agency clients these insights will be able to power superior underwriting performance. In some cases we have already demonstrated to curious clients some of the advanced interactive style reporting that really is next level data science, and the response has been unanimously positive. Those who see what we have developed understand its intrinsic value immediately.

The key to data insights is having the right data and then asking the right questions. We think we have the claims expertise to ask many of the right questions but we plan to consult closely with our clients, especially in the property area, to ensure we are getting the benefit of their expertise as well.

We still have a long way to travel on the data and automation road and there are many challenges ahead. Predictive models, data exchanges, automating first notice of loss and integrated chatbots all loom ahead. At the end of the day, though, the claim service will be the foremost part of our service. And as they said at Simply Business, even tech companies look at tech as an enabler rather than the solution itself. Data analytics and predictive models will assist the service delivery, but machines will never be able to provide empathy, and human understanding and expertise, all of which will remain the essential ingredient for service delivery on claims.

Technology will do amazing things to the claim function  – particularly in automating first notice of loss, and in providing amazing data analytics to give visual insights to risk, but in the middle will remain a claims person needing to show empathy to offer that great customer experience.

(Note – part of this article appeared in a previous blog; this is an updated version)