Is your business information rich, but knowledge poor?!

Excel in Business (EiB) Is your business information rich, but knowledge poor?

At BIBA 2017 we conducted a survey of 60 Brokers, Insurers and MGAs, as part of our ongoing research into Management Information in the Insurance Industry…

With the insurance industry so reliant on its data for writing profitable lines of business, and the looming regulatory changes that are set to demand increased data transparency, the findings from our survey were quite alarming…

EiB Insurance MI Survey Infographic from BiBA 2017 Conference

It may not come as a surprise to you that a resounding majority of respondents acknowledged the accuracy of their data as the most essential aspect of their MI. However, despite this, almost half of the respondents also expressed a lack of confidence and reliability in their quality of data.

Something doesn’t seem quite right there to me…

Interestingly, our findings also aligned with those of Deloitte’s ‘Information rich, knowledge poor’ white paper, which surmised that mastering information management remains a constant challenge for the insurance industry.

So,let me get this straight, you’re aware of the inaccuracy of the data being presented to you by your MI team, or you’re even producing this erroneous MI by yourself, yet you still continue to calculate your written and earned premiums knowing full well the risk this carries…It’s downright kamikaze! Especially given you have a carrier breathing down your neck demanding you prove the accuracy of your MI!

I have been working with several MGAs for over 5 years now, to explore the main issues which they are faced with when producing their MI reporting, and, to be perfectly honest, I have to take my hat off to you all! How you manage to produce such complex reports like triangulation statistics or loss ratios, manually, month after month, I do not know!

It has been a real eye opener, and I can honestly say that after all these years I now totally understand the real-world challenges which you face when it comes to sanitising your bad or erroneous data, especially given the diverse premium and claims data sources which are often submitted via EDI.

So we’ve developed a solution, EiB Insurance Analytics, especially to provide automated premiums and claims MI for MGAs, Insurers and Brokers. It’s been tried and tested many a time, in fact, at one MGA, the solution rejected 24% of all transactions, as they were factually invalid from an MI perspective. Would you even know this though? It is 99% certain you wouldn’t right now.

That said, I’ve put together a brief example of how our EiB Insurance Analytics software automates calculations within just a few minutes, something which would normally take you days of manual effort and expense – and all without leaving Microsoft Excel!

It even allows you to examine key metrics, such as Written Premium, Earned Premium, Loss Ratio, Incurred Loss Ratio etc., by Underwriting Year, Policy Start Year and a raft of other business views such as Broker, Product, Class, and Claims type. Then of course add any views pertinent to specific classes of business e.g. Motor – examples could then include Age, Occupation, Area, Post Code, NCB Banding …

 

The list is endless, and entirely dependant on what source data you already have at your disposal! So do please feel free to check it out by clicking here.

This really is just the tip of the iceberg as to how I see the future of analytics progressing for your industry, so if you’ve found any of the information in this article interesting and would like to learn more about our EiB Insurance Analytics solution or discuss any of your own MI issues or ideas, then please do get in touch. I would love to hear from you!