The insurance industry is a sector built on data and, with the increasing amounts being generated, data mastery and digital technology will become vital parts of the insurance process.
Data becomes valuable information when you can spot patterns, but this can be difficult to do if your output is presented in a format that’s not easy to share or understand.
What is data visualisation?
As the name suggests, data visualisation is the practice of presenting data in a pictorial or graphical format, such as an infographic or graph. It enables concepts to be more easily understood and makes patterns quicker and easier to spot.
Although it’s easy to believe that data visualisation is a modern concept, the first documented example is an illustration of geological resource distribution from 1160BC!1
In modern times, digital dashboards have provided great leaps forward in making data more accessible. With the advent of big data – extremely large data sets – we expect a rise in demand for visual data representation. With the advent of tools such as Power BI, this is becoming a reality.
Benefits of data visualisation
For organisations managing high volumes of data, visual representation brings insights to life across the organisation. This allows brokers and underwriters to make fast, informed decisions from their preferred location or device, speeding up decision making overall.
Organisations across the insurance industry are using data visualisation to aid areas of their business such as:
- Claims analysis
- Fraud detection
- Premium forecasting
- Revenue comparison2
For example, data visualisation tools can not only help a business communicate risks to customers, but improve their effectiveness at providing support to them in the aftermath of a major incident. 3
It can also be used to analyse claims to check for anomalies that could indicate fraud.
Visualisation in niche markets
In organisations that serve niche markets, specialist risk modelling is traditionally carried out by external providers, for example, the calculation of flood risk based on detailed historical analysis of flood plains, maps and weather events.
With the advent of more powerful data modelling tools we’re now able to quickly overlay and incorporate data from a variety of internal and external sources to create an overall, and more accurate, picture of risk, removing the need for additional costs in many cases.
Global insurer creates a clearer picture of risk with data visualisation
Many insurers have modelling products which enable them to visualise risk in a specific part of their organisation. One global insurer, however, wanted to take this a step further – taking data from across internal and external systems to paint a much clearer picture of risk for its portfolio.
With the insurer responsible for providing insurance services for shipping fleets carrying cargo across the globe, Coeo helped them to improve their risk calculations by leveraging advances in cloud analytics technologies. Rather than simply rendering data from multiple source policy systems in a dashboard, Coeo created an interactive map that includes key pieces of data such as the course a ship is taking, the value of the contents on board, long-range weather forecasts and reports of piracy and civil unrest.
With real-time movement tracking, the insurer now has a living, breathing representation of its risk exposure in real time and an intuitive way to explore the potential impact of natural disasters and other risks that would be difficult to do using a traditional spreadsheet.
Reversing the race to the bottom
This post showcases one of the seven steps that that we believe organisations in the insurance sector can take to accelerate their own adoption of a data culture and increase their time to value on technology investments.
The other key steps were:
- Offer value-based services
- Maximise data assets
- Measure the right things
- Evolve the business model
- Cross-sell and up-sell
- Model your future
Click the banner below to access our full whitepaper on data culture in the insurance industry.