New technologies impact different sectors in ways that are specific to the industry, but the benefits of big data and predictive analytics are also finding new applications in various fields. Big data is the collection of solutions that process and analyse vast tracts of data to make best use of it, while predictive analytics is the various processes that enable data-based predictions. These technologies have the potential to transform the insurance industry where big data offers greater insights into customer activity and behaviour, and predictive analytics enable insurers to respond to data much faster.
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Better data insights are leading to improvements in data analytics in insurance. This is due to a wider range and higher quality of data, which means the information collected by insurers is more actionable.
New sets of data are based on first-hand information that is gathered from smart devices, social media and interactions with customers. Reports suggest that as much as 10MB of data is collected per household every day, through a variety of IoT-enabled devices. Because this information is taken directly from the source, it offers valuable insights for insurers.
When actuaries are able to make use of improved data and predictive analytics insurance modelling, products can be developed that better respond to rapidly changing businesses and markets, as well as risk patterns and risk concentrations.
Predictive analytics algorithms enable insurers to adjust their premiums dynamically. An example of this is in property insurance, where variables such as construction costs, weather trends and claim history can be continually monitored to more accurately predict risk and price.
Insurers are also able to use predictive analytics powered by machine learning to continually optimise and present more appropriate and relevant insurance products. This is through the analysis of customer behaviour, preferences, buying patterns and pricing sensitivity.
As previously mentioned, new ways of gathering data can lead to greater efficiency and accuracy in pricing and risk. But external data sources can have additional benefits for insurance companies in claims management.
With a higher number of cars that are equipped with sensors and mobile apps that gather data, insurance companies are less reliant on the parties involved to determine liability. Machine learning can also be applied to processing to ensure that claims are resolved faster.
Other uses of new technologies can be seen in smart thermostats and smoke detectors, which can detect malfunctions and fires, notify the relevant agency and keep a record. This can reduce the damages that insurance companies are required to cover.
With nearly a quarter of Americans using a wearable device to track their activity and vital signs, insurers are better able to make use of this data in pricing life insurance policies.
These are just some of the new sources of data that insurance companies can use in predictive analytics to determine risk, render claims management more efficient, and even as preventative measures that will increase profitability.
Insurtechs are smaller and more focussed on new technologies, data, AI and mobile app development. They are more entrepreneurial and disruptive to the traditional insurance industry. Technology companies are aware of just how valuable big data can be to insurance companies of all sizes. But insurtech firms are using data analytics to give them a head start.
Insurtech companies are using technological innovations in pricing and underwriting, settling claims, shaping policyholder behaviour, gaining insights on customers and improving customer experience.
Metromile and Root Insurance are insurtech companies that are using customer habits and driving distance to create usage-based auto insurance products. Friss and DataCubes use data science to accelerate underwriting and fraud detection.
In order to compete with insurtechs, more traditional insurance companies may need to take on a digital transformation that modernises their applications without losing valuable resources. This may be in the form of a unified platform that manages operational and analytical data in an effective solution.
Every year, the losses due to insurance fraud are in the tens of billions, which is costing the average customer more in insurance premiums. One answer to this issue that insurance companies have developed is predictive analytics insurance software, which reduces risk and prevents fraud.
By analysing data gathered by behavioural biometrics and behavioural analytics companies, it is possible to cross reference customer behaviour against past customer records to highlight suspicious behaviour patterns and fraudulent activity. This behavioural intelligence can be used with predictive analytics to determine policy premiums.
Behavioural intelligence can also be used to determine new customer risk, by using predictive behavioural analytics to compare each user against millions of others to find the most likely outcome.
In some fields, insurance companies are using data analytics to create tailored insurance packages. In the car industry and auto insurance, companies that include AIG and AXA are using driver behavioural analytics for this purpose.
An app named XLNT Driver is offered by AIG to record driver performance and give every journey a rating in terms of safety. The data is shared within AIG on the company cloud, it gives the company more reference information on which to base tailored packages, and it also encourages safer driving.
AXA has a similar app called DriveSafe, which records and shares journey information, and rewards points for safer driving. Individual drivers can then use their scores for discounts on their insurance.
Insurance companies are looking to big data analytics, AI and machine learning for even more opportunities to collect and analyse data. It is hoped by many that these technologies will enable insurance companies to prevent or reduce the risks of accidents occurring, thereby changing the approaches of insurance to become much safer.
Big data and predictive analytics are undoubtedly changing the face of the insurance industry. Those that are able to harness the potential that new technologies offer will be in a position to reap the benefits, whether they are in an established firm or a startup. This greater focus on data to drive innovation and growth is likely to continue, so moves to modernise must be continuous, and carefully considered.