Closing the gap

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Insurance is an old and highly regulated industry. Perhaps because of this, insurance companies have been slower to innovate and embrace technological change compared to other industries – but change is coming, and to remain relevant, firms need to start moving. 

Globally, more and more insurance companies are beginning to augment their technological capabilities so that they can do business faster, more cheaply and more securely.  

A key technological trend currently in the limelight is the application and use of artificial intelligence (AI) and machine learning. There are multiple ways that these technologies can and will help drive savings and benefits. 

Customer experience/personalisation: AI can enable a seamless automated buying experience, leveraging social and geographical data points relevant to individual customers, supercharging the use of tools such as chat-bots and invigorating nascent product offerings such as insurance on demand. 

Enhanced claims processing/settlement: AI can and is already being used by some firms to improve claims processing by progressing queries and claims, from initial reporting to ongoing communications with customers.  

In some cases, claims do not require any human interaction at all and for the firms that have already begun to automate portions of their claims process, considerable time savings and increased quality of service benefits are being realised.  

Online interfaces and virtual claims adjusters make it more efficient to settle and pay claims following an accident, while simultaneously decreasing the likelihood of fraud. 

Behavioural pricing: Using the ever-increasing range of sensor-enabled (Internet of Things) devices to collect personal data, which is analysed via AI-enabled platforms, will help pricing models to be tailored to individual usage patterns and needs, i.e. usage-based insurance. 

Trends are great, but in order to prevent them from turning into fads or resulting in failed (and possibly expensive) projects, the problem to be solved and the outcomes required – along with the technology itself – need to be clearly understood.  

Generally speaking, when contemplating the application of AI, the better the data quality, the better the results will be (in broad terms). However, this is augmented by understanding data bias and then being able to understand how decisions and results have been derived (with the use of machine learning).  

An example of data bias would be to build an AI that may not be a good representation of the environment it is running in. An AI for a self-driving car that is trained with data from the daytime will be biased towards working in the daytime. To remove data bias, data from night-time needs to be included also.  

With the correct understanding and appropriate use of these new and powerful technologies, there is huge potential to transform the insurance experience for customers – from frustrating and bureaucratic to something fast, on-demand and more affordable.  

Tailor-made insurance products will attract more customers at fairer prices. If insurers apply AI tech to the mountain of data at their disposal, we will soon start to see more flexible insurance such as on-demand, pay-as-you-go insurance, and premiums that automatically adjust in response to accidents and customer health etc.  

We will see insurance become more personalised, because insurers using AI tech will be able to understand better what their customers need.  

Insurers will also be able to realise cost savings by speeding up workflows and will discover new revenue streams as AI-driven analysis opens up new business and cross-selling opportunities.  

Most importantly, the best outcome for all concerned is the very real prospect of all this being able to make it much easier for customers to interact with insurance companies and people being more likely to purchase insurance. 

Shahid Safdar is managing director, Middle East, at Charles Taylor InsureTech 

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