Blog: Artificial Intelligence in insurance: challenges and opportunities – The Sociable
With extreme efficiency and in an unprecedentedly short time scope, artificial intelligence (AI) is moving insurance towards the ultimate customer goal – convenience.
Getting a personalized insurance plan, requesting immediate assistance, and resolving claims within seconds. Sounds impossible, right? Well, now it’s not.
Now, there’s a clear consensus about the power of AI in insurance, and customers of insurance companies can now get the full package without ever leaving the comfort of their homes.
Approximately 75% of executives believe that it will either significantly alter or completely transform the whole industry in the upcoming years.
But while there is great potential, there are also reasons to be cautious.
A rushed, one-sided, or superficial implementation could avert the journey towards more data-driven and customer-focused service.
AI shakes up the status quo of the whole insurance industry, with the potential to manifest in practically every sphere of operations. With capabilities to carry out advanced analytics and assess big data, companies can deliver more informed and personalized services. They can utilize predictions, optimize their processes, save costs, and develop better risk management strategies.
This results in better customer service, with bots able to construct customer profiles in order to suggest the best product. Such an individualized approach, combined with speedy delivery, ensures a smooth onboarding process and comprehensive support throughout the customer life cycle. The contrast with manual methods is impressive: Underwriting solutions based on AI can save up to 97% of the overall time resources.
The speediness also pervades claim settlement. Without unnecessary bureaucracy, claims can be resolved within three seconds.
Fraud detection is also streamlined: AI can evaluate data from multiple resources such as facial recognition or devices connected through the Internet of Things. These could include personal health gadgets, tools assessing your vehicle state, and even drones, which would be deployed to review the situation both when signing a contract and post-loss.
However, it’s not all plain sailing when it comes to AI implementation in insurance. The main challenges can be broken down into three categories.
The first concern is data quality. AI assesses a variety of structured and unstructured data that includes historical claims, personal documents, transactions, investigative reports, GPS data, images, and others. But the system can only function properly if these are built on organized and exact datasets. This is particularly important considering that AI will itself develop by learning based on these large volumes of data.
While disruptive technologies bring new opportunities, they also introduce new threats. Insurers must therefore always keep privacy and legitimacy issues in mind – the second challenge in this area. They may need to redesign the existing security policy.
Also, as they may encounter aversion towards AI, they need to convince both staff and customers that the innovation isn’t an imposed tool utilized by the company to avoid responsibilities, but a solution that empowers, supports, and guides better decision-making. This is still an industry-wide goal, as 60% of consumers have expressed fears about purchasing coverage via chatbot.
Third, insufficient infrastructure compatibility and the lack of resources may bring chaos into the implementation process. Companies without a comprehensive plan or clearly delegated responsibilities and goals are more likely to encounter glitches and ambiguities. Whether with hardware, software, systematization, or manpower, there’s a need for the capacity and will to develop and monitor the processes.
There are solutions, however, to these issues that can guide insurers towards smooth AI integration. AI isn’t just implemented out of nowhere – its quality is likely to resemble that of existing processes. Companies should prioritize data quality and promote transparency in their operations.
This requires a systematic approach to data that includes pre-processing, normalization, image processing and design of deep learning algorithms to general framework for the AI system.
With privacy and legitimacy concerns, insurers have the task of managing expectations, while also building greater trust in AI. They need to implement a robust security architecture and train staff. But fostering understanding goes a long way, too. Customers need to be reaffirmed that whether it’s pricing, customer care, security, personalization or settling claims, they are entitled to continuous support.
Building a powerful infrastructure is crucial, and implementing AI may require a different IT environment than the one in place. Insurers should make sure to conduct targeted investments to facilitate migration to new technology systems.
Likewise, they should implement a comprehensive gameplan. AI often fails due to the lack of clarity or implementation only for change’s sake – so instead, insurers should critically assess the process and make sure it always contributes to wide-scale organizational goals.
AI can only be driven to its full potential if companies fully understand both the challenges and benefits. While the process may be laborious and requires financial and time commitments, it can positively transform the way insurance services are delivered.