Adoption Of Technologies Like AI, ML By the Insurance Sector
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The 21st century has given a stage to the discourse around technology and its ability to enable co-creation and seamless service delivery. While businesses across sectors are embracing technology-enabled solutions, technological innovation continues to outpace technological adoption. Even though the insurance industry has been cautious in leveraging technology in the past, in recent years it has accelerated its pace of technological adoption. Several insurance companies are endeavouring to introduce automation in their processes and leverage technologies such as artificial intelligence (AI) and machine learning (ML) to embed themselves in the customers’ journey and offer hyper-personalised solutions seamlessly. While newer ways of leveraging technology are envisaged on a regular basis, we discuss a few ways by which AI and ML are transforming the insurance sector.
The era of data explosion
Data has always played a central role in the insurance industry forming the foundation of most decisions. Today, the human race is creating more data than ever before as we get deeply entrenched into digital solutions. As a result, the insurance industry today finds itself in a very unique position—it has to contend with an explosion of data from myriad sources including telematics, online and social media activity, voice analytics, connected sensors and wearable devices. The insurance industry now needs technology and machines not only to mine this data but also to leverage it to derive analytical insights. This is where technologies such as AI and ML can play an integral role in the insurance industry.
Accelerating adoption of AI & ML by the insurance sector
First, what does AI actually mean and why is there so much excitement around this technology?
Simply put, it is the science and engineering around making machines intelligent. The aim is to ensure that machines are able to mimic cognitive functions associated with human minds. These cognitive functions include all aspects of learning, perceiving, problem-solving and reasoning. Only thing is that machines are able to do all these faster, more accurately, often at a lower cost. ML, at the same time, is a set of techniques that give machines the ability to automatically learn, especially about things that can’t be conventionally programmed, by leveraging and harnessing data gathered—just as humans do through experience. Clearly, these technologies can play an integral role in understand the needs of the insurance customer better and in building and delivering the relevant solutions. They can help insurers solve business challenges and enhance their value proposition across the insurance value chain, i.e., from underwriting and loss prevention, product pricing, claims handling, and fraud detection to customer acquisition and servicing.
From the what to the how of AI and ML
AI-powered technology enables the insurance companies to better understand their customers and embed themselves deeper into the customers’ journey. The widespread adoption and use of existing devices (such as cars, fitness trackers, home assistants, smartphones, and smart watches) will continue to increase rapidly, further exacerbated by new categories such as clothing, eyewear, home appliances, medical devices, and shoes. The resulting explosion of data engendered by these devices presents insurance companies with an opportunity to understand their clients more deeply, allowing them to create more product categories and more personalised pricing, all delivered in an efficient and seamless manner.
One of the biggest roles that technology can play is in better risk assessment and claims settlement. Automation and AI-powered technology can now help insurers assess the risk associated with an individual. Wearables can help insurers understand an individual’s lifestyle and assess whether the person leads an active healthy lifestyle or an unhealthy high-risk life style. Devices fitted to cars can help insurers understand a person’s driving habits and assess whether the individual is a safe driver or a risk-seeking driver. This means that instead of putting individuals in groups and then deciding the premium-based on the profile of the group, insurers can now price the premium based on the individual’s unique risk profile. For example, while buying a health insurance policy, a healthy individual would have the benefit of paying a lower premium compared to an unhealthy individual. Similarly, when it comes to settling claims, AI-powered tools and robotics can help insurance companies assess the damage more accurately and quickly so that the claim is settled at the earliest. For an insurance buyer, this can prove to be invaluable.
The important thing to remember is that technology adoption should not be done in silos. For the industry to truly benefit from technology, it needs to be holistically embraced by the entire ecosystem. Enabling technology infrastructure needs to be created such that all insurance companies, advisors, the regulator, and the customer can talk to each and co-create value accretive solutions.
Finally, the power of these technologies will need to be harnessed with a sense of responsibility. The data used by the AI will need to confer to strong data privacy, data security and customer consent norms. And the AI/ML decisioning models cannot introduce discriminatory biases in access or pricing. For this, the insurtech ecosystem and insurers will need to work closely with the regulator and society at large, so that this revolutionary technology can truly deliver on the promise of a better safety net for all.