Technology moguls and billionaires Elon Musk and Mark Zuckerberg have taken opposite sides of the issue, with the former saying that AI is a “fundamental risk” to civilization and the latter extolling its virtues.
The insurance industry in Asia and in some parts of the world mostly welcomes AI technology, and most of them are at the forefront of adopting newer technologies.
Most insurers don’t have to deal with debilitating legacy environments, or are more willing to explore and support these technologies in view of the impact they deliver relatively quickly and at lesser costs. With the proliferation of artificial intelligence (AI)-based technologies in the recent past, Most insurers globally and in Asia have started taking their first steps in that direction. While 80% of the business leaders surveyed for a recent Microsoft–IDC study agreed that AI will be pivotal in their business competitiveness, around 40% of all large enterprises reported AI deployment of some type in their business.
Conversational AI tools are becoming more common, with insurers harnessing these tools for improved customer engagement, assisted sales, product advisory, claims notifications, and more. Several new tools and use cases are also emerging in the area of more complex, algorithm-intensive solutions — such as underwriting and claims assessment, as well as fraud reduction.
While there is near-unanimity in the insurance industry leadership on utilizing AI, there is also a growing realization that AI would evolve into a strategic capability instead of stopping at merely providing point solutions to specific present-day problems.
Best practices for embracing AI
The impact of AI on insurance as a “tectonic shift.” As such, it is important that insurers are well-prepared for the shift by understanding the business impact of AI, calibrating their approach to adapt AI for the most suitable areas, and developing and executing structured plans.
The following pointers for insurers that want to successfully adopt AI:
- Understand the technology, related impact and lessons learned so far. There are several instances of experiments driven by AI and related technologies in the insurance industry. While many are in the early stages of adoption and usage, there are several instances of early failures as well. Insurance companies must spend time understanding the context of these experiences. Technologies around Machine Learning (ML), natural language processing (NLP) and conversational AI have been evolving fast. Several point solutions have emerged over the last few years. However, true enterprise-impacting solutions on advisory, underwriting and claims have yet to gain traction. Industry learning must be considered while developing implementation roadmaps.
- Integrate the adoption approach with enterprise strategy. Most instances of AI usage and adoption are predominantly in the experimentation mode. As the technology evolves, it would be critical to factor overall enterprise strategy and goals into the actual approach. Linking the AI strategy with specified business KPIs ― be it improved customer experience/NPS score, lead-to-contract conversion, fraud detection or even overall claim-ratio reduction ― will provide important strategic direction and purpose. All downstream work around technology roadmaps, solution selection, and data strategy should have traceability to the business objectives.
- Do not compromise on data foundation. The insurance industry is rich with data, but not known to leverage the data for attaining enterprise goals. The success of AI usage and adoption is predicated on a sound data foundation. It is almost existential for AI use cases to make a meaningful impact, be it underwriting or claims. In many instances, it may be a long, drawn-out exercise to get the data foundation right, but it is every bit worth the effort and cost.
While AI and associated technologies do have benefits, skeptics’ concerns regarding some ethical issues are not without merit. Insurers must seek to maintain the trust of their customers as well as maintain the integrity of their systems.
Trust is key to the insurance business, The insurer’s job is to keep promises at the end of contracts or when defined events happen. With the explosion of data around us and blurring lines between private and secured data and public and unsecured data, it is important for the insurance industry to keep its fundamental promise of trust intact while leveraging AI.
Trustworthiness will become even more important as insurers gain access to more and more data about individuals.And as responsible corporate entities dealing with sensitive customer data, insurers should define and devise their own data protection strategies and policies in consonance with the promises they have made to their clients, who, in turn, will certainly reward these companies well.