Why the insurance industry can’t risk overlooking AI

Why the insurance industry can’t risk overlooking AI

Ulrika Mann

Ulrika Mann,

During the past five years, industrial use of artificial intelligence (AI) has risen exponentially across industries. Companies such as IBM, Apple, Toyota, and Fidelity have demonstrated an interest and appetite for deep research and innovation by introducing AI platforms and solutions for customers, partners, and employees. For instance, Toyota’s websites use AI to perform sophisticated, real-time reasoning to ensure feasibility or availability of the exact combination of options chosen by a consumer to design a specific vehicle.

At the same time, front-runners across industries are partnering with technology companies to identify game-changing business solutions that can be achieved through AI. An example is The North Face, which is experimenting with its Fluid Expert Personal Shopper tool, powered by IBM’s Watson, to provide customers with a more intuitive search experience through a natural language capability.

The insurance industry has not been immune to AI’s advancement – whether implementing robo-advisors for investment management (Vanguard and Charles Schwab) or applying AI to insurance and loan underwriting (the Chinese search giant Baidu, which provides enhanced risk assessment capabilities). With so much activity around AI experimentation and implementation – combined with customer demand, cost pressure, and the need to maintain or expand their foothold in the market – insurers can no longer afford to overlook AI and its implications.

Potential benefits to the insurance industry include:

  • Revenue expansion: The real-time learning and adaptive capabilities of AI provide a platform for insurers to explore new product lines, geographies, and cus­tomer segments, as well as quickly identify new avenues for revenue expansion. Organizations can scale exponentially by using virtual assistants to help manage areas such as information search, data management, and process automation.
  • Advisory excellence: Robo-advisors eliminate human bias and improve trust and confidence levels by pro­viding consistent, rules-based advisory services at an affordable cost. To improve their job performance, successful human advisors will use virtual assistants to tackle routine administrative tasks and focus on more constructive, creative, and relationship-building activities.
  • Improved operational efficiency: AI-based needs analysis systems allow insurers to not only improve their probability of lead-to-quote conversion but also re­duce turnaround time conversions. AI solutions enable organizations to reduce their manpower requirements and thus benefit from significant savings in overhead costs, especially those associated with routine jobs in the middle and back offices. And with AI systems performing routine activities, em­ployees can focus on skilled tasks, building expertise, and evolving the AI solu­tions.
  • Maximized customer experience: With NLP, speech recognition, and virtual as­sistants, insurers can embrace innovative ways of transforming the cus­tomer experience. With virtual assistants available at various touch points, insur­ers can take their customer service to greater heights by offering contextual and personalized products and solutions.
  • Competitive advantage: Insurers with AI capabilities can position themselves to handle market challenges better than their competitors in the ever-changing insurance business. Insurers can develop a real-time appreciation of prospects’ be­havioral and demographic actions, recognize imperceptible changes in the mar­ket forces that dictate those changes, and forecast optimal responses through better product and business solutions designing.

While it might appear that AI is a cure-all for many sales, service, and risk management conundrums, insurers should follow a series of self-diagnostic steps before embarking on this journey.

  1. Assess readiness. AI decision-makers must spend quality time with their executive teams, peers, and functional business leaders to reflect upon the potential implications of new AI-based products or applications on operating models, products, and operational workflow.
  2. Start small. Given the cultural and risk challenges facing the sector, insurers should start by developing a proof-of-concept model that can safely be tested and adapted in a risk-free environment. Insurers should focus the case for a proof of concept on activities that require agility, automation, and continuous innovation.
  3. Manage change. Because AI capabilities can potentially displace humans, in­surers need an effective and thoughtful HR strategy. Full communication and retraining of affected staff, as well as a focus on building new skill sets and training, will go a long way toward minimizing resistance and encouraging acceptance.

Download the report How Insurers Can Harness Artificial Intelligence.