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How Insurers Can Harness Artificial Intelligence

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Article Synopsis :

Artificial intelligence (AI) holds vast potential for insurers interested in reinventing their business models and transforming customer experience. This report from Cognizant looks at the opportunities and challenges related to scale adoption of AI.

 The Digital Insurer reviews Cognizant’s Report on How Insurers Can Harness Artificial Intelligence

AI – your new ‘collarless’ workforce

The report opens with an examination of the evolution of AI, from systems that do, to systems that think (e.g., big data, social listening), to systems that learn (e.g., dynamic underwriting, virtual assistants, robo-advisors). Based on the trends, the report recommends insurers adopt AI in two stages:

Stage 1: Use AI to assist human workers rather than displacing them, particularly in two areas:

  • Underwriting. AI systems can be used to perform research, aggregate, refine and present required information to underwriters, allowing them to focus on core underwriting activities.
  • Advisory services. Virtual assistants can manage the low-value activities of advisors, such as lead management, scheduling, planning, licensing, etc., enabling them to focus on building skills and providing value-added services.

Stage 2: With AI making inroads in the insurance industry and the peripheral systems elevated to support AI, insurers can evaluate pilot programs that aim to turn underwriting claims into dynamic self-learning models. Transforming the customer experience through virtual assistants, robo-advisory, robo-contact centers and chat bots can be explored in the next five to ten years.

In the context of AI, the report groups insurers in three buckets:

The Foundationally Intelligent Insurer: Insurers at this stage rely heavily on traditional processes and legacy systems. Most have a limited online and media social presence and tend to resist major organizational change. As a result, the major imperatives for these insurers to gain a solid foothold in the market include:

  • Attaining rapid growth through channel and touchpoint expansion
  • Employing operational intelligence to optimize resource overhead and improve customer experience
  • Modernizing technology to solve legacy problems such as scalability, service turnaround times, etc.

The Incrementally Intelligent Insurer: Insurers at this stage have embarked on the digital journey and have improved engagement with distribution partners, customers and internal stakeholders. Such insurers have invested heavily in IT solutions that enable “pull” marketing techniques rather than just “push” approaches. Targeted marketing, gamification, a social media presence, mobile solutions and analytics are the key themes within the organization at this stage. Insurers in this category are typically characterized by:

  • Knowledge of customers’ needs and behaviours
  • Multiple channels and touchpoints of sales and service, which introduce new challenges for enabling the customer experience

The Institutionally Intelligent Insurer: Insurers at this stage are at the forefront of the insurance industry, employing technology to effectively solve business problems. These businesses have advanced point-of-sale capabilities, straight-through processing (STP) functionality and a single view of the customer across all channels, among other characteristics. The most important techno-business levers of insurers at this level include:

  • Improving the customer experience at every touchpoint and channel
  • Discovering possible routes of up- and cross-sell through better customer data and interaction mining
  • Improving operations through intelligent decisions across manually intensive processes such as underwriting, claims management, etc.

Per the report, five main challenges often undermine planning and implementation of winning AI projects:

Building a Strong Foundation: For AI platforms to solve business problems, they need to be exposed to huge volumes of domain-specific information covering all possible business scenarios. The success of an AI solution largely depends on the continuous learning it creates from every single business transaction or interaction it makes. The challenge for an insurer is to ensure the availability and accuracy of information to be fed into AI-based solutions.

The Glitch Risk: Technologies such as speech recognition and machine learning require human oversight for their work to equate with human capabilities. Voice recognition systems require painstaking training and only work well with controlled vocabularies. Diverse accents, background noise and distinctions between homophones (such as “buy” and “by”) all pose challenges, as does the speed of natural speech. AI systems also introduce the risk of unpredictable and bizarre errors that are not easily resolved through root cause analysis.

Stakeholder Readiness:  Are employees ready to work with AI? Are customers? Both need to be measured and studied pre-launch.

Privacy Concerns and Regulatory Hurdles: Since AI solutions require every interaction/transaction to be recorded for machine learning, the insurance industry will have to battle data privacy concerns since most AI solutions are likely to reside on the cloud of a third-party technology provider. Insurers will need to ensure that information security and data privacy policies, procedures, methods and tools are employed to protect data from breach or unintended use.

Technology Refresh:  Implementing AI requires a good deal of supporting technology and maturity of the insurer technology landscape. Among the attributes insurers need to evaluate are the existing state of the technology landscape, level of integration between various systems and applications, state of process digitization, and availability of data from various sources. Building such infrastructure and enabling integration with AI solutions requires both time and cost considerations.

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Digital Insurer's Comments

New AI-as-a-Service tools available in public clouds massively simplify AI for commercial use. Bots and digital assistants, APIs, machine learning frameworks, and fully managed machine learning services put powerful AI capabilities in the hands of everyday developers. Talk about a game changer.

Traditional software does only what it’s told to do; AI software learns by doing. Incorporating AI – your new ‘collarless’ workforce – in key operational layers should be a top priority. 

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