Is AI “Pie In the Sky” For Insurance Distribution?

Globally, insurers contend with numerous distribution challenges — whether it is by virtue of the shift to remote sales networks or from balancing tech-assisted and human sales. Artificial intelligence (AI) is known to create seamless and personalized experiences with solutions ranging from lead prioritization to automated underwriting. Evidently, the most impact from AI, with as much as 80% of value, is expected to be in marketing and sales against 10% in risk management and 3% from gains in operational efficiency.

Last year, more than $200 billion in policy premium was issued in a fully digitized process (quote, bind and issue) in the USA. No doubt impressive, it only represented 15% of the $1.6 trillion in available premium.  Similarly, in Canada, the $9 billion or 12% of insurance premium that was issued fully digitized represented only 1.3% of all policies issued. Current projections estimate that fully digitized distribution will nearly double by 2024.

From multiple surveys, it is evident that majority of customers engage in digital research before buying a policy and will quickly navigate to another provider’s online store to look for better alternatives, if they are not satisfied with what they are offered. This has caused a dip in customer loyalty with increased switching. Leadership in a specific product segment or region no longer suffices and customers are more aware of brands previously outside their line of sight. Market saturation has also caused a decline in  number of new customer acquisitions every year.

These factors are making it imperative for carriers to reimagine and rejig their distribution networks. Instead of only launching a website and waiting for aggregators to bring in customers, insurers need better direct digital channels to drive sales, wherein AI plays a critical role. Digital technologies such as optical character recognition (OCR), machine learning (ML), and natural language processing (NLP) help insurers gain from a customer’s digital behavior. With many prospects researching digital channels, there are troves of customer data that AI engines can leverage and thus empower the distribution channels to make smarter decisions. Key use cases include:

Sophisticated customer segmentation: With only a small customer pool joining new every year, carriers need to attract customers with existing policies. Using third party databases, AI can apply advanced segmentation and use preferences to decide on most effective distribution channels.

Adaptive channel allocation: AI’s machine learning capabilities can learn from customer’s digital behavior, past purchases and interaction history to allocate agents, whether digital or physical. Since many customers continue to value human interactions, AI helps optimize workloads and match human agents with appropriate prospects.

Demand analysis and promotions:  AI can predict demand, aligning demand areas to specific channels. By learning behavior of millennial customers, the AI engine could use correlation with time spent on LinkedIn for this segment, to use targeted ads to distribute a brand-new policy renewal offering.

Apart from gains in fully digitized offerings, broker platforms benefit as well. Generally, brokers thrive with higher closing ratios while providing professional advice, cross-selling and protecting against fraud. With AI, brokers will get more time to focus on delivering a superior customer experience, as documentation and transactional execution processes will become automated.

AI is fast becoming a trend that cannot be disregarded. Businesses expect to benefit from increased revenue and profits, better customer experience and improved decision-making. To take advantage, barriers such as the lack of a clear strategy, talent availability and functional silos have to be addressed. But it’s important to consider that an end-to-end AI vision is wishful thinking. Insurance processes rarely provide a linear process ripe for full automation. Forcing a vision of full-auto AI will be counter-productive. AI cannot fully master current systems. Rather, existing systems and processes should re-align and transform to optimize its usage.

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