This is the first of a two-part series.
The insurance industry faces a formidable challenge: 25% of its workforce is expected to retire by 2018. One way for human resource executives to address this sizable problem is through “collective intelligence.”
By definition, collective intelligence combines the power of human capital (internally and externally sourced) with automated capabilities, or software “bots.” Together, humans and “bots” work in distinct but cooperative roles.
Through collective intelligence, insurers can resolve complex business challenges, perform specialized functions in less time and create predictive models that precisely classify risks. Assigning machines to handle repetitive, high-volume tasks can heighten overall productivity while accommodating heavier workloads — all at a fraction of the cost (and time) it would take full-time employees to perform these functions.
One Part of the Equation: Crowdsourcing
A recent Oxford Economics report found that a hefty 83% of executives already rely on consultants, intermittent employees or contingent workers to meet market demand. (For more, read “Jumping on the Gig Economy.”) For insurers, crowdsourcing talent is driven by four objectives:
Overcome skill shortages. In addition to the high retirement rate among insurance workers, the Bureau of Labor Statistics reports that less than a third of insurance employees are under the age of 35. According to the Insurance Information Institute, there will be at least 400,000 open positions by 2020. Crowdsourcing could help resolve this gap.
Develop new products and services. What’s more, crowdsourcing can be an extremely effective way to experiment with new solutions. For instance, the Monetary Authority of Singapore has invited fintech firms worldwide submit ideas and design solutions for 100 problem statements that were originally crowdsourced from the general public. Insurers could also benefit from this approach.
Solve complex challenges at lower cost. Members of the crowd can perform specialized tasks, such as predicting the factors that influence policyholder decisions. Insurers that successfully employ crowdsourcing and other flexible working models can position themselves at the forefront of innovation. For example, Kaggle, a platform for predictive modeling and analytics that was recently acquired by Google, conducts contests to source predictive models from data scientists worldwide. Similarly, insurers such as Allstate, State Farm, Liberty Mutual and Prudential have used crowdsourcing to develop predictive models for accurately classifying risk, tailoring coverage and predicting customer purchase patterns.
Create a better value proposition. Lastly, crowdsourcing can rapidly generate real-time data that can be shared across the crowd. This newly available data could help insurers and customers avoid hazards, prevent accidents, manage crises and reduce insurance costs.
Bring In the Bots
Filling the talent gap will also require automated intelligence in the form of machines that “do,” “think” and “learn.” (For more on this topic, see our Cognizanti article “Intelligent Automation: Where We Stand and Where We’re Going.”) To get the most out of automated intelligence, insurers should ask themselves the following questions:
Can automated rules-driven processes lead to bottom-line improvements? Robotic process automation (RPA) works best for insurance processes that are repetitive and rules-driven. Full-time equivalents (FTE) can be reduced significantly, and savings can be realized across front- and back-office functions. For instance, our OptimaWrite/Intake automates data extraction and validation tasks, eliminating manual processes, streamlining submission intake from all document types and formats, and automatically prioritizing insurance-related tasks. In fact, users have reported a 50% to 80% reduction in turnaround time, 60% reduction in costs and 92% accuracy in data extraction.
Can automation generate more value by applying advanced cognitive capabilities? The potential benefits of using artificial intelligence and cognitive computing extend far beyond cost reduction. These capabilities can streamline risk selection, improve decision making, drive better outcomes and provide a platform for developing innovative offerings that increase market share and add brand value. For example, Swiss Re is improving how it assesses life insurance risk by engaging Watson, IBM’s artificial intelligence system.
Can automation improve customer engagement through chatbots and robo-advisors? Having taken the wealth management industry by storm, robo-advisors have now staked their claim in the insurance field. These smart machines can simulate human behavior to attract customers, or function as an automated advisor to guide clients. Another trend is to engage customers via chatbots, which provide a digital chat experience that replicates a human conversation. Insurers now sell policies through mobile chat applications — eliminating the need for associates to fill out long forms and providing customers with contextual advice on products and services.
All told, “robo employees” can augment the talent of in-house personnel and significantly increase their productivity. When coupled with crowdsourced human capital, they can become an integral part of the insurance workforce.
Instead of viewing crowdsourcing and robotic intelligence as separate entities, companies should keep in mind Aristotle’s saying, “The sum is greater than the whole of its parts.” In part two of this series, we’ll examine specific scenarios where crowdsourcing and robots can combine forces to yield better results for insurers, as well as tips on how to get started.
To learn more, please read “Collective Intelligence: Filling the Insurance Talent Gap,” or visit our Insurance business unit website.