Filling the Insurance Talent Gap – Cognizant Whitepaper
Article Synopsis :
With 25% of the US insurance workforce expected to retire by 2018, the industry faces a formidable resourcing challenge.
Cognizant’s ”Collective Intelligence – Filling the Insurance Talent Gap” is of the mind that the coming turnover may actually be a good thing. Combining the skills of full-time employees, intelligent machines, and specialized external resources, carriers can not only overcome talent deficits, but reduce overhead, maintain compliance, and bolster overall performance.
According to the paper, ‘collective intelligence’ design is comprised of three elements:
- Employee Intelligence
- Crowd-Sourced Intelligence (external)
- Robotic/Automated Intelligence
Additionally, collective-intelligence tasks can be of the following four types:
- Humans (primary) and bots (secondary)
- Bots (primary) and humans (secondary)
- Humans and bots working together in distinct roles
- Humans and bots working together and sharing the workload
Crowdsourcing and Automated Intelligence are keys to harnessing the power of this model. The two are defined below:
Crowdsourcing: A flexible working model which helps in eliminating talent gaps by sourcing talent from outside. Additionally, it draws fresh ideas and boosts innovation.
Robotics/Automated Intelligence: The automation of manual tasks, saving time and cost. Additionally, it can eliminate processing errors and improve levels of customer engagement.
Robots can be deployed to augment the capabilities of in-house talent, boosting overall process productivity. The future may indeed include an array of Robo-Analysts, Robo-Assistants and Robo-Specialists lending a hand in front- and back-office support. Any and all things repetitive can be handled by a bot.
Collective intelligence can be leveraged for things such as screening new applicants, assessing property damage, detecting fraudulent claims, providing customized pricing, and tracking customer sentiments.
With so many workers retiring, the practical solution to bridging the talent gap is to divide complex tasks into simpler ones and distribute them to “crowds and bots” working in tandem with in-house employees.
There are admittedly several challenges with this model, such as work quality, regulatory compliance, and the bots’ lack of empathy/emotion in dealing with primarily human customers.
The report issues the following recommendations for firms wishing to explore a ‘collective intelligence’ model:
- Identify low-hanging fruit—that is, areas lending themselves to the ‘collective intelligence’ model (e.g. policy renewal).
- Experiment with small in-house projects.
- Expect—and, more importantly, learn from—failures.
- Communicate the ‘collective intelligence’ model to in-house talent as a means for equipping them with new more valuable skills.
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Digital Insurer's CommentsThere is growing fear of machines taking over jobs in the insurance industry. But the reality is machines will fill jobs humans are either unwilling or unprepared to fill.
We agree with this paper’s thinking, that the human/machine debate isn’t really an either/or proposition; insurers (and companies in other industries) will win by blend humans and machines in an artful mix both humans and machines will find attractive (yes, machines will buy insurance in the future, and in some cases already are).
The hiring and training of good people will never go out of style. But for certain repetitive-task jobs, in future, you’ll want to hire and train a bot.
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