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Arthur D. Little

Arthur D. Little has been at the forefront of innovation since 1886. We are an acknowledged thought leader in linking strategy, innovation and transformation in technology-intensive and converging industries. We enable our clients to build innovation capabilities and transform their organizations. ADL is present in the most important business centers around the world. We are proud to serve most of the Fortune 1000 companies, in addition to other leading firms and public sector organizations. For further information, please visit www.adlittle.com

FINNOVATING

Finnovating is a Matching as a service platform that enables X-Techs from all around the word to connect , collaborate and get the funding they need to grow from investors and corporations globally. The Finnovating platform is a space were the key players of the Tech industry can connect together easily and boost global Tech innovations.

SØNR

Sønr is the world’s most comprehensive source of innovation intelligence. It is a subscription platform used by some of the best known insurance companies globally.

It tracks millions of companies around the world and provides insight on the latest market trends, the startups and scaleups reshaping the industry, and intelligence on how other big insurers are innovating.

Sønr includes a suite of tools designed for teams to better collaborate and connect. From recording meetings to capturing and sharing Notes, to being able to track and share activity across the company using Watchlists and CRM boards.

The platform is backed up by a team of consultants, researchers and analysts who support clients in discovering and creating new business opportunities.

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.

 The Digital Insurer reviews Cognizant’s Report on Filling the Insurance Talent Gap

Humans and bots working together toward higher ‘collective intelligence’ is the best use of both bots and humans   

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:

  1. Employee Intelligence
  2. Crowd-Sourced Intelligence (external)
  3. Robotic/Automated Intelligence

Additionally, collective-intelligence tasks can be of the following four types:

  1. Humans (primary) and bots (secondary)
  2. Bots (primary) and humans (secondary)
  3. Humans and bots working together in distinct roles
  4. 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:

  1. Identify low-hanging fruit—that is, areas lending themselves to the ‘collective intelligence’ model (e.g. policy renewal).
  2. Experiment with small in-house projects.
  3. Expect—and, more importantly, learn from—failures.
  4. Communicate the ‘collective intelligence’ model to in-house talent as a means for equipping them with new more valuable skills.

Link to Full Article:: click here

Digital Insurer's Comments

There 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.

Link to Source:: click here

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