Helping organizations engage people and uncover insight from data to shape the products, services and experiences they offer

Learn More

Contact Us


We'll be in touch soon!


Refer back to this favorites tab during today's session for access to your selections.
Refer back to this favorites tab during today's session for access to your selections.x CLOSE


Reimagining the Insurance Workforce: A Vision for the Future (Part 2)


Here's how the combination of crowdsourcing and smart bots can fill the talent gap and forever change the insurance workforce.

Part two of a two-part series

By the end of this decade, the insurance industry will face a serious talent crisis, fueled by retiring baby boomers and millennials’ perceptions of insurance as an unattractive career option.

Using collective intelligence, however, insurers can address the skills deficit while increasing workforce productivity. The key to success lies in dividing larger, complex tasks into smaller chunks of work that can be routed on demand to internal and external employees and robots, which then work in tandem to get work done more effectively.

Collective Intelligence in Action

Here are five scenarios where crowdsourcing and robots can combine forces to yield better business results for insurers:

Screening new applications.

It takes a good deal of time to screen new business applications, particularly in the life insurance and annuities sectors. Humans are much better than bots at completing these manual processes. To increase process efficiency, bots can rely on machine learning algorithms to conduct a first pass on handwritten forms. Data can then be divided into discrete chunks so that individual elements retain needed context. A handful of crowdsourced workers can then verify every single piece of data, and the bot can reassemble the pieces into a complete data set, which can then be fed into the core systems for further processing. Doing so would accelerate turnaround time, reduce labor costs and ensure accuracy.

Assessing property damage.

When natural calamities occur, property damage must be accurately assessed by claims adjusters prior to bringing in emergency assistance services. Robots can be invaluable in these situations, due to their ability to process large volumes of data. At the same time, their image-recognition capabilities are limited. Bots with deep learning abilities can take the first step in capturing aerial or satellite imagery, then slice the images into discrete data chunks. Crowdsourced volunteers can analyze the image chunks and annotate the nature of the damage. As more data sets are fed back to them, bots can learn to better identify significant patterns. The crowd can also help with translating, geocoding and categorizing emergency text messages or tweets. This can dramatically improve crisis management and expedite claims settlement.

Detecting fraudulent claims.

Bots can do a fair job detecting fraudulent patterns in workers’ compensation claims by scanning text data captured from claim forms, witness comments and adjuster notes. Images and videos posted by claimants on social media can be an additional source for identifying fraud. Given bots’ limitations in analyzing images, the crowd can help decipher conflicting patterns. This can help carriers minimize unnecessary payouts.

Shifting to dynamic pricing.

Insurance companies have traditionally relied on historical data to develop actuarial tables. Real-time data captured through crowdsourcing via wearables, mobile devices and social apps, however, can provide valuable insight into consumers’ purchase behaviors, spending patterns, life events, lifestyles and other risk factors. These massive data sets can “train” bots to update their models on an ongoing basis, resulting in fine-grained segmentation and dynamic, non-linear models for predictive analytics. Such training can help improve pricing accuracy and loss predictions while increasing profitability.

Tracking customer sentiments.

Social media and other public forums offer a treasure trove of consumer feedback data. Bots using advanced data mining can gauge customer sentiments on a vast scale, but their accuracy is typically lower than that of trained humans, particularly in analyzing opinions and understanding informal language on social media. Bots can distribute these large data sets to crowdsourced resources to help remove “noise” and facilitate further analysis. Alternatively, if accuracy levels are low, bots can do a “first pass” and then turn to crowd services to add metadata (labeling, prioritizing messages). This helps improve sentiment tracking and provides important input to engage with and retain customers.

Figure 1

Getting Started

Before implementing a collective intelligence workforce, insurers must address quality, behavioral and compliance considerations. For instance, they must understand how work will be divided and assigned to new virtual talent, which tasks should not be outsourced, when to intervene manually, how to understand context and how to ensure regulatory compliance and liability controls.

We advise insurers to take the following actions:

  • Identify low-hanging fruit. Focus on tasks where humans are the primary agent, and the robot supports or augments them. Start with repeatable and rote tasks that don’t demand much from human talent. 

  • Experiment with small pilots in-house. Conduct pilots to increase the effectiveness of existing business processes without disrupting or transforming them completely. For example, outside data scientists can be useful in developing predictive models to analyze the patterns of historical data, better match price to exposure or tailor coverage. 

  • Fail fast and learn quickly. Work sourced from the crowd can be a double-edged sword, equally capable of diminishing the quality of the output or failing to adhere to compliance requirements. Bots must be trained to apply extensive, domain-specific knowledge and contextual data that a carrier has assembled over time to produce positive outcomes. This can help insurers assess whether collective intelligence serves the intended purpose. The success of “trial runs” can also help garner the support needed for wider adoption.

  • Repurpose talent. By embracing collective intelligence, insurers can enable in-house employees to focus on creating new value in the roles they perform, which can lead to higher job satisfaction. Using the crowd and bots, they can also overcome severe talent shortages, augment in-house capabilities, heighten productivity and drive growth.

As the insurance industry works to address its talent crisis in the coming years, collective intelligence will play an integral role — particularly for companies looking to attract and retain a fully engaged, future-ready workforce.

To learn more, please read "Collective Intelligence: Filling the Insurance Talent Gap" or visit our Insurance business unit website.

Related Thinking

Save this article to your folders



Ensuring the Future of Insurance with...

Anybody can “go digital,” but rather than a big-bang approach, leading...

Save View

Save this article to your folders



Drones: Four Ways Insurance Might Benefit

Drones have changed the way we see the world. And with their increased...

Save View

Save this article to your folders



Overcoming the Insurance Talent Gap with...

By combining the skills of internal employees, intelligent machines and...

Save View
Reimagining the Insurance Workforce: A Vision for the Future (Part 2)