Putting it all together: New ways of working in insurance

The Intelligent Broker: Part 3

In our last article we explored how data, analytics and Artificial Intelligence (AI) can be used to deepen insights and improve decision making for brokers. In this article, we highlight opportunities for brokers to combine these capabilities, along with automation, to move select parts of their business toward a fully technology-enabled future and launch new business models in the process.

In other parts of financial services, technology-driven disruptions are already occurring. Robo-advisors provide decision support for standardized, low complexity investment and retirement planning strategies, enabling clients to get advice and have their investments managed with minimal, if any, manual intervention. That model is based on algorithms that help clients choose the appropriate investments based on their risk tolerance, time horizon and investment goals. Recommendations are made based on models that incorporate the decisions that investors like them—for example, with a similar profile and risk tolerance—have made in the past.¹

That same principle can be applied to broking, where the primary objective is for brokers to understand their clients’ risks and develop a program tailored to their needs and objectives. That “tailoring” is based on several considerations, many of which can be codified into algorithms that provide recommendations based on public data and client-provided inputs, an understanding of decisions that clients like them have made, and market insights in terms of coverage and pricing details gathered from carrier quotes.

While the application of new automation, data, analytics and AI capabilities has benefits across broking business segments, the resulting broking models will vary. The lower-complexity Small Commercial segment can, in many cases, be fully technology enabled. For those clients, a comprehensive set of automation solutions and data, analytics and AI-enabled decision tools can be implemented. Key interactions include:

• Automated data gathering from 3^rd party data sources, which the client validates and refines
• Assessing risk and recommending coverage based on clients with similar profiles
• Designing a program based on client-provided feedback on their needs, priorities and risk tolerance
• Engaging the market
• Creating submissions and comparing quotes using automation solutions
• Providing decision support to enable clients to choose the policy that best fits their needs based on AI-powered predictive models
• Binding coverage and processing billing using automation solutions
• Reviewing the policy and identifying inconsistencies using Natural Language Processing
• Engaging the client throughout the policy term to flag potential changes in their risk profile that may warrant coverage changes using AI-based risk monitoring solutions

Within that model, the only time manual intervention is required is to handle a scenario that is not (yet) covered by existing solutions. Those exceptions will decrease over time as solutions and models are continuously improved to address additional scenarios.

Risk considerations

For medium and large businesses with more complex and non-standard risks, the broking process is often more bespoke and therefore typically less suitable for a fully technology-enabled solution. However, brokers can still use the new solutions to create capacity for their higher-skilled resources and provide insights that improve decision making. For example, automation and AI solutions can be applied to gather and consolidate data, review policy documents, and even make preliminary coverage and pricing recommendations that the broker can then validate. And AI-powered online risk monitoring solutions can be used to continuously monitor risks and flag scenarios that require broker review. By taking that approach and expanding the solution set over time, the broker is enabled by technology and able to deliver better outcomes to their client.

The role of data and technologies

Many brokers have been constrained in developing these end-to-end solutions due to a lack of data and technology capabilities. Advanced analytics and AI were in their infancy and third-party data sources not readily available. The dynamics have changed and broking is approaching a ‘tipping point’ similar to what the financial advisory industry faced three to five years ago. What’s required for the model to ‘tip’ more fully:

• Availability of more and better data sources
• Carrier receptiveness to automated data exchange solutions
• Continued improvement of AI solutions, with better accuracy and broader applicability

Carriers and brokers are aggressively leveraging AI. Eighty-seven percent of Property & Casualty (P&C) executives agree that AI will work as a co-worker, collaborator and trusted advisor within the next two years.² In addition, 79 percent say AI is advancing faster than their organization’s pace of adoption. And 84 percent of those executives feel AI will mature to make an impact equal to what a human is capable of. Brokers must keep pace with carriers or risk being disrupted.

Impacts and opportunities

What are the broader implications for the future of broking and the intelligent broker? We see three main opportunities for brokers:

1. Build (or buy) capabilities to technology-enable broking for Small Commercial to develop an offering that can compete with emerging direct channel competition.
2. Deploy an expanding solution set to drive efficiency and improved decision making for medium and large businesses.
3. Implement new business models that are AI-native to develop and refine technology-enabled platforms without the constraints or complexity associated with legacy systems.

¹ Accenture Strategy Tech-led change: AI research, 2018

² Accenture Technology Vision "Intelligent Enterprise Unleashed", 2018