The Intelligent Broker: Part 2

The role of a broker is to assess clients’ coverage needs and negotiate best terms, conditions and pricing with insurers. Now, imagine a world where these answers are at a broker’s fingertips to identify and mitigate the risks clients face immediately. It’s possible when the Intelligent Broker combines the power of human and machine.

In Part 1, we discussed how automation improves processes and creates capacity for resources to focus on more value-added and customer-facing activities. In Part 2, we examine how brokers can enhance business insights to provide more service and offerings. Based on Accenture research, 60 percent of insurance companies have begun making plans to use AI in their organizations. Ten percent are already seeing results—which shows there is significant opportunity to do more with AI.1

Why now?

The amount of data available to brokers has increased dramatically in recent years due to cheaper storage, new third-party data sources and the ability to extract information from unstructured data sources using automated solutions. Many brokers have already transitioned from data warehouses to data lakes to provide access to both structured and unstructured data. Third party sources such as Dun & Bradstreet, Capital IQ, Core Logic, Advisen and ShareThis are providing a deeper level of information to brokers such as demographics, exposures, prior coverage, buying behavior and claims. The culmination of data storage and third-party data access enables brokers to better use analytics for enhanced business insights.



Analytics to date

Brokers have been using analytics for years. Descriptive analytics provide insight into what happened in the past, such as price and coverage benchmarking using “like them” cluster analysis.

Today, winning companies are improving services and offerings through advanced analytics that leverage data inputs and outputs to predict what will happen in the future (predictive analytics) and how actions change an outcome (prescriptive analytics). Using these insights, brokers can identify opportunities such as improving prospecting/cross-sell based on propensity to buy or introduce risk controls that will reduce future losses for their clients.

Analytics and AI go hand-in-hand. Recent Accenture Strategy research found that 43 percent of insurance executives consider analytics to be a critical part of their AI deployments.2 Developing data and analytics moves brokers one step closer to using AI for business insights they are not capable of achieving today.

Adding the power of AI

AI allows machines to have foundational skills of intelligence: sense, comprehend, act and learn. For example, machines can now learn complex tasks such as text analysis, understanding natural language, and adaptive problem solving. When coupled with analytics, AI’s power is exponential. Insurance providers see the value—87 percent agree that the multiplier effect of technologies is a driver of innovation breakthrough.3

With new capabilities, brokers will be in a better position to provide seamless and personalized customer service.

The four foundational skills of AI are sense, comprehend, act and learn.  Without each component, Artificial Intelligence would be incomplete.

AI and the Intelligent Broker

The combined strength of AI and human ingenuity underpins the Intelligent Broker. Brokers will be able to solve complex challenges, develop new products and services, and break into or create new markets. In fact, 79 percent of insurance executives agree that AI will revolutionize the way they interact with customers.4 The Intelligent Broker of the future will:

  • Secure best price: Leveraging external market data to determine market prices
  • Deepen relationships: Using machine learning to gauge propensity to buy and suggest appropriate products to cross-sell
  • Gauge sentiment: Incorporate the Internet of Things to identify potential churn and engage with clients to improve retention
  • Improve loss control: Real-time monitoring of internal and third-party data sources to identify risks to recommend preventive actions to limit and/or eliminate losses
  • Design optimal coverage and program structure: Using machine learning to suggest optimal coverage, limits and deductibles
  • Enhance customer service: Automatic issuance of certificates using virtual assistance

Are you ready for AI?

The industry is waking up to the power of AI: 59 percent of insurance executives are looking for opportunities to use AI to accelerate the introduction of new products and services, while 55 percent are looking to improve customer satisfaction, retention or attraction.5 AI is not just for top brokers; smaller brokers can adopt AI on a smaller, more affordable scale using narrowing applications that focus on one functional area at a time. Larger brokers—who have advanced data and analytics—can assess AI opportunities across the enterprise.

Getting Started:

  1. Identify use cases across the value chain that will address clients’ and employees’ biggest pain points
  2. Develop data-driven models to prioritize use cases with the biggest impact
  3. Pilot and continue to refine approach using agile methodology

Now is the time to combine the power of human and machine to become the Intelligent Broker.

In Part 3, we will discuss how brokers can put automation, data, analytics and AI together to enable new ways of working.

1 Accenture Strategy, Global Consumer Pulse Research, 2017.

2 Ibid.

3 Accenture Technology Vision for Insurance, 2017.

4 Ibid.

5 Accenture Strategy, Tech-led Change for AI research, 2017.

Daphne Estevez

Senior Manager – Accenture Strategy, Insurance

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