The Intelligent Broker: Part 1

It’s hard to miss the headlines—the robots are coming, data related to our daily activities is being used to tailor the products and services we are offered, and artificial intelligence (AI)-powered models will soon be better than humans at analyzing patterns and predicting outcomes. It is no surprise that Accenture research finds 82 percent of property & casualty (P&C) insurance carriers agree that their organizations need to innovate at an increasingly rapid pace just to maintain a competitive edge.1

Within insurance, much of the media attention is focused on carriers. We believe there is just as much, if not more, opportunity to apply new technologies and solutions to transform broking. The Intelligent Broker is here, and existing broking processes and capabilities must evolve for brokers to remain competitive and create new opportunities for innovation and growth.

The Intelligent Broker brings together human and machine capabilities through three key components:

  1. Automation of manual data extraction, entry and processing tasks
  2. Data and analytics to enable new insights and improve decision making
  3. AI to enable the automation of more complex tasks and make the insights and decision support that analytics provides even more powerful

In this first article, we focus on how brokers can use automation to improve processes and create capacity for resources to focus on more value-added and customer-facing activities.

From manual to insightful

Automation, which includes robotics process automation (RPA), involves using software to mimic the actions of a human without having to alter the underlying systems. Today’s automation solutions have gotten more intelligent. They can be applied in combination with optical character recognition (OCR) and more advanced natural language processing (NLP) solutions to extract data from forms or e-mails, execute manual data entry into systems or templates, and complete rule-based manual processes in core broking administration or billing systems.

Given the variation within broking, where individual carriers typically have their own forms and templates, technology needs to be flexible. A recent Accenture survey of executives across the insurance industry indicated that 93 percent plan to invest in RPA by 2020 and nearly 97 percent plan to invest in NLP.2 The intelligence of today’s automation solutions has opened the door for even more use cases. Brokers should be exploring potential applications within their business.

Key activities that brokers can target for automation include:

  • Creating submissions by populating exposure gathering workbooks and gathering and formatting loss runs
  • Extracting information from carrier quotes and moving that data into broker administration systems, quote comparison templates, and proposal documents
  • Populating and issuing certificates of insurance
  • Indexing and assigning client and carrier correspondence
  • Performing reconciliations in payment systems

By automating manual work, brokers can reduce the time and effort spent on non-value-added tasks and create capacity for their workforce. That capacity can be used to focus on activities that drive growth or can be dropped to the bottom line as savings.

Another benefit of automation is the ability to improve the customer experience. Accenture Strategy research revealed that 82 percent of consumers consider the amount of time it takes to completely resolve an issue or problem to be an important driver of their customer service experience.3 Since automated processes typically have reduced turnaround time, brokers can respond to customers more quickly. In addition, automation can improve quality by reducing error and omission exposures associated with incorrectly keyed information.

Until recently, the lack of digitized and structured data limited the application of RPA. For many brokers, the information required for processing is on paper-based or scanned forms and in non-standard and unstructured formats like e-mail and Excel files. How are things different now? New solutions are available to enable the automated extraction of data. Advanced OCR solutions that leverage machine learning algorithms can read hand-written text with high accuracy and NLP solutions can extract information from unstructured sources such as e-mails and attachments. Carriers and other industries have already begun using these tools, and brokers should look for opportunities to apply them as well.

Getting started with automation

Successful programs start by identifying high-impact opportunities that demonstrate early success and scaling from there. This involves four steps:

  1. Identify high-volume and rule-based tasks where data is being extracted, entered, or processed through an automation assessment
  2. Prioritize a use case to design, build and pilot an automation solution for a targeted line of business or segment within a geography
  3. Learn from the pilot prior to scaling the solution across additional geographies, lines of business and segments
  4. Expand the program to address additional use cases prioritized by the business

In Part 2 of the series, we’ll explore how brokers can make better decisions using data, analytics and AI to improve coverage and program structures for customers, and drive growth for their business.

1Accenture 2018 Technology Vision research.

2Accenture 2017 Technology Vision research.

3Accenture Strategy 2017 Global Consumer Pulse Research.

Jeff Mitch

Managing Director – Accenture Strategy, Insurance


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