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Maximizing ROI with Insurance Automation
Aditya Kathpalia
Aditya Kathpalia Posted on Jul 10, 2024   |  5 Min Read

Amid a highly dynamic and competitive landscape, insurers face numerous and unique challenges, which directly impacts their return on the capital cost. Evolving policyholder expectations, rising labor costs, strict regulations and compliances of insurance industry and ever-increasing pressure from digitally driven insuretechs mixed is sufficient to make insurers break into a sweat. Keeping the intensity and volume of data generated and mundane nature of some of the workflows in various insurance operations in mind, many thinkers assumed insurers would turn swiftly towards automation and intelligent automation to improve efficiency of their core processes while saving costs. However, most of the insurers have faced the challenge of maximizing RoI from automation projects across the globe have been bogged down the traditional automation technologies including robotic process automation (RPA) and its incapability to manage the diverse variables and complex processes of the industry.

Insurance Automation Challenges

Enters Natural Language Processing (NLP) models including LLMs or large language models! The sudden yet sustained rise of these models has facilitated insurers to deploy advanced automation tools extensively across the insurance value chain to reduce the manual efforts as well as costs associated with front and back-office operations. Not only this, NLP-powered automation has augmented employee productivity by shifting their focus toward more strategic and value-generating activities and tasks humans can do better. The cumulative impact has positively impacted customer experience as more employee effort and time is being dedicated to customer engagement and providing better and personalized products that match customers’ needs.

But the question remains unanswered.

Why Haven’t Insurers Maximized Their Returns from Automation on Time?

Now let’s think of processes where automation gives the greatest return on investments; usually, these are the processes that have high volumes of data, multiple applications, involves intensive manual efforts, strict service-level agreements (SLAs) and grave fines for any errors. Going with this definition, insurance processes should be the ideal match for AI adoption and automation, yet there is a definite struggle for adoption of these technologies on a wider scale. Why? For one, insurers have always been cautious, which makes them skeptical. But this alone fails to justify the lack of enthusiasm of insurers toward new-age technologies such as AI and automation to address their challenges and in the environment where other conservative verticals such as logistics and manufacturing are leveraging artificial intelligence to its full. Rather, the reason behind insurers’ skepticism and the slow speed of automation of the insurance industry has conceived from the limited capabilities of the traditional automation tools like RPA. Now any RPA system requires:

  • Well-defined processes
  • Pre-established variations on the standard processes
  • A dedicated team of highly skilled RPA developers
  • Constant and seamless collaboration between RPA developers and technical experts such as claims agents

Unfortunately, these criteria prevented several processes from being considered fit for automation, especially claims. Usually, a typical claim processing involves a series of diverse process “paths” dependent on the unique situations. In the process terminology, these process “paths” are known as “exceptions”.

Now, the traditional automation techniques are incapable of handling the complexity of these situations or managing the different edge cases that agents can easily work on. Adding to the layer of complexity is the need of an extensive team of RPA developers, highly skilled and paid individuals who pick a specific process and convert it into a programming language for automation to understand. Several exceptions in insurance operation such as underwriting, claims processing, customer onboarding would simply require these RPA developers to constantly program each edge case separately. A decade ago, with such complexity, outsourcing these simpler processes to automation service providers made sense, though it left insurers in a dilemma. The outsourcing labor costs surpassed the premiums. Moreover, by outsourcing these data-intensive processes, insurers were unable to leverage the data or otherwise generated insights when these processes are recorded digitally. But thankfully, new automation and artificial intelligence reached the scene as saviors which are being leveraged by insurance automation service providers to truly automate the business processes!

Giving the Steering Wheel to Subject Matter Experts

Advancements in natural language processing (NLP) and large learning models including ChatGPT has addressed a major issue with automation: the need for an extensive and high salaried team of developers. These automation systems have NLP at their core which makes the development and management of the most complex and highly variable components of automation easy and accessible to the insurance subject matter experts. This alone has eliminated several issues that insurance operations usually encounter.

1. Automation of exception-intensive Insurance processes

It is the subject matter experts like brokers, customer service agents, and underwriters who know the operations inside out. In case of any unprecedented event, there are chances they have faced similar situation before. Since automation is now in the simple conversational English, rather in any programming language, new technologies support subject matter experts to deal with such situations simply by conversing with AI-powered tools. And this isn’t all. Now automation can be applied to most complex processes with diverse variability in rules or documents (insurance claims), further ensuring reduced overheads and improved revenue through shifting employee efforts on more strategic tasks.

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2. Leveraging the experience and industry expertise of veteran workforce as an enriching data source

Amid a data-powered economy, in-depth understanding of business processes can provide unparalleled efficiency gains to re-think, re-design and improve the existing processes. And the same applies to the insurance industry! However, the sheer vastness of incidents and scenarios involved in covering the insurance business, capturing an accurate and comprehensive view into the business operations would be simply too expensive when compared to the RoI. However, by leveraging NLP-driven automation, the automated tasks and SMEs’ responses to their exceptions are digitally recorded in the form of a journal. This data can be further utilized to run prompt-based queries through LLMs and can help executives get answers to the most complex queries about their business operations, get insights into market trends, and make highly informed business decisions. It is worth noticing that it’s the point where old industry players have the data advantage as compared to the new ones. The sheer process data volume, if captured accurately, can provide market data at a scale that new players can’t achieve yet.

3. Shifting the focus of front-office staff on strengthening customer relationships

As compared to other industries, there has been a gradual decrease in the CSAT score in the insurance industry. The reason behind this decline is delays in claims resolution, frustrating claims creation, and the general feeling of a customer being just another number. However, NLP-powered automation systems are addressing these challenges. For instance, an insurance contact center witnessed a reduction in average handle time (AHT) from 513 seconds to mere 209 seconds simply by automating the processes of account lookup, voice to text note taking, and primary actions. With such extensive automation, insurers can guide their workforce to focus on customers, provide personalized products and care based on customers’ needs, develop lasting trust, and above all, improve the entire experience for both customers and employees.

Concluding Thoughts

The new version of automation, powered by NLP offers a rich and unique opportunity for an industry coping with extensive overheads, poor customer experience, strict compliance and regulations, and new competition. NLP-based automation has broken down the shackles of traditional limitations that were binding the legacy automation systems. With artificial intelligence and automation available to insurance subject matter experts in the most accessible and easy manner, the complex, variable intense processes can be easily automated to improve productivity. As more and more processes are automated, the variable overheads will decline significantly in this data-intense, high-volume industry, market trends will be leveraged efficiently, and an improved customer experience will be delivered to drive sustained growth with more cross-selling and up-selling opportunities.

Accelerate Insurance Automation Initiatives with Natural Language Processing