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Top 10 Use Cases of Hyperautomation in Insurance (2024)

The traditional insurance industry faces increased pressure from insurtech companies due to the new capabilities they are bringing to the market. 61% of insurers say their top concern about insurtechs is changing customer expectations. This concern is well-founded as 41% of consumers are likely to change their insurers due to a lack of digital capabilities.

In response, insurance companies are speeding up their digital initiatives and the COVID-19 pandemic has been a catalyst for this transformation. The next step in digital transformation is hyperautomation: combining traditional automation technologies such as RPA with intelligent technologies such as AI, process mining, or chatbots to automate as many business processes as possible. According to Gartner, hyperautomation is inevitable and is quickly becoming a condition of survival rather than an option for businesses.

In this article, we’ll explore ten ways hyperautomation can improve insurance processes.

Underwriting

Efficient pricing of the risk is one of the most important factors that can keep insurance companies competitive. The challenge is that underwriting requires handling a great deal of data for better risk assessment, while 85% of customers are dissatisfied with the speed of the underwriting process. Hyperautomation can enable insurance companies to speed up the underwriting process without compromising the accuracy of their risk assessment.

Submission processing

Intelligent automation tools that combine RPA with AI technologies such as natural language processing (NLP) can automate most submission/application processing tasks, including:

  • Collecting data from customers,
  • Extracting data from unstructured documents,
  • Filling company systems with extracted data,
  • Checking incoming data for completeness.

Risk assessment and pricing

Machine learning models and other advanced analytical techniques can significantly improve insurers’ insight into the risk profile of customers. These models can be trained on data from internal and external sources such as third parties, claims histories, or documents provided by customers to predict the risk profile of new submissions.

Using analytical models allows underwriters to price the risk more profitably due to these models’ ability to recognize patterns in large amounts of data better than humans.

Check our article on insurance risk assessment for more.

Policy management

There are lots of manual tasks involved in policy management processes, such as:

  • Issuing policies,
  • Updating policy information for existing policyholders,
  • Renewing policies at the end of the policy period,
  • Adjusting coverage and premium at the renewal,
  • Canceling policies.

AI-powered bots can process documents, emails, phone call transcripts and web forms, classify these documents, extract relevant information, and update company systems. Bots can also notify policyholders about upcoming renewal dates and allow them to renew their policies.

Claims management

Processing claims efficiently is crucial for insurance companies because it affects profitability and customer satisfaction:

  • Claims accounts for nearly 70% of insurance companies’ costs,
  • Around 90% of customers say effective claims processing is a criterion for selecting a provider.

Hyperautomation technologies can automate or augment the entire claims management process:

First notice of loss (FNOL)

Hyperautomation technologies can enable real-time FNOL with technologies such as:

  • Chatbots that can work 24/7 to guide the policyholder to submit information about the incident, such as incident details, police or medical reports, or photos and videos about the incident.
  • Computer vision models to predict the cost of the claim based on visual data provided by the policyholder,
  • IoT such as smart cars, smart homes, or smart health devices that collect real-time data that allow insurers to validate information provided by the policyholder.

Check our article on FNOL for a more detailed account of the technologies that transform the FNOL process.

Claim evaluation and resolution

AI-backed risk models and intelligent document processing tools can process data collected during the FNOL process and enable insurers to resolve many claims without human intervention. For claims that require human intervention, claim adjusters can process the documents collected during the FNOL process with OCR and computer vision technologies. Then, they can determine whether the policy covers the policyholder’s claim and estimate the cost of the claim.

Fraud detection

Insurance fraud costs more than $100 billion annually in the US. This translates into higher premiums to policyholders, negatively affecting both insurance companies and customers.

Insurers can leverage hyperautomation technologies such as:

  • AI/ML models to analyze patterns in policyholder behavior and detect false or exaggerated claims.
  • A combination of chatbots, computer vision, and IoT for immediate and real-time FNOL to prevent policyholders from distorting the reality.
  • Blockchain to prevent double dipping, where policyholders file a claim with multiple insurance companies.

Check our article on insurance fraud detection technologies for more.

Customer services

Personalized insurance services

More than 80% of customers are willing to share more data in return for personalized insurance services. By using real-time data from smart devices and AI models, insurance companies can assess the risk on a more personal level and offer customized services such as peer-to-peer (P2P) and pay-as-you-go (PAYG).

Customer onboarding

During customer onboarding

Responding customer queries

AI-powered chatbots can offer  7/24 customer service, answer FAQs, and provide information about customers’ policy coverage, deductibles, or premiums. Check our article on insurance chatbots for more.

Regulatory compliance

Insurance companies are subjected to strict regulations that can change frequently. Keeping track of these changes manually is error-prone that can cost the business and the customers. To improve compliance processes, insurance companies can leverage technologies such as:

  • Intelligent automation bots for automatic name screening, monitoring regulatory changes, and generating compliance reports,
  • Process mining to assess compliance levels and risks by comparing ideal processes with actual processes.

Further reading

If you have questions about hyperautomation and its applications in the insurance industry, we can help:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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