More companies in the insurance sector are planning to embrace automation: each of the 500 insurance executives participating in AutoRek’s 2023 survey said they plan to elevate their automation budget in 2024 and beyond.
Advances in technology and decreasing costs have made Robotic Process Automation (RPA) and AI available to both SMBs and big companies, supporting insurance process automation and addressing growing customer demands.
In this article, specialists from Itransition cover the concept of insurance automation, providing an overview of technologies that support automation, sharing major use cases, and highlighting possible automation steps.
Technologies needed
RPA and AI are two essential technologies enabling insurance service providers to implement automation. 19% of respondents to the 2023 Automation in the Insurance Industry survey by OZ are already using them in conjunction to deploy more powerful automation.
RPA
Robotic process automation (RPA) is a transformative technology that employs software robots or bots to automate repetitive, rule-based tasks.
Unlike human workers, RPA solutions can operate around the clock without fatigue or interruption, consistently executing tasks with high accuracy and precision. If complemented with AI, bots can also perform high-value activities and process unstructured data.
AI
Artificial intelligence (AI) is a powerful technology that enables machines to mimic human intelligence and decision-making capabilities.
AI algorithms, as opposed to rule-based systems, can also learn to make more accurate and confident decisions over time. Using AI-powered solutions can improve processes such as risk evaluation, fraud detection, customer interaction, and insurance product and service personalization.
Prime candidates for automation in insurance
The following automation use cases hold the most potential for insurance companies:
Claims processing
Claims processing is a critical activity for any insurance business, which can be too labor-intensive and exhausting as it typically involves a multitude of tasks requiring manual data entry and verification.
For instance, employees often have to manually extract data from customer claims and re-enter it into the claims management system, which is too time-consuming.
By adopting RPA and AI, insurers can streamline this and other tedious claims processing activities with AI-powered RPA bots.
Risk assessment and underwriting
Risk assessment and underwriting are undoubtedly among the most important insurance processes, as they directly impact an insurance firm’s profitability.
If an insurer underestimates the risk, it can end up paying more on insurance claims, which can cause financial losses. Risk overestimation, in turn, can lead to the loss of profitable clients and missed business opportunities, which can also harm an insurer’s financial health.
Underwriters have to analyze vast amounts of data (from insurance applications and financial transactions to public criminal records) to generate risk profiles and estimate risks accurately.
However, manually collecting all this data across multiple disparate sources is too exhausting and time-consuming for underwriters, making it a perfect case for automation.
An insurer can deploy AI-enabled RPA bots to automate the collection of the required data, both structured and unstructured, and thus streamline the underwriting process.
Customer onboarding
Onboarding new clients is one of the most laborious activities for agents and account managers in insurance companies.
A typical onboarding process involves many steps, including processing application forms and documents provided by customers, verifying and validating customer data, and entering this data into a company’s business systems, such as customer relationship management software or policy management tools.
Executing all these routine tasks manually takes up much work time from employees who could otherwise spend it on other important tasks. Insurers can improve traditional onboarding processes by implementing intelligent AI-powered bots.
These bots can collect and process text and images from different documents, including ID cards, application forms, or contracts. In addition, they can validate data automatically and transfer all received information to the destination digital system without human involvement.
Regulatory compliance
Regulatory compliance is a complex process, which can be more efficient if automated. With the help of AI and RPA, it becomes possible to retrieve customer data from internal and external sources for regulatory compliance checks. AI and RPA can also assist with customer data validation.
Moreover, AI-driven RPA solutions can run automated analyses of audit data and provide results to security and compliance teams on an ongoing basis, thus significantly decreasing manual workloads involved in compliance checks.
Adopting automation in insurance
Workflow automation can be challenging for insurers, especially if they have not previously dealt with RPA and AI. We recommend adhering to the following algorithm to ensure smooth adoption:
Detecting automation opportunities
Start with analyzing the existing process landscape to identify workflows that are prime automation targets. Consider manual, repetitive, and rule-based processes with high execution frequency.
Creating a business case and try out the pilot testing
Write a business case for each automation option to define specific pain points, highlight possible solutions, and outline expectations as tangible measurables, be it cost reductions or time saving.
Carry out a Proof-of-Concept (PoC) to validate the feasibility of AI and RPA as well as assess the compatibility of the outlined technology stack with the project requirements.
Adopting technology solutions
Once the automation project is proved to be technically and economically feasible, proceed to full-fledged implementation of RPA and AI technologies. Set up necessary integrations to ensure seamless data flow between RPA and AI platforms and the business’s software systems.
Training and supporting employees
Run training sessions before switching to automated processes to make sure that employees can understand and use the new systems adequately.
To ensure smooth technology adoption within a large company, you can also set up a Center of Excellence (CoE) to coordinate automation programs, streamline knowledge transfer, and foster an automation-driven culture across an organization.
Scaling and optimizing automation
Following successful initial implementations, gradually scale automation across the organization, expanding to new processes and areas as appropriate. Continuously monitor, review, and optimize automated processes to ensure automation delivers the intended benefits.
Final thoughts
The insurance industry can benefit significantly from embracing AI and RPA automation technologies. Insurers can boost efficiency, reduce costs, and improve customer experience by automating repetitive processes like claims processing and customer onboarding.
However, successful implementation requires a strategic approach, including careful process analysis, pilot testing, employee training, and continuous optimization.
Third-party experts proficient in AI and RPA technology can provide strategic guidance and handle technical aspects of a project to help an insurer ensure maximum automation ROI.
