According to Accenture, underwriters spend around 40% of their time on low-level tasks, such as manual data entry. That’s why insurance companies increasingly turn to automation technologies to streamline their routing operations and free up their specialists for more complex assignments.
In particular, robotic process automation in insurance has proved effective for handling many routine processes, from claims processing to insurance risk assessment. In this article, we delve deeper into RPA capabilities and benefits for streamlining insurance risk assessment and facilitating other insurance providers’ operations.
Traditional approach to insurance risk assessment
Insurance actuaries use statistical models to analyze historical data, including demographic data, economic indicators, health and mortality data, information on road accidents in the area, as well as climate data to elaborate insurance policies based on how likely people would potentially make claims. They also evaluate the likelihood and severity of risks to people’s lives, health, and material assets.
Underwriters use these findings when they create a customer’s risk profile. For this purpose, they also analyze the information provided by applicants, such as personal data, health records, workplace conditions, or details about their assets. In addition, they can consider an individual’s claim or credit history. Based on the risk profile, insurers decide whether to approve or deny coverage.
A customer’s risk profile also influences coverage conditions and premium rates. Applicants with higher perceived risk face higher deductibles and premium rates. Besides, some hazards can be excluded or limited in coverage. For example, in case of property in earthquake-prone zones, insurers can provide separate earthquake insurance, while the standard insurance policy will exclude coverage for earthquake-related damages.
Traditional risk evaluation often relies heavily on manual processes. This time-consuming assessment requires significant human resources, resulting in higher operational costs. Insurers can also struggle to handle big volumes of data or get comprehensive information to create a precise risk profile. Moreover, manual data processing and entry are susceptible to human errors, leading to incomplete or inaccurate data.
How RPA can help in insurance risk assessment?
Robotic process automation means utilizing software robots, or RPA bots, to automate repetitive operations. By mimicking human actions, these bots can navigate through large data volumes and perform various tasks. While they lack human or AI analytical capabilities, they excel at executing operations based on explicit predefined rules.
The foundation for efficient decision-making in insurance risk assessment lies in accurate and comprehensive data gathering and processing. This is where RPA proves helpful, improving the time-consuming and error-prone traditional risk assessment processes.
Data extraction
RPA bots are programmed to gather data from various sources. They сan use application forms submitted by customers, existing customer records, and external sources, such as property, medical, government, weather and environmental databases or social media. These software robots can navigate through different interfaces and document formats. Notably, they can elicit valuable insights from unstructured sources, such as emails exchanged between insurers and their customers.
Utilizing RPA, insurers can quickly get comprehensive information to create the insured party’s risk profile. For example, to evaluate property risks, bots will combine historical data on related claims with external data on property conditions, including property inspection reports, weather in the area, and neighborhood characteristics. The analysis of this information can reveal a pattern where certain types of claims, such as water damage, are more frequent in properties with specific conditions, like proximity to flood-prone areas.
Data validation
RPA bots perform validation checks by employing programmed rules and logic to assess the accuracy and completeness of gathered data. In particular, bots use pattern matching to validate data formats and perform comparisons and range checks to see if entered numerical values fall within predefined ranges or thresholds.
For example, in the case of car insurance, bots can compare the car’s identification number against the expected structure of a valid number. They will also check if the vehicle cost falls within an expected price range. In the case of an error, a bot logs its type and the time it occurred. After that, the bot sends a notification to relevant risk managers or underwriters, requiring a manual check.
As a result, only accurate data is used in subsequent operations, making it easier to process a particular insurance claim.
In addition, RPA can be used for cross-reference checks. For example, to confirm the accuracy of an applicant’s claims history, software bots query an external claims database. They can identify the number and the types of claims filed, their frequency, and their outcomes. If there are some red flags, such as frequent or large claims, bots may trigger a manual review.
Data processing
Robotic process automation helps efficiently sort the retrieved information and eliminate data redundancy. As a result, insurers get a well-organized and categorized dataset for more profound analysis and decision-making.
For example, RPA bots can systematically sort the claims data based on predefined parameters, such as claim type and submission data and the severity of damage. If two claims with identical details are identified, the RPA system flags one as redundant, ensuring that only unique and non-duplicated data is processed further. Moreover, RPA bots categorize the claims into specific groups based on predefined criteria, such as the cause of the incident (e.g., natural disasters, theft, or road accidents).
In addition, RPA can dynamically adjust categories based on changing circumstances or emerging risk factors. For instance, if there’s a surge in claims related to severe weather events, the RPA system can adapt its categorization, introducing a new group, “Extreme weather risks.” This feature allows for targeted evaluation of the unique characteristics of these new risk factors.
Consequently, insurers can develop or modify coverage options to ensure that policyholders have adequate protection against emerging threats. Or, based on the severity and likelihood of the hazards, an insurance company can adjust premium rates for affected policies.
Reporting and communication
RPA enables the creation of customized reports according to predefined criteria. For instance, bots can send a report focusing on a particular type of risk for risk managers or customer risk profile reports for underwriters. Such messages can be issued according to a set schedule or in response to specific triggers.
Additionally, RPA bots can be programmed to monitor critical indicators and send out automated alerts and notifications when predefined thresholds are breached. For instance, bots can spot a surge in claims for a particular coverage within a certain period. In this case, a bot alerts insurers, sending them a notification with all the relevant information, e.g., type of claim, affected policyholders, and geographic areas impacted.
Final thoughts
RPA allows insurance professionals to shift their focus from routine tasks to more strategic aspects of insurance risk assessment. What’s more, this technology facilitates handling large volumes of data, providing a comprehensive and real-time view for more informed risk evaluation. Overall, reduced manual effort and fewer errors translate into an accelerated and cost-effective insurance risk assessment process.
