The combination of artificial intelligence (AI), robotic processing automation and predictive data analytics is fundamentally redefining how businesses operate, how consumers engage with brands and, indeed, how we go about our daily lives. The field of insurance is no exception. Outlined here are three ways smart technology is affecting insurance, with a focus on identifying lessons learned and defining specific keys to success.
Back Office Robotic Process Automation
The impact of rules-based robotic process automation (RPA) on insurance operations has been well-documented. RPA tools are driving efficiency and productivity gains in generic back-office functions such as F&A and HR, and insurers are tackling processes related to claims administration and account management.
One key challenge is scalability. In many cases, concept initiatives have failed to gain traction, resulting in isolated pockets of automation that yield limited benefit. In others, overly ambitious enterprise-wide projects struggle with boil-the-ocean syndrome. A well-defined center of excellence (CoE) model that develops and documents best practices and then propagates them across different business units has proven effective.
Another critical lesson has been the importance of CIO involvement. This was lacking in many early RPA projects. For one thing, because RPA tools focus on process and business functions rather than programming skills, CIOs often weren’t interested. Business unit heads, moreover, feared that CIO involvement would lead to bureaucratic logjams and derail aggressive adoption schedules. Practice has shown, however, that CIO oversight is essential, to avoid both general shadow IT problems as well as specific interoperability, stability and security issues related to RPA functionality.
See also: Using Technology to Enhance Your Agency
Leading early adopters have also continually pushed the envelope of automation levels. In a claims processing environment, 70% of claims may be simple and straightforward, and therefore ideally suited to an RPA application. At the other end of the spectrum, 5% to 10% of claims are complicated and unusual, and therefore require a human’s expertise and judgment to evaluate. While doable, automating these complex outlier claims isn’t cost-effective. The challenge then becomes to focus on the remaining 20% to 25% of claims. By analyzing the frequency of different types of claims, insurers can identify cases where the time and effort needed to configure a bot will yield a return.
Applying Cognitive Capabilities to RPA
RPA has delivered impressive benefits to insurance operations in terms of cost reduction, accuracy and auditability. That said, the tools are limited to the specific if/then rules they’re configured to follow. If a bot encounters a scenario that doesn’t align with what it’s been taught, it gets stuck.
More advanced cognitive systems apply pattern recognition to analyze unstructured data to identify key words and phrases in context. This promises to take insurance automation to the next level. While an RPA bot can extract a specific piece of data such as a policy number from a specific form, it can’t interpret underwriting rules or aberrations from a form on which data is unstructured and organized differently.
A cognitive application, meanwhile, can scan documents of various types and formats and apply machine logic and learning to identify relevant data in spite of discrepancies in how the data is structured or presented. This allows people to focus on policy/claim exceptions rather than formatting issues. More specifically, by injecting cognitive applications into operational workflows at key “intelligent gates,” insurers can more easily identify aberrations in unstructured data and highlight the policies and claims that require further human involvement.
IoT, AI and Insurance Underwriting
The combination of Internet of Things (IoT) and artificial intelligence will have perhaps the most transformational impact on insurance. By deploying networks of smart, connected IoT sensors, insurers can collect and analyze volumes of data at the point of critical business activity. Leveraging the pattern recognition and predictive analytics powers of AI, meanwhile, creates insights that insurers can use to refine actuarial tables and improve the rules of underwriting.
Consider these examples:
- Sensors in vehicles ranging from commercial trucks to passenger cars monitor and document speed and driver behavior. Insurers can analyze data to calculate accident probabilities of safe vs. risky drivers over time. Based on those calculations, premiums could be adjusted. Smart sensors and cameras can also detect drowsy drivers or erratic behavior, triggering alarms.
- Smart home technology that monitors suspicious activity and automatically shuts off water pumps in the event of a burst pipe can lead to lower homeowner policy costs, particularly for premium coverage such as insuring valuable artwork from theft and damage.
- Pharmacies that store and transport medicines can deploy temperature and humidity monitors to ensure that supplies stay within required guidelines. Reducing the risk of tainted medicine reaching consumers could reduce liability risk.
- Smart video analytics can determine wear and tear of roofs, oil pipe damage from foliage and animal migration and levels of water and soil contamination. Such insights enable corrective action before catastrophes strike and reduce the level of unforeseen risk for underwriters.
- By monitoring pressure or fluid flow in an oil pipeline, sensors can trigger shut-off valves if limits are exceeded, thereby preventing costly environmental damage.
