The words “artificial intelligence” might bring to mind a futuristic scene where robots rule over society, but in fact artificial intelligence (AI) is already in play in our daily lives in smaller ways today, from virtual assistants that obey spoken commands to smart software powered by machine learning. AI tools have the power to automate processes, increase efficiency, and save time and cost for businesses in every industry—including insurance.
In this article we’ll discuss what AI is and some of the ways it can be implemented within the insurance industry.
What is AI?
It can be hard to know exactly what “AI” refers to in a business context, especially when a number of terms are often used interchangeably. Let’s start by outlining the terminology, including the main types of AI and some other common terms you’ll hear in discussions around the topic.
Artificial Intelligence (AI)
Artificial intelligence (AI) is a broad term encompassing any application that uses computers to complete tasks that traditionally require human intelligence or judgment. It’s usually broken down into 3 overarching categories: Narrow AI, General AI, and Super AI.
Narrow (or weak) AI is the type of AI that’s currently available for application today, and subsequently the type being discussed in this article. It covers technology capable of performing a specific task that requires human-level (or less than human-level) cognition. Narrow AI is programmed to operate in real time within a predefined range, using information from a specified data set. A good example of this would be virtual assistants like Siri, Alexa, or Google Assistant. While it might seem like these technologies are thinking for themselves when you ask them questions, in reality they are simply entering your requests or commands into search engines or applications and retrieving the results.
General (or strong) AI is technology capable of self-directed, human-level cognition. This type of technology is still highly experimental, and most experts agree it has not yet been achieved.
Super AI refers to technology that surpasses human cognition and intelligence. This is the kind of AI you might see depicted in movies, like Skynet from the Terminator movies.
Image Source: Great Learning
Automation
Automation, like artificial intelligence, is a very broad term. It refers to any task that can be set to perform automatically without a human operating it. Usually, the task must be set up within a program where the parameters are specified, and moving forward the task will occur automatically when the specified action triggers it. A marketing automation platform, for example, allows you to send a welcome email automatically every time a new customer is added to the system. While not all automation requires AI, AI is often employed to automate complex tasks intelligently.
Machine Learning (ML)
Machine Learning (ML) is a type of AI that “learns” (or adapts its behavior) as it acquires data—for example, the algorithms that suggest recommendations to you in Amazon, Spotify, or Netflix based on previous purchases or choices you’ve made. This is done through statistical inference and predictive modeling; the software infers a likely output given a set of input data. The more data the technology acquires, the more accurate its predictions will be, hence the “learning” aspect.
Natural Language Processing (NLP)
Natural Language Processing (NLP) is a branch of AI that helps humans interact with computers using natural language like speech or text—for example, your phone’s ability to compose a text that you dictate, or Siri’s ability to respond to speech commands. Another common application of this technology is the use of chatbots, which are programs that can respond to written chat queries using a set of predetermined responses. Check out our blog on chatbots to learn more.
Robotics Process Automation (RPA)
Robotics Process Automation (RPA) uses software bots to perform a selected series of actions on a computer. It’s often used to eliminate manual data entry or transfer, allowing data to be read from one system and entered into another. It can also be used to compile data across systems, generate reports, or complete routine processes like creating or cancelling accounts.
Internet of Things (IoT)
The Internet of Things (IoT) refers to smart devices (other than computers and smartphones) that collect and interpret data. This could include virtual assistants like Google Home and Amazon Echo, wearable fitness technology, smart appliances, home security and surveillance devices, and more. Within the realm of insurance, IoT can help provide real data on customer activity such as driving, health, and home security habits that could impact their policy coverage or premiums.
Applications in Insurance
Now that we’ve defined what AI is and how it can be used in the business world, what are its specific applications in the insurance industry? AI can offer many benefits to agents, carriers, and other participants in the insurance value chain.
Benefits of AI solutions
Saves time and money
AI can handle routine, manual processes more quickly than a person, saving operational costs and freeing up staff to focus on more impactful work with clients.
Provides 24/7 service
AI tools like chatbots can provide 24/7 service to customers, allowing them to receive help outside office hours.
Improves customer experience
AI automates processes, leading to faster results for the customer. It can also aid in creating a more personalized product or experience for the customer.
Increasing accuracy
Machine learning becomes more accurate as more data is acquired, so the results will become better and better as time goes on.
Provides documentation
AI tools keep automatic records of everything they do, creating a documented history to produce reports from or refer to for E&O purposes.
Want to see some specific examples of how AI can revolutionize the way we do business? Here are some of the top ways AI is already being implemented within the insurance industry.
Underwriting
AI can provide additional insight into the underwriting process, extracting relevant data from customers’ social media, reviews, and other online activity to build more personalized policies for consumers based on their individual lifestyle. This can aid in determining the risk factor for a client, and machine learning can compare across vast data sets to more accurately estimate exposure or even use predictive analytics to predict future risk factors or need for policy changes (Agency Nation).
IoT technology has also begun to transform the underwriting process, allowing clients to pay for only the coverage they need based on their actual tracked behaviors. Devices like the Progressive Snapshot, which provides data on how the client drives and offers lower auto premiums for better driving behavior, have been around for some time, but this type of technology is now expanding to other areas as well. See some great examples here, including smart smoke alarms that reduce fire risk and home premiums, wearable fitness devices that affect life insurance coverage and cost, smart doorbells connected to home policies, and even smart toothbrushes tied to dental insurance policies.
Data entry and routine processes
Manual data entry is a tedious and time-consuming part of many insurance processes on both the agency and carrier side. AI applications like RPA can greatly reduce the resources needed for this, completing data entry tasks in 40% of the time and with half the workforce (AIthority). In an industry where data is often siloed in incompatible systems, AI can seamlessly read data from one system and write it into another. For example, HawkSoft’s HawkLink for Google Chrome browser extension autofills client data from the HawkSoft system into carrier portals, raters, or any other website accessed through Chrome.
Routine tasks, like policy creation or cancellation, can be automated with AI as well. On an episode of the Connected Insurance podcast, Chisel AI founder and CEO Ron Glozman describes how his program, initially created to read textbooks, has been customized to read insurance policies and binders. Data points can even be extracted from application submissions to automatically initiate the process with the carrier without human intervention.
Claims
Claims continue to be one of the most manual and labor-intensive processes in the insurance industry, and historically one that significantly impacts customer satisfaction. AI is being employed to combat this in a number of ways:
- Digital image processing to assist damage estimation by comparing against images of similar damage on similar vehicles, read license plate numbers, and flag photos that have been previously submitted as fraudulent (PropertyCasualty360).
- Faster processing by identifying incoming correspondence, indexing metadata, and routing claim files to the appropriate queue for review to provide quick access to adjudicators (Insurance Journal).
- Error tracking, claim verification, and compilation of claims data from multiple systems and sources (The Lab Consulting).
- Self-scheduling for claims appointments and repair work (IA Magazine).
- Automated disbursements to policyholders and vendors (Agency Nation).
24/7 customer service
One of the hottest topics for independent agents around AI right now is chatbots, which use a chat window on a business’s website to answer questions submitted via chat in real time with a set of predetermined responses and actions. Chatbots can greatly reduce the amount of time staff spend answering routine questions, and allow customers to get help 24/7, even when staff isn’t in the office. They’ve been shown to cut operational costs by 30%, and 64% of internet users identified 24-hour service as their best feature (ACT).
Chatbots can be a double-edged sword, however. While a survey by Globant found that 34% of policyholders want to switch to a chatbot if they’ve been on hold with a live agent for five minutes, over 50% also grow frustrated if a chatbot has yet to provide them a clear path to resolution within five minutes (IA Magazine). Policyholders are most open to using chatbots for more routine activities such as policy reminders (36%) or checking the status of a claim (29%), with more complex issues being escalated to a staff member to resolve. Take a look at our blog on chatbots for more information on whether chatbots could be a valuable tool for your agency.
Lead acquisition & customer retention
AI’s ability to extract meaningful data from social media and other online behaviors or interactions makes for more accurate lead scoring, helping agencies to identify their hottest leads and find high-value groups to target.
This can be helpful in customer retention as well. AI-powered analytics systems can give greater insight into customer activities, arming agencies to create more impactful cross-selling and renewal opportunities. Some programs can also predict customer “sentiment,” or how they feel about the agency or their policy, and flag customers you may be at risk of losing. Check out this guest post by Aureus Analytics, an analytics and machine learning platform that integrates with HawkSoft, to learn more about how AI tools can help you use data to grow your agency.
Work smarter, not harder
The potential of AI is limitless, not only within the insurance industry but in the world at large. AI tools can help agencies complete the routine, process-oriented elements of their work more efficiently, leaving them more time to do the important work of growing their business. By embracing the power of AI, agencies can work smarter, not harder, to win the loyalty of the modern consumer.
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Author bio:
HawkSoft
Since 1995, HawkSoft is a leader in management systems for independent insurance agencies that want effective workflows and a delightful experience for staff and policyholders. HawkSoft offers the following promise to insurance agents: your investment in HawkSoft will pay for itself in the first year. Learn more about HawkSoft’s unique father-and-son story here, or request a demo to learn more about our agency management system.
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