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In today's rapidly evolving insurance landscape, the integration of Artificial Intelligence (AI) has emerged as a game-changer, and is reshaping the future of the industry. With advancements in technology and the vast amount of data available, AI is empowering insurers to streamline operations, enhance customer experiences, and make more informed decisions.
According to McKinsey & Company, AI is revolutionising and streamlining underwriting processes, claims management, fraud detection, and customer service, among other areas. By leveraging AI algorithms, predictive analytics, and machine learning models, insurers can unlock valuable insights, automate manual tasks, and improve risk assessment. This transformative technology is ushering in a new era of efficiency, profitability, and personalised insurance offerings, paving the way for a brighter future in the insurance industry.
Here are some of the most common ways insurance companies are using AI to better serve their customers and create new efficiencies and how they can potentially expand upon them.
In an increasingly competitive insurance industry, the adoption of AI is essential to stay ahead of the game. AI brings immense value by automating repetitive tasks, enabling insurers to scale operations and improve efficiency. AI is also capable of streamlining claims processing, underwriting, risk assessment, and reducing human error all while saving valuable time. Additionally, AI-powered analytics and machine learning algorithms provide insurers with valuable insights to make data-driven decisions, enhance customer experiences, and develop personalised insurance offerings. As AI’s capabilities expand it will become a strategic imperative for insurers seeking to thrive in an ever evolving market.
Below are some of the most common ways insurance companies are using AI to better serve their customers and create new efficiencies and how they can potentially expand upon them.
AI-powered chatbots have reached a level of sophistication where they can carry on conversations in a very natural and human way. If you have used the chat feature on a website recently, you were likely chatting with a robot and didn’t even notice.
Natural language processing technology allows chatbots to understand and answer complex questions, like the questions that might be asked to complete an insurance quote. Though most of the industry’s current bots will still refer you to a human agent for that. GEICO’s “Kate” can answer fairly basic questions about your account balance and coverage, and help you retrieve documents in the GEICO Mobile app. Liberty Mutual has deployed Alexa Skills that can answer questions about its insurance, provide auto insurance estimates, and connect you to agents.
Allstate’s ABIe is an interesting use case for chatbots, as it is used internally to help walk insurance agents through selling commercial insurance products for the first time. Anticipating a decline in auto insurance policies due to autonomous cars, Allstate agents have begun selling commercial insurance, though most of the agents have no experience in that arena. ABIe (pronounced “Abbie,” short for Allstate Business Insurance expert) walks agents through the commercial selling process. It understands what product an agent is working on, where they are in the process, and who they are. It handles tens of thousands of inquiries a month — just imagine human trainers trying to deal with that!
Chatbots are capable of providing much more advanced customer assistance for insurance customers, and features like touchless claims service are indeed in the pipeline. And it appears that consumers are ready: according to a PointSource survey, preferences for using chatbots with insurance are no different from the preferences of engaging with retail companies. A total of 77 percent of consumers are okay with interacting with chatbots if it means avoiding wait times for customer service representatives with insurance companies, versus 75 percent for retail. However, there may be some resistance to do more with chatbots because the insurance industry model is based on using local, independent agents to write policies and assist customers. Insurers should rightfully be recalcitrant in removing that human touch, but more advanced use of chatbots could free agents to interact more with customers, build relationships, and make sure that the ever-changing needs of their customers are met.
The most common application for smart insurance products is usage-based insurance, which relies on telematics devices and data to assess driver behavior in order to determine the appropriate rate. In English, the little doo-dad you plug in under the dash or the app on the phone can tell whether or not you drive like a hooligan, track your mileage, see where and when you drive, and even if you have been in an accident. Your rate is then based on that information. For example, if you floor it off of every light, slam on the brakes, and powerslide around corners, don’t count on getting a good rate. But if you drive carefully, don’t drive too much, and avoid driving in big cities, you can probably expect a discount.
So instead of your rate being based on actuarial information, it’s based on how you drive. According to NAIC, this can increase affordability for lower-risk drivers, many of whom are also lower-income drivers. It also gives consumers the ability to control their premium costs by incenting them to reduce miles driven and adopt safer driving habits. Fewer miles and safer driving also aid in reducing accidents. The use of telematics also helps insurers more accurately estimate accident damages and reduce fraud by enabling them to analyse the driving data (such as hard braking, speed, and time) during an accident.
Advancements in AI will eventually help insurers use the data of individual drivers more efficiently and accurately to not only determine rates on an individual basis, but to predict the future insurance needs of customers to help agents provide a fully personalised experience for every customer.
While it’s possible that chatbots and AI can one day replace agents and underwriters, today many customers and insurers rely on the phone call to sell policies and assist customers. In the case of acquiring new customers, it takes a combination of online marketing and offline phone conversations to write new policies. Most customers will begin their journey researching insurance companies online, checking out reviews, and getting quotes. But when it comes time to write a policy, many conversions still happen on the phone.
The transition from online to offline poses a major hurdle for insurance marketers, because they can’t tell what happens once the customer picks up the phone. Did they get a quote? Did they sign a policy? Did they decide to keep shopping? What campaign or keyword drove the call? Without this information, marketers cannot accurately retarget and nurture people who have already called. They may be wasting ad dollars retargeting people who converted, or end up suppressing ads for someone who is still shopping. In addition, they can’t optimise their campaigns accurately because they can’t track what happened online that made the customer call. The solution for bridging this online-offline customer journey also lies in AI.
Conversation intelligence platforms like Invoca use AI to analyse phone conversations to understand what the caller intends to do and what the outcome of the call is.
As these caller data points and outcomes are identified, a signal is automatically triggered in Invoca, providing real-time conversion and optimisation data. These signals can then be pushed to the marketing stack (Google Analytics, Google Ads, Adobe Experience Cloud, SFDC, etc.) in real time to make data-based marketing decisions. Marketers can utilise these insights to make smarter decisions on everything from PPC bidding strategy to retargeting.
In addition, this call data can be used to personalise the caller’s experience, which is especially critical when insurers use distributed call centres, whether that be local agents or regional call centres. Calls must be routed to the right agent in the right state and carry the customer’s information with it. Conversation intelligence solutions can not only properly route calls, but attribute conversions from those calls to the appropriate marketing campaigns. It’s a win-win-win for customer experience, marketing, and customer acquisition.
It’s estimated that insurance fraud costs the industry about $80 billion dollars a year in the U.S. alone. Consumers feel the pain from fraud in the form of higher rates and insurers get hit by the cost of sniffing it out and preventing fraud. There is a new sheriff in town to help police insurance fraud, and it’s artificial intelligence.
Insurance fraud runs the gamut from consumers intentionally causing property damage for profit to workers comp fraud and sophisticated schemes perpetrated by organised crime rings. In order to detect potentially fraudulent claims, AI-powered solutions can use data from past claims and known-fraudulent activity. IBM offers a Counter-Fraud Management for Insurance (CFM) solution is designed to help insurers prevent and intercept attempted fraud while detecting, identifying, and building the case against past fraudulent activity and improper payments. Powered by IBM Watson, CFM is able to help insurers detect fraud before it occurs, and more importantly, before they write a check.
Fraud prevention and detection is one of the most logical and potentially lucrative applications for AI in the insurance industry. It has the potential to create huge cost savings for insurers and consumers alike, and it is a function that currently uses an extraordinary amount of labor to accomplish. With AI helping human investigators work on cases, insurance fraud will become increasingly difficult for the bad guys to pull off and reduce one of the most burdensome costs that the industry faces.
When it comes to claims processing, insurance companies are relying heavily on AI capabilities to streamline operations and improve efficiency. By leveraging AI algorithms, predictive analytics, and automation, insurers can expedite the claims intake process, automate routine tasks, and enhance decision-making. AI can also facilitate automated claims prioritisation, fraud detection, and damage assessment. Moreover, AI-powered natural language processing capabilities extract valuable insights from documents, while chatbots and virtual assistants offer personalised customer support. Through these advancements, insurance companies are able to enhance productivity, reduce costs, and deliver a seamless claims experience to their customers.
In today's contact centre environment, ensuring consistent and high-quality customer interactions is crucial. Invoca offers a valuable solution by automating Quality Assurance (QA) for phone conversations, helping contact centre agents hit the right talking points and deliver exceptional customer experiences. As highlighted in our recent blog post "How Customer Care Teams Drive Better Results with AI," this feature enables supervisors to assess calls objectively, identify areas for improvement, and provide targeted coaching.
With its AI-powered conversation analytics, Invoca tracks compliance with scripting guidelines. Additionally, Invoca gives contact centre managers the tools to review each call transcription and recording, and tag agents in comments. With Invoca's automated QA capabilities, contact centres can boost agent performance, ensure consistency in customer interactions, and drive overall customer satisfaction and retention.
The rise of AI is reshaping various industries, including health insurance. A recent innovation is the emergence of AI-powered platforms designed to help patients challenge unfair insurance denials.
One such tool is Claimable, a startup that uses AI to generate appeal letters for patients who have been denied coverage for medications or treatments. By analysing medical records and insurance policies, Claimable's AI can identify potential grounds for appeal and craft persuasive arguments. This technology empowers patients to navigate the often complex and frustrating process of insurance appeals, increasing their chances of successful outcomes.
The potential impact of AI-powered patient advocacy is significant. Many patients are unaware of their rights or lack the resources to fight insurance denials. By automating the appeal process and providing easy-to-use tools, AI can level the playing field between patients and insurance companies. This could lead to better access to care, improved patient outcomes, and greater cost-effectiveness for the healthcare system as a whole.
As AI becomes more integrated into the insurance industry, concerns about transparency and fairness arise. AI algorithms, while powerful, can be complex and difficult to interpret. This lack of transparency can lead to challenges in understanding and contesting decisions made by AI-powered systems, particularly in cases of denied claims. Patients may feel their cases were not reviewed fairly if they cannot comprehend the reasoning behind the decision.
Furthermore, biases in AI algorithms can lead to discriminatory outcomes. If AI systems are trained on biased data, they may perpetuate existing inequities. Insurers must be vigilant in identifying and mitigating potential biases in their AI systems to ensure fair treatment for all customers.
As AI continues to automate various tasks within the insurance industry, the role of human agents is evolving. While AI can handle routine tasks and provide initial customer support, complex issues often require human intervention.
However, the human agent's role is shifting. They are no longer just order-takers or problem-solvers. Instead, they are becoming strategic partners, using AI tools to enhance their decision-making and improve customer experiences. By understanding how AI works and its limitations, human agents can focus on higher-value tasks, such as building relationships, providing personalised advice, and handling complex claims.
The key is to find the right balance between AI and human expertise. By combining the strengths of both, insurers can create a more efficient, effective, and customer-centric operation.
It’s only natural that an industry that relies heavily on data to make financial decisions in so many areas of its business ends up using artificial intelligence solutions. The potential use cases in the insurance industry are manifold, but the speed with which insurers embrace the technology may determine their ability to compete in the near future.
Want to learn more about how Invoca’s AI can help insurance marketers drive more leads at a lower cost? Check out these resources:
