Artificial Intelligence (AI) is making waves across multiple industries, and insurance is no exception. A sector once reliant on human judgment and manual workload is rapidly redefining its practices. The integration of AI in insurance has changed how companies assess risk, process claims, and interact with customers. But what exactly are those changes? Keep reading to learn how AI introduces extra efficiency into the insurance sector!
As of today, AI is being used heavily in the insurance industry. The reasons mainly relate to enhancing processes like automation, quicker decision-making, and personalized service. Technologies like machine learning, data analytics, and natural language processes drive these enhancements. As AI can study large amounts of data swiftly, insurance companies have been quick to adopt this tech and apply it to:
AI applications in insurance are primarily focused on enhancing efficiency. Companies using AI have been recognized for superior data handling and improving performance above industry standards. Let’s take a look at exactly where AI is making a difference.
Traditional risk assessment methods rely on historical data and human expertise, but AI can process a broader range of factors. AI’s massive advantage to insurers here is the analysis of real-time data 24/7, which helps predict risks. But how exactly does this happen?
The process begins with AI algorithms sifting through customer information, behavioral data, and external factors like economic conditions or climate patterns. AI then assesses the probability of specific risks, like the likelihood of a customer filing a claim or the potential for suspicious activity.
Use Case: Lemonade is an insurance company that uses AI to assess risk and determine premiums. Its AI-driven platform analyzes data from various sources to offer personalized insurance rates.
AI streamlines claim processing by automating much of the workflow, reducing the time it takes to handle claims, and improving accuracy. In a typical process, AI can help with initial claim intakes, such as collecting and verifying information and making decisions on straightforward claims.
The process usually starts with the customer submitting a claim online or through an app. AI tools then automatically validate the claim by cross-referencing it with policy details, previous claims, and external data like repair costs. AI can assist human adjusters in more complex cases by highlighting critical information and suggesting potential outcomes based on similar past cases.
Use Case: State Farm uses an AI-driven system to quickly assess damage from car accidents, estimate repair costs, and speed up the overall claim settlement process.
Artificial Intelligence can provide personalized and timely responses to queries and service requests. Unlike traditional customer service, which can be subject to inconsistencies, AI-powered chatbots can be swift and work round-the-clock.
Note: AI can efficiently handle a wide range of routine insurance queries, such as policy details, coverage options, and claim status, providing instant responses that enhance customer service. However, it has limitations when dealing with complex or emotionally charged issues that require human judgment or empathy.
Chatbots work by studying queries and delivering relevant responses based on data that’s been pre-programmed. Over time, they learn from each interaction and expand their database. Expanding helps the chatbot improve the helpfulness of the information offered. Additionally, AI can help anticipate customer needs by analyzing behavior patterns and preferences.
Use Case: Salesforce introduced AI-powered chatbots called Einstein Bots. Multiple businesses have begun using them to enhance customer engagement and cut costs. These businesses saw a 30% increase in customer satisfaction and more than 27% increase in agent productivity. Additionally, 64% of agents in these businesses have stated that they use AI to solve complex inquiries.
Integrating AI into the insurance industry means transforming practices as we know them. Manual workload is one of the easiest things to delegate. However, more complex applications emerge as companies leverage AI to gain a competitive edge. Here’s just a few:
AI can recognize deviations or patterns indicating suspicious activity by analyzing massive amounts of data. These can include unusual claims patterns, inconsistencies in customer information, or behaviors that deviate from the norm. AI systems also continuously learn from new data, improving their accuracy.
Use case: Progressive uses AI to flag potentially fraudulent claims by cross-referencing data from multiple sources. The sources include social media, historical claims data, and customer interactions.
Systems driven by Artificial Intelligence can analyze customer data and predict when a policyholder is renewing or lapsing. All of this is made possible through a customer’s behavioral patterns. AI can examine those, signal future actions, and provide predictions in real-time. Additionally, AI can offer suggestions tailored to policy upgrades or changes based on the customer’s current needs.
Use Case: Allianz uses AI to evaluate a customer’s profile and usage patterns to suggest the best policy options during renewal.
Telematics involves using devices and mobile apps to monitor a driver’s behavior, which requires speed, braking, and mileage. Usage-Based Insurance (or UBI) can then use this data to create insurance premiums tailored to a driver’s driving habits. Offering premiums customized to such a degree can give companies a competitive advantage compared to those who use generic factors like age or location.
AI can enhance telematics and UBI by reducing the time spent analyzing this data. It can also wholly minimize human error, making it a good choice for insurers to implement.
Real-life example: Root Insurance uses AI-powered telematics to offer personalized auto insurance rates. By analyzing driving data collected through a smartphone app, Root can offer fairer pricing based on how safely an individual drives.
Regarding AI in the insurance industry, it is safe to say that this technology is reshaping the landscape. More and more companies are implementing AI into their day-to-day operations at lightning speed. As we look ahead, the question is no longer “if” AI will change the industry but rather “how” and “to what extent.”
McKinsey predicts that by 2030, the underwriting process for most personal and small-business insurance products will be nearly instantaneous, with AI making decisions based on data from various sources.
However, this efficiency comes with challenges. The same AI models that assess risk so effectively can also create ethical dilemmas.
Where do we draw the line if AI algorithms use sensitive data, such as genetic information, to make decisions? The insurance industry must navigate these waters carefully, ensuring that technological advances are not at the cost of fairness and transparency.
Using AI for underwriting or claims management could lead to practices that are perceived as unjust, particularly if the algorithms lack transparency. Insurers could face legal repercussions if customers or regulators deem these practices discriminatory or biased.
To mitigate these risks, there is a growing call for stringent regulations and industry standards to govern the use of AI in insurance. Regulatory bodies increasingly focus on making AI models transparent, fair, and accountable. For example, the European Union’s General Data Protection Regulation (GDPR) sets strict guidelines on how personal data can be used, directly impacting how AI can operate within the insurance industry. Similarly, the emerging AI Act in the EU seeks to establish clear rules on using high-risk AI systems, including those used in insurance.
Last but not least, as AI gradually takes over human tasks, there is a growing concern about the future of jobs. Artificial Intelligence can handle manual, repetitive, and data-intensive tasks. However, the element of human touch, particularly in handling complex cases, must be considered. Insurers should balance using AI to increase efficiency and replacing human capabilities entirely with that of Artificial Intelligence.
As we sum up this article, the future of AI in insurance remains a complex topic. On one hand, Artificial Intelligence boosts efficiency and accuracy while eliminating human error. AI can also be trained to offer personalized customer service. On the other hand, however, AI being introduced into the insurance sector raises ethical questions, legal challenges, and growing concerns about jobs. The industry stands at a crossroads, and today’s decisions might just shape the sector’s future for decades.
