AI in Insurance: How Insurers Can Harness the Potential of Artificial Intelligence for 2025
The use of AI in insurance is accelerating rapidly. From virtual agents and fraud detection to claims process automation, AI is integrated into core insurance processes to meet customer demands, drive operational efficiency, and enhance risk management.
How can your insurance company leverage AI? What are the benefits, challenges, and best practices? Let’s discuss this and more, including how AI-powered cloud communications technologies like Vonage Communications can help you enhance your AI-driven processes.
What is AI in insurance?
Artificial intelligence (AI) in insurance involves integrating technologies like natural language processing, automation, machine learning, and computer vision into insurance processes. With the ability to ingest vast volumes of data, AI insurance technologies can recognize patterns, extract insights, make predictions, and automate manual tasks.
The integration of AI and insurance has led to a reshaping of core insurance processes, optimizing every stage of the insurance value chain, from risk management to claims and customer service.
What are the main objectives insurers try to address with AI?
Of all the reasons that insurance companies adopt AI, three key objectives stand out:
Creating better customer experiences: Handling insurance responsibilities can be tiring and complex for customers. AI helps insurers meet customer demands for streamlined, stress-free, and individualized customer experiences. This is achieved via various technologies, such as chatbots and process automation.
Driving operational efficiency and productivity: To shorten the claims lifecycle, insurers leverage AI’s ability to automate key claims processing activities, such as claim data analysis, document generation, and automatic payouts. AI also removes time-consuming, error-prone tasks from insurers’ workflows, optimizing efficiency and productivity.
Risk mitigation: To mitigate fraud and accurately identify risk factors, insurance underwriters and claim adjusters are turning to AI to automate error-prone processes, detect hidden risks, monitor compliance, and more.
3 AI-related trends shaping insurance
From the explosion of IoT devices to the urgency of digital transformation, the evolution of AI in insurance is being shaped by key technological, industry, and market trends.
1. IoT devices
The abundance of Internet of Things (IoT) devices on the market — and, in turn, the data they generate — shows no signs of slowing down. Statista’s research into global IoT connections forecasts that by 2030 there will be over 32 billion IoT devices worldwide — almost double the amount we have today.
Medical devices. Fitness watches. Security cameras. Smart cars. These connected devices provide insurance companies with enhanced access to increasingly intricate customer data. This data can create accurate risk profiles, personalize pricing models, accelerate claims processes, and more.
2. Robotics
Robotics and AI are rapidly integrated across various industries, from agriculture to healthcare, automotive, and construction. You’ve got 3-D printing, self-driving cars, robots performing surgeries, autonomous manufacturing equipment — the list goes on.
While these innovations are exciting, they pose special considerations for insurance companies. Insurers must navigate the growing complexity of regulations, policies, risk assessments, and customer expectations in a robotic environment.
3. Digital transformation
Digital transformation in insurance has become necessary for remaining competitive amid volatile market trends, customer demands, and business challenges. It involves deploying digital technologies at scale — not just AI but cloud computing, cloud communications, big data, IoT, and API integrations.
Digital transformation is a slow, methodological, and continuous venture. Insurance companies must align AI needs with their wider digital transformation strategy.
Use cases of AI in the insurance industry
The use cases of artificial intelligence in the insurance industry are vast. AI-driven strategies and technologies can be integrated into core insurance processes, such as claims and underwriting, as well as general activities, like customer service and sales, to drive numerous benefits.
Let’s explore all the ways that AI in the insurance industry can be put to use.
Customer service
Customer service is one of the biggest applications of AI in insurance, paving the way for hyper-convenient, accessible customer experiences.
AI-driven virtual agents can assist customers 24/7, empowering them to access support whenever needed. Using natural language processing (NLP) and ML, virtual assistants can simulate human conversation, gauging intent to provide efficient, accessible, and proactive service.
For example, virtual agents can answer basic queries about insurance policies and provide personalized information such as claim statuses and coverage. They can also initiate claims, complete transactions, and other increasingly complex tasks.
AI virtual assistants can also enhance insurance communications between agents and customers. AI assistants can transcribe calls in real-time, gauge customer sentiments, route calls to the appropriate location, and guide agents in answering specific questions.
Claims
Claims processes can be complex, lengthy, and error-ridden. AI insurance solutions tackle these challenges using computer vision and automation.
For example, computer vision and ML models can analyze pictures and videos to assess the damage done to vehicles and homes. They can evaluate the nature of the damage, identify the necessary fixes or replacements, appraise damage values, and even automate payments — all faster than human assessors.
Plus, AI-powered contact centers streamline business communications to keep customers updated every step of the way. Virtual agents can communicate claims statuses, and agents can access claims information in real-time.
Underwriting and pricing
Digital transformation has given us access to vast volumes of data across many digital touchpoints. Combining this data is time consuming for insurers, who are pressured to make fast decisions while being fair and competitive.
AI facilitates data-driven underwriting and pricing decisions. It gathers, unifies, and analyzes data, such as demographics, financial information, telematics, social media activity, health records, and claims history.
With immediate access to this data and AI’s ability to identify hidden patterns and trends, insurers can make fast, data-backed underwriting decisions that minimize risk. They can also tailor pricing models and coverage to the risk profiles of individual customers, resulting in fairer premiums and more satisfied customers.
Sales
AI is transforming sales. Hubspot’s latest State of AI report found almost half of all salespeople use generative AI, followed by task automation and data analysis.
For insurance sales teams, AI platforms unlock powerful customer and sales analytics that can be used to increase conversions directly. AI transforms raw customer data — such as purchasing behaviors and browsing patterns — into actionable insights, enabling sales reps to personalize policy recommendations, tailor outreach efforts, and create targeted (and AI-generated) sales materials.
AI tools also track and analyze key metrics like bind rates, quota rates, policy sales growth, and net profit margins. This provides insight into sales performance and can be used to guide sales strategies.
Fraud detection
Digital fraud is a persistent threat. But, with the help of AI, insurers can spot and eliminate fraudulent claims before they lead to financial damages.
AI-powered fraud detection tools combat fraud attempts in real-time. They use ML algorithms to identify anomalies in claims data and customer behavior, immediately flagging abnormalities as suspicious.
For example, AI might flag potentially forged documents, deepfakes, and inaccurate data entries. Fraud detection tools can also analyze suspicious activities, such as high claim frequencies, peculiar payment patterns, and unusual social media activity.
Risk prevention
Traditional risk assessments fail to provide insurers with the data comprehensiveness and real-time insights needed to make informed, proactive underwriting decisions. On the other hand, AI-driven risk assessments accumulate real-time data from numerous sources and, bolstered by ML and predictive analytics, help insurers accurately identify risks.
For example, a vehicle insurance company needs up-to-date demographics, historical claims data, and financial data to assess risk. Not only can AI deliver this data much faster than manual methods, but it can also collect and analyze driving behavior data from telematic devices.
Predictive analytics is another AI capability that can help you prevent risks. AI looks at intricate datasets to establish patterns, trends, and relationships that are invisible to the human eye. This data can be used to identify policy risks and provide customers with proactive risk reduction advice.
Follow the journey
How is AI used in healthcare insurance underwriting?
Healthcare insurance underwriting is the critical process of assessing customer risk factors to determine fair premiums and coverage options. Done manually, underwriting is a painstaking task that leads to risk profiling mistakes, poor price optimization, and procedural errors.
AI-driven underwriting improves the speed and accuracy of assessments. It can evaluate data like age, lifestyle, and health risks, helping insurers create tailored risk profiles and offer aligned premiums and coverage. Predictive modeling can also use data like genetic predispositions and lifestyle patterns to make sound predictions about policyholders’ future health.
AI in insurance claims: Does it improve processing?
Yes. Claims processing is one of the most significant applications of AI and automation in insurance.
Traditional claims processing was done manually and took a long time to complete. Claim adjusters had to inspect damages physically, pore over lengthy claims documents, meticulously analyze data to detect fraud, and manually calculate and process payments.
AI-powered claims processing automates these steps. For example, computer vision can analyze images and videos of car and property damage to evaluate the extent of damages, necessary fixes, and reimbursement costs.
NLP can be used to review claims documents. Fraud can be detected using machine learning. And AI can even auto-approve payments.
These automations streamline claims processes, reducing weeks-long claims lifecycles into days or hours. As a result, settlements can be reached faster.
The benefits of using AI in insurance agencies
The advantages of using AI are hard to ignore. Here are some of the key benefits that insurance agencies realize when they embrace AI:
Reduced human error: Manual data entry and exchange often leads to human error, costly mistakes, and slowing down processes. AI tools automatically input and evaluate data for accuracy, minimizing this risk.
Increased customer satisfaction: The combination of speedier claims settlements, self-service chatbots, personalized pricing, and enhanced customer communications leads to better customer experiences and increased satisfaction.
Efficiency and productivity: AI streamlines claims and underwriting processes, resulting in faster decision-making and settlements. And, with AI handling tedious, time-consuming tasks, insurers have more time to focus on complex, strategic, and customer-centric activities that directly impact growth, revenue, and relationship-building.
Fewer risks: AI helps mitigate threats and costly assessment risks by automating fraud detection and underwriting risk profiling.
ROI in the implementation of generative AI in insurance
One of the most widely adopted forms of AI — generative AI — boasts significant return on investment (ROI) potential. Its ability to leverage historical data and trends to build predictive models, expedite claims processing, and automate content creation is a testament to its widespread usage and the revenue gains it returns.
To give you an idea of generative AI’s ROI potential, let’s look at findings from Google’s latest study, The ROI of Gen AI in Financial Services, which includes insights from 340 senior finance leaders.
According to the study, 90% of finance companies currently implementing generative AI have seen revenue gains of 6% or more. Increased productivity and efficiency contribute significantly to these gains, with 50% of firms reporting that employee productivity doubled due to Gen AI implementation.
These ROI gains are projected to skyrocket as early adopters of AI continue to mature their implementations.
Challenges and risks of AI in insurance
Despite AI’s huge potential, it’s not all smooth sailing. The risks and challenges of implementing and using AI can hurt the efforts of ill-informed and ill-prepared businesses.
Some of the main challenges and risks of AI in insurance include:
Data quality issues: If the training data you supply to AI is low-quality, outdated, or biased, its outputs will follow suit, leading to unfair outcomes, misleading insights, and other inaccuracies.
Explainability and transparency: Deep-learning models are difficult to understand, posing issues when you must explain them to regulators and customers.
Ethical concerns: Leveraging AI to handle sensitive customer data can pose ethical risks, resulting in lost customer trust.
Regulatory challenges: Regulatory laws and guidelines surrounding AI constantly adapt as AI evolves. Ensuring that AI solutions and strategies comply with new and updated regulations is a continuous challenge.
High costs: Implementing AI solutions can get expensive, especially if you’re migrating away from legacy systems and don’t have a robust, long-term strategy. Sadly, financial loss from poorly executed AI implementation is a frequently encountered risk.
How insurers can prepare for accelerating changes and get the most out of AI: best practices
Despite the above challenges, AI remains an invaluable — if not vital — tool for insurance agencies. The key to a successful AI strategy is to prepare for the curveballs of change presented by the fast-paced AI landscape.
To do this, you can follow these five best practices.
1. Develop a strategic, agile AI adoption strategy
A clear, long-term AI adoption strategy helps you identify the AI use cases and technologies that align with your business priorities, needs, budgets, skills, and data readiness. It provides a roadmap to success and helps you eliminate and navigate challenges.
Start by evaluating your current and future business needs. What areas of your business are most in need of AI transformation?
Once you’ve identified your priorities, source the AI technologies you’ll need to meet your goals and develop a plan that integrates them seamlessly, keeping your budget, skills, and data maturity in mind.
Agility is also key. Your strategies may have to adapt to remain viable in the fast-paced AI landscape. So, conduct regular reviews and pivot strategies in response to change.
2. Aim to understand — and keep up to date with — AI innovations and trends
Early adopters of AI innovations fare best. IBM surveyed 1,000 C-level insurance executives in their “Generative AI in the Insurance Industry” study and found that 77% of insurance industry executives think rapid AI adoption is crucial for keeping up with competitors. And for those that do, increased customer satisfaction, retention, and other benefits are reported.
This stresses how vital it is for insurance leaders to stay updated with AI-related trends and technologies. It allows you to seize profitable opportunities and remain competitive in the face of rapid AI innovation.
However, jumping on trends requires fully understanding the technology behind them to be safe. Insurers need to invest time in researching AI technologies and understanding their use cases and risks.
3. Embrace cloud technologies
Cloud technologies and AI go hand-in-hand, future-proofing your business by providing scalability, flexibility, security, and enhanced business capabilities.
For example, cloud data warehouses allow you to quickly and easily collect, store, clean, and integrate the vast volumes of data essential for training AI models. Cloud communication tools allow agents to deliver seamless insurance communications from remote locations and via multiple channels. They also come with powerful features like CRM integration and call recordings, giving insurers (and AI) access to mountains of key data that can be used for analysis.
4. Keep up with regulatory compliance
As AI capabilities expand, so do laws and regulations. Regulatory guidelines, such as GDPR, HIPAA, and PCI-DSS, govern AI's fair, ethical, and transparent use in business. Specifically, they dictate how AI can use sensitive customer data, ensuring protection from misuse, threats, and privacy violations.
To avoid legal action, financial loss, and reputational damage, insurers must monitor regulation changes to ensure continuous compliance.
5. Support and invest in your talent
AI training for your employees is essential. It onboards your workforce with new AI technologies, equipping them with the necessary knowledge and skills. It also fosters a culture of digital transformation, where employees are empowered to work with AI to achieve unified goals.
You may also need to hire new talent with specific skills and expertise, such as data scientists, data analysts, and AI engineers.
The future of AI in insurance
According to SAS’s recent study on Gen AI in insurance, which gleaned insights from almost 250 insurance decision-makers, 60% of respondents said they’d either fully implemented or were in the process of fully implementing Gen AI.
Moreover, almost 90% plan to invest in Gen AI even further next year. This tells us that AI is set to integrate even deeper into core insurance business processes.
We can see what this might look like by identifying insurance companies’ main goals for AI adoption. Customer satisfaction and retention topped the list at 81% in SAS’s survey, followed by reduced operational costs, time-savings, and enhanced risk management.
Future AI implementations will be significantly customer-centric. This means tighter integrations with IoT devices, advanced algorithms that unlock deeper customer insights, and increasingly intelligent, human-like chatbots. These AI solutions help you reduce cycle times to minutes — if not seconds — and further enrich underwriting, pricing, and insurance customer service personalization.
Enhance AI-driven processes in insurance with a top suite of communications APIs and cloud-based solutions
For insurance companies, leveraging AI to streamline key processes, access deeper customer insights, and enhance communications is crucial. With Vonage Business Communications, you can access a suite of communication APIs to maximize customer engagement and deliver seamless, personalized customer service — all while enriching AI-driven processes.
Vonage Voice API lets you deploy customized conversational AI to deliver automated customer service around the clock. Access rich analytics from your conversations and calls, and integrate your platform with your insurance CRM to see claim histories, premium logs, policy information, and more.
Reach out to our expert team to learn more about Vonage APIs and AI for the insurance industry.
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Still have questions about AI in insurance?
AI in insurance refers to the range of artificial intelligence technologies that streamline and automate insurance processes, such as claims processing, risk management, and customer service.
Insurance companies can use AI in lots of different business areas. It’s commonly used to automate claims and underwriting tasks, including data entry and analysis, report generation, and risk assessments. Other use cases include fraud detection and customer self-service.
Research by SAS shows that 11% of insurance companies have fully adopted AI, while 49% are in the process of implementing AI at scale. 90% plan to implement AI in the next year.
Virtual agents, or conversational AI, use natural language processing to simulate human conversation and deliver 24/7 self-service to insurance customers. They’re capable of providing information like claims statuses and quotes, answering queries about insurance policies, and handling issues like claims processing and appointment booking.
AI and machine learning in insurance can help insurers meet customer expectations for faster claims settlements and more personalized services. For example, ML can analyze large volumes of customer data to extract insights and make predictions about coverage, enabling insurers to offer the personalized policy and pricing options that customers desire.
No. AI is designed to augment insurance agents, not replace them.
Human insurance agents can uniquely empathize with customers, perceive subtle social cues, and foster meaningful relationships — abilities that AI cannot. Plus, AI makes decisions based exclusively on cold, hard data, whereas insurance agents use context and innate human insight to guide decisions.
