Big data has a major business impact on the insurance industry, and AI-powered technologies are potent means of maximizing its potential. Therefore, insurance companies are opting for artificial intelligence (AI) capabilities to be integrated into their systems. And this is just the start of the trend: Precedence Research estimates that the global insurance AI industry will be valued around USD 79.86 billion by 2032, up from an expected USD 4.59 billion in 2022.

If you want to follow this trend, wondering how AI changes the insurance sector and what advantages it offers, this article is exactly for you. To help you grasp the advantages of artificial intelligence, we have examined the most common applications in the industry as well as potential developments. We have also prepared some how-to recommendations if your insurance company plans to integrate AI into its daily operations. 

Enhancing Insurance Operations: The Most Effective Use Cases of AI

The complexity of today’s insurance tasks is too much for traditional technology. This is why innovative solutions are needed to create customized plans, automate tasks like payroll, invoicing, and fraud prevention. However, AI-driven Insurtech development services can change the way insurance companies operate. Below we describe how the companies from the insurance industry can leverage the power of AI for streamlining their business processes.

The Most Effective Use Cases of AI in Insurance

Insurance Product Management

Insurance policies can become much more personalized when machine learning handles customer demand, sentiment, and potential insurance scenario analysis. ML can assess the following data to satisfy the individual demands of different consumer groups: 

  • Real-time data, such as location or weather patterns; 
  • Consumer data, such as demographics, preferences, and behavior;
  • Historical data, such as claims or insurance cancellation reasons.

Natural language processing (NLP) also plays its role in crafting better insurance policies. This technology can extract data from chats, emails, surveys, and reviews so that later analyze it and understand the demand or sentiment concerning specific insurance products.

Serhii Leleko: AI & ML Engineer at SPD Technology

Serhii Leleko

AI & ML Engineer at SPD Technology

“Fair and accurate pricing for insurance products is also a decision-making point when presenting those products to customers. And we can actually achieve a great level of price fairness with deep learning models. They have the ability to adapt, when new data is fed to them. This enables dynamic pricing, where market conditions and customer behavior that change rapidly can be taken into account.”

Marketing and Sales

Machine learning techniques allow analyzing vast volumes of customer data, including location, demographics, online behavior, and purchase history to craft targeted marketing campaigns. Plus, there are scoring AI models that make it possible to analyze lead data from any campaign and see what actually triggered a lead to engage with a specific insurance company. This can help correctly allocate sales efforts. 

Another tool that is incredibly helpful for insurance marketing campaigns is generative AI. With its assistance, companies can create unique emails, social media posts, and ad copies. Additionally, companies can personalize these marketing materials for different customer segments. Hence, generative AI is capable of increasing engagement and click-through rates.

Using chatbots and virtual assistants in insurance also contributes to superior customer engagement. AI technology serves as a foundation for chatbots that can understand contextual information, outline customer needs, and provide responses in a natural language. Thus, insurance companies can utilize such conversations, for example, to suggest recommendations as part of sales efforts. In our project for HaulHub, we leveraged OpenAI’s GPT for the tasks involving NLP and created the chatbot that understands contextual data, processes customer inquiries, and facilitates conversations.

Underwriting and Risk Management

AI tools with deep learning and predictive analytics algorithms can automatically collect and analyze vast amounts of both customer data and potential future events with specific risk pools. When an insurance business is able to anticipate possible hazards and provide precise risk assessments for customer-relevant insurance coverage, this helps with predictive underwriting.

On top of that, underwriting processes can also be supported by the use of AI for customer behavior analysis. For instance, insurance agents can rely on evaluation of driving behavior, when elaborating car insurance plans, or health metrics, when crafting life insurance coverages.

Fraud Protection and Cybersecurity

In insurance, anomaly detection with machine learning helps to ensure fraud prevention. For that, ML algorithms examine vast data sources, so they could identify patterns that seem like unusual user behavior and can signal suspicious activity. 

Detecting anomalies involves more than just looking for unusual activities. Before a claim is even filed, early warning indicators of fraud can be recognized by utilizing AI approaches like predictive risk modeling. At the same time, behavioral biometrics examines user actions in parallel to look for warning signs of account takeover. Anomaly detection also carefully examines personal data for irregularities, which aids in the detection of attempts to create false identities. 

AI technology also offers enhancements for cybersecurity. While deep learning helps with identity verification through facial recognition and document authentication, machine learning and NLP monitor systems to discover emerging cyber dangers.

Explore the nuances of fraud detection using machine learning models in our recent article!

Claims Management

The Accenture report reveals the result of how an AI-driven system integrated in the processes of a Polish insurance company. That result is a 10% reduction in claims errors, which is a great improvement considering how many claims insurance companies need to process daily. 

Similar or even better outcomes for claim processing can be guaranteed, when insurers add the following capabilities to their systems:

  • ML-powered demand forecasting that aids in predicting the volumes of claims and commit resources to fulfill them;
  • Computer vision along with object detection to analyze images in case damage assessment is required;
  • Fraud detection that can spot irregularities in claims, detect discrepancies in repair costs, or check a history of false claims;
  • NLP to fine-tune chatbots to answer inquiries concerning claims status and gather customer information. 

Moreover, with automation brought to claim processing with AI, insurance companies have the ability to simplify the process of data collection and analysis. In this manner, we completed a project for a B2B Intelligence Services company. Our team integrated NLP and the YOLO model to ensure the extraction of data from PDFs with precision. Camelot or AWS Textract streamlines the extraction, and GPT verified data accuracy for smooth downstream analysis. This resulted in 5x cost reduction.

Billing and Payroll

Predictive analytics, anomaly detection, and automation algorithms in AI applications offer the capabilities for preparing and sending invoices and bills for customers. 

With the use of predictive analytics, artificial intelligence technologies may anticipate revenue based on billing data and trends, as well as examine payment data to enhance billing tactics. Also, insurance agents can monitor spending data and spot odd patterns with the help of anomaly detection. This makes it possible to identify possible inefficiencies that could result in fraud or overspending and implement cost-cutting solutions.

Automation, in its turn, can further simplify claim management. We have proved this when completing the project for our client, where machine learning models for NLP, text classification and embeddings to predict the master service code for invoices based textual description. Consequently, the client’s system was set up to have automated invoice processing, which helped to accelerate the process of the aggregation of the information on operational costs from weeks to hours.

Shaping the Future of Insurance with AI

With the fast-paced advancement of artificial intelligence, it will soon be possible to change the insurance industry beyond recognition. But what exactly will be different? Let’s see what awaits insurance with this new technology in the near future.

The Future Trends of AI in Insurance

Usage-Based Insurance (UBI) Products

With data analysis that becomes enabled thanks to AI, businesses can get the most out of data and improve their usage-based insurance (UBI). In particular, they can use AI models to process data about drivers’ behavior and vehicle usage trends in real-time. AI gathers and evaluates pertinent data on mileage, journey duration, speed, acceleration, use of seatbelts, engine performance, and other topics with the use of telematics devices. Furthermore, risk assessment makes use of this data.

Equipped with all that data, businesses can tailor personalized insurance plans. For example, UBI programs might offer discounts for drivers who stay below a certain mileage threshold per month, indicating less overall usage of the vehicle. 

Cyber Insurance Products

According to Forbes, 2,000 cyberattacks were reported in 2023. And while not all the issues are reported, the real number is always higher. People and businesses are now reliant on technologies in almost every aspect of their actions. So, cyber insurance becomes another necessity. 

With cyber insurance, policyholders can prevent financial losses because of data breaches, network outages, and cyber extortion. AI technology enables insurers to predict the chances of cybercrime and notify their clients about it. Thus, preventive measures against possible issues can be undertaken.

The Rise of IoT-Generated Data and Unified Data Ecosystems 

Since data is the primary input for any AI-driven systems, any enterprise or industry must have open access to it in order to maximize its value. This might entail businesses releasing their data in accordance with open-source guidelines, promoting a cooperative environment where all parties gain from a shared data set. To protect data security and privacy, nevertheless, a strong legislative and cybersecurity framework must be established.

Therefore, any insurance company will have access to the data from those devices to analyze it and develop coverage plans. The most efficiency of this opportunity will be seen in healthcare, automotive, and property insurance. AI will be able to collect and analyze data from wearables, smart cars, and smart home devices to later transform collected insights into insurance terms.

Switch to Predictive and Preventive Insurance 

We know insurance as the one that returns the losses of the accidents that have already happened. But what if insurance companies will be able to forecast potential risks with AI and prevent them? Predictive analytics and data-driven insights will become the cornerstone of preventive insurance.

Serhii Leleko: AI & ML Engineer at SPD Technology

Serhii Leleko

AI & ML Engineer at SPD Technology

“Supervised learning models (classification & regression) predict incidents and claim costs respectively. Unsupervised learning (clustering) groups policyholders by risk profiles. In such a way, insurers get the opportunity to personalize insurance coverage plans and reduce overall claims. The customer will be happy because of such AI capabilities in insurance.”

AI as a Service (AIaaS)

Artificial intelligence integration in insurance companies’ systems has its limits. It is difficult and resource-efficient to develop and maintain ML models for each and every aspect of operational processes. That is why, AIaaS can help insurers to buy pre-built AI models that can address specific functionality as well as purchase tools without the need to invest in building their own complex AI infrastructures.

Those pre-built AI models will offer such capabilities as:

  • Image Recognition
  • Generative AI
  • Predictive Analytics
  • Personalized Recommendations
  • Chatbots
  • Smart Home Integrations
  • Fraud Detection
  • Sentiment Analysis

InsurTech earnings are eventually increased and customer needs are met when insurance organizations utilize AIaaS to simplify processes, cut costs, and provide a more efficient and personalized client experience.

How Insurance Companies Can Start Adopting AI in 2024

The transformative power of artificial intelligence is evident, however, many companies still puzzle over its implementation in existing systems or development of custom insurance software with AI. Yet, we are sure that there is nothing impossible. We believe that if you prioritize starting points, craft suitable and appropriate strategies, and invest in training your staff, your organization will handle AI adoption challenges. See the details on our tips below.

Start with Small Pilots of POC Projects

Start by figuring out a precise, well-defined field in which AI can be useful. This could involve getting more precise risk assessments, identifying fraudulent claims, or gathering AI-generated insights from sensor or consumer data. With a chosen goal for your future insurance software, run a proof-of-concept project. This will allow you to test the feasibility and effectiveness of AI in a controlled environment before making larger investments into a custom AI solutions development.

Build a Far-Reaching Still Adaptable AI Transformation Strategy 

While starting small, you still need to elaborate a vision for how AI will be used to transform your insurance operations in the long run. To do that, you must identify the domains in which artificial intelligence will have the most effects, as well as any obstacles you may encounter and a plan for implementing AI gradually. 

Moreover, take care of the adaptability of your strategy, since it can undergo numerous adjustments, while you will be adopting AI. So, in the process you need to have a better understanding of how AI can accommodate your growing requirements.

For example, we helped to deliver custom insurance software for Pie Insurance. To successfully realize this project, we focused on the most important features during the planning stage. We outlined that the core elements we needed to develop were a custom billing system, operational data maintenance platform, and self-service application for end customers. 

Also, the platform needed to be integrated with a policy management application. With such detailed planning, our team managed to deliver a solution that directly addressed Pie Insurance’s business needs. Upon a successful delivery of the project, the company achieved a 40% reduction in operational time and a 30% increase in profits.

Prioritize Customer-Centric Solutions

Customers are the center of your business, therefore all the services, features, and functionalities of your insurance applications should also ensure that they receive the maximum benefits from it. To do so, prioritize features like chatbots for customer support efforts, develop personalized risk assessments to be able to design on-point premiums, and ensure active loss prevention measures. 

On top of that, you can leverage generative AI for the creation of tailored marketing campaigns depending on the unique characteristics and inclinations of each client. This can entail giving helpful safety advice, promoting pertinent insurance products, or delivering targeted savings. Customers will find this strategy more relevant, and engagement rates will rise as a result.

But just as crucial is that your app’s UI/UX be created in accordance with the most recent developments in the sector. Adding AI-driven functionality to your app is a great step, but in order to keep users, you also need to make the program easy to use and intuitive.

Start Automating Rule-Based Processes 

Insurance processes are built on repetitive data entry, document verification, claim review, payment processing, and others. These tasks are ideal for robotic process automation (RPA).

If your insurance business decides to opt for RPA development services, you can speed up all the aforementioned operations. Also, insurers can spend less time on policy renewals processing policy charges. The less time insurance agents spend on such tasks, the more they can focus on creative tasks. Moreover, automation eliminates human error, consequently, improving customer experience due to faster and impeccable service. 

Focus on Talent Acquisition and Development

AI serves as an enhancement of human efforts and expertise. Therefore, your staff needs to be trained to use AI tools and get the maximum benefits from it. If you do not have skilled talent in-house, you may also need to consider acquiring new talent with expertise in machine learning, data science, and AI implementation. With the powerful combination of human efforts and AI capabilities, your company can maximize the results of work.

Consider a Technology Partnership

Partnering with an established AI/ML solutions development company that has experience with InsurTech brings benefits for any company. However, small insurance firms can benefit from such a collaboration the most. 

Tech partners can share their extensive experience in building and integrating AI solutions as well as providing ongoing support. And while the tech side of your business is supported by specialists, you can use your resources for strategic planning in order to potentially accelerate your AI adoption process among employees and reduce development costs.


By changing insurance coverage plans, risk and claim administration, billing and payroll, fraud protection, and marketing, artificial intelligence improves the insurance sector. It helps businesses to create customized products, automate procedures, and guarantee correctness throughout the whole process. 

However, this is just the beginning: AI’s capabilities advance, and its impact is growing. In the near future, insurers will be able to improve their services thanks to AI-enabled usage-based and cyber insurance, AI-as-a-service, and preventive approach for risk prevention. For the case you are also planning a switch to AI-driven insurance, get started with feasible initiatives. To successfully adopt AI, start by running a small pilot project, develop adaptable AI implementation strategies, prioritize customer-centric solutions and consider technology partnerships with a trusted AI/ML development company! 


  • What Is AI in Insurance?

    AI in insurance shifts business operations via data analysis. It makes possible more easy and accurate risk assessment, individually-tailored insurance policies, simplified administrative processes, and even risk prediction to avert claims. For insurance businesses, this means more efficiency, better customer service, and more equitable pricing.