Have you ever tried to check your insurance claim status? It often requires several calls, some emails, or even a visit to an agent to get claim status details. A lack of web presence leads to lower customer satisfaction.
In recent 10 years, many insurance carriers have made significant modifications courtesy of disruptive digitalization. Gartner forecasts that the global insurance industry’s IT spending will reach $256.8 billion in 2026.
The total, nevertheless, is still quite low, with legacy system complexity only slowing innovation. Despite the digital promise, deep-seated reliance on manual review, siloed data, and spreadsheets remains a significant operational bottleneck for the insurance industry. The large industry, which globally accounts for $8.155 trillion, needs to urgently change the ways of business development. It’s clear that the make-insurance-great-again mission heavily depends today on technology adoption.
Let’s discuss 13 ways companies can transform from traditional insurance agencies into innovative insurtech firms.
Internal workflow automation with RPA and machine learning
Paperwork, manually written notifications, follow-ups, and underwriting are extremely tedious. Automation allows companies to reduce the cost of routine work and refocus some full-time employees to more creative tasks.
McKinsey reports that leading insurers are already achieving a 20–40 percent reduction in customer onboarding costs, major improvements in claims accuracy, and large-scale automation across underwriting, claims, customer service, finance, actuarial, and IT operations through generative and agentic AI.
One of the first persuasive examples of cost-cutting was the automation of payout calculation at Fukoku Mutual Life Insurance. The company reported replacing 34 employees with AI. However, the industry hasn't been reporting on such achievements lately. Instead of replacing talent, leaders are now prioritizing human-machine synergy by investing in AI literacy and "human-in-the-loop" workflows.
It’s worth noting that AI remains a heavy investment. Depending on the work the machine learning algorithms are going to do and regulations, it may require an explanation layer over the core ML system.
On the other hand, the practice of RPA (robotic process automation) doesn’t always involve machine learning but can address routine and repetitive work. RPA uses transparent human-designed rules to do repetitive operations.
European insurance group PZU partners with UiPath, an RPA provider. The overall scope of automation covered nine areas of activity, including preliminary analysis of car damage claims, data entry, payments, and more. The company claims to have increased the number of decisions per employee by 15 percent, reduced call times to customer service by half, and achieved 100 percent accuracy of data entry.
Digitizing paper records with optical character recognition
The other side of the problem is inbound correspondence and documentation. Financial institutions process and gather thousands of files in paper archives daily. That’s not the best way to store, process, and exchange information. It doesn’t contribute to saving the environment either.
If files are digitized, analyzed, and stored in a cloud, documents can be automatically reviewed and rejected in the case of inconsistent information or errors, which allows insurance staff to deal only with consistent and correct information.
The customer data that has been collecting dust in paper archives for decades is no longer an expenditure item in a profit-and-loss statement. If you apply optical character recognition (OCR) algorithms, these become valuable assets that tell an insightful story about your customers. The real-life applications of OCR appeared in the early 2010s. So, there’s nothing new to the technology except for the increased accuracy of the underlying machine learning algorithms capable of digitizing text.
Modern insurance uses AI-powered OCR to understand document context. The process typically looks like this:
- Ingestion: Scanned documents, PDFs, faxes, or photos are uploaded to the system.
- Recognition (OCR + AI): The algorithm identifies the layout, recognizes characters (typed or handwritten), and classifies the document type.
- Extraction: Instead of just extracting raw text, the system pulls specific data points (e.g., policy numbers, claim amounts, patient names) and maps them to structured fields in a database.
- Validation: AI models check the extracted data for inconsistencies (e.g., comparing a claim against historical policy data) and flag errors for human review.
Besides digitizing paper records, Insurers use OCR+AI across almost every department, from underwriting to onboarding new clients.
Read our dedicated post about intelligent document processing in insurance to learn more about how it works.
Machine Learning and Computer Vision in insurance: automation of claim processing
AI insurance software reshapes claim processing. Ask yourself how long it takes for your agency to make claim decisions. There are lots of issues with a variety of scopes: a broken finger, a big car accident, a fire in a luxury villa, or a significant agrarian claim from a large corporate client.
Assume that their US land bank was affected by drought. There are thousands of acres across the country sown with crops. How long would it take for employees to gather and process all data required for payout decision-making? There’s a good way to cut costs for such projects by employing AI. Machine learning algorithms can calculate detriment using satellite images or drones to explore fields.
Source: Scor
The algorithms can also analyze vehicle photos to instantly estimate repair costs and flag pre-existing damage. For example, Zurich Insurance Company, in partnership with Nearmap, integrated high-resolution aerial imagery and AI-based roof scoring to identify risk exposures and property damage in seconds. This US nationwide rollout has empowered Zurich to sharpen its risk selection, accelerate quoting times, and offer precision-tailored pricing, particularly in regions prone to extreme weather.
Claim reporting is also changed by a combination of machine learning and mobile technologies. State Farm arms clients with an app. A customer can send the vehicle image, and the claim will be submitted without wasting time on dealing with paper documents or large web forms.
Redefining traditional ways of claims and policy management in the age of digital insurance
Besides AI-driven automation, claims management gets impacted by a broader spectrum of software solutions.
Claims management is a critical business process of any insurance company, which starts with claim registration and ends with payments to the insured party. Claims management software reduces manual workflow and a number of human-to-human interactions. Clients need less time to apply and smoothly proceed down the path of claim handling.
Here is an example of the modern industry standard. Assume an insurance company operating in the healthcare segment.
- Claim management software automates information exchange between insurance and healthcare provider systems.
- If the company deals with a number of small private practices – which still work with paper documents – the import is streamlined by image recognition algorithms that digitize the documents.
- The system calculates coverage and payment for each claim according to set policies.
- The system processes claims and sends them to a fraud detection module.
- Once the claims are approved, insureds receive their payments.
The policy management software must be an integrated part of the system in order to provide business users with instruments to manage reconciliations, customize business logic, manage policy rights, etc.
The software by SAP, Oracle, Patra Corp, GuideWire, and Insly fulfills standardized needs in claim and policy management tools for the insurance industry. However, the forefront of innovations is insurtech startups and technology consulting companies, which employ the power of AI, Blockchain, and IoT technologies.
For instance, Aviva, the UK’s largest general insurer, significantly enhanced its claims operations by end-to-end AI transformation. According to McKinsey, this transition enabled the company to reduce the average liability assessment time for complex cases by 23 days while simultaneously boosting routing accuracy by 30 percent.
Personalized insurance pricing with IoT
The old-fashioned style of risk assessment is to rely on impersonalized datasets. But today, endpoint devices can provide large amounts of personal data. The approach can help both insurers and customers – the latter get cheaper coverage or bonuses and highly personalized services, while businesses get more accurate risk assessment, stable margins, and satisfied clients.
Recent research shows significant consumer interest in sharing behavioral data in exchange for lower premiums and personalized services. Approximately 89 percent of surveyed consumers said they would share health, exercise, and driving data to help lower insurance prices and get more personalized coverage.
An underwriter can consider a broad range of highly personalized records. Connected devices and wearables provide deep insights into the customer’s physical condition, like blood pressure, temperature, and pulse. Now, the insurer can even explore the client’s lifestyle patterns, such as the number of steps per day, or how often and how long it takes someone to brush their teeth.
Beam uses IoT technology to offer dental insurance. A smart toothbrush tracks how well customers take care of their teeth. Then the company provides a personalized insurance plan based on teeth-brushing data. The firm claims that the insurance can up cost to 15 percent less at renewal.
Vitality is another example. The company offers health insurance with rewards. Members earn points for physical activity. These points determine a status level—Bronze through Diamond—which unlocks rewards and premium discounts from the company’s partners. Data is collected through fitness devices and apps.
To learn more about personalized insurance, read our dedicated article.
Telematics Insurance – a way to make car risk management better
Telematics insurance is a group of innovative car insurance products that get installed directly into a vehicle. The device includes a GPS system, motion sensors, a SIM card, and analytics software. A telematic box tracks speed, location, time, crash accidents, driving distances, breaks, and other driving data.
First, the system processes gathered information and transmits it via the mobile Internet to the insurance company for further analysis. Then the driving analytics are added to a customer's personal account.
By tracking drivers’ behavior, insurers can deploy usage-based models to dynamically adjust premiums. For instance, a company can increase charges from irresponsible drivers, reward customers for safe driving, and notify police in the event of a car accident. Telematics adoption allows for utilizing such disruptive business models as:
- usage-based insurance (UBI)
- pay-as-you-drive (PAYD)
- pay-how-you-drive (PHYD)
Allstate’s Drivewise app monitors driver behavior
There are many successful use cases. For instance, Allstate personalizes auto insurance through its Drivewise and Milewise telematics programs. Drivewise, available via a mobile app, tracks driving behavior, provides trip-by-trip feedback, and rewards safe driving. Customers can review performance metrics and rewards, while premiums are influenced by factors such as speeding, hard braking, and driving times.
Milewise extends this approach with a usage-based pricing model that combines a fixed daily rate with a per-mile charge, allowing costs to reflect actual vehicle usage. Customers can also customize coverage limits and optional protections to better match their driving habits and risk profile.
Disruptive business models – P2P Insurance
Peer-to-Peer (P2P) insurance is one of the most disruptive business models which is rapidly gaining popularity due to the available technology basis. The model entails that the network of people agrees to cover similar risks by creating a single finance pool consisting of their premium shares. The P2P model doesn’t require traditional intermediaries, such as insurance companies.
At the end of each coverage period, any available funds are refunded. This way, customers minimize their costs and mitigate claim conflicts. However, the model has several drawbacks, such as fraud sensitivity, ethical aspects, difficulties in achieving consensus, and a lack of trust between peers.
The P2P insurance has already passed three main milestones:
- Insurance distribution. For instance, Friendsurance pioneered one of the first commercial P2P insurance models by grouping policyholders with similar coverage needs and rewarding claim-free groups with cashback bonuses. If an insured event occurs, the insured reports a claim via Friendsurance and gets coverage. As soon as a contract expires, customers receive pre-agreed cashback from funds in the pool. It’s worth noting that the company has since expanded into broader digital insurance and bancassurance solutions.
- Insurance carrier. For example, JustInCase is one of Japan's leading insurtech pioneers and among the few companies operating across Insurance-as-a-Service (BaaS), on-demand insurance, and P2P insurance models. As a licensed provider of small-sum and short-term property and casualty insurance, the company delivers fully digital products, including COVID-19 coverage, deferred-payment P2P cancer insurance, and AI-powered mobile phone insurance.
- Self-governance. An example of a self-governing insuretech organization is Teambrella. The decentralized peer-to-peer coverage platform enables members to collectively evaluate claims and vote on reimbursements. Rather than relying on a traditional insurer, participants share risk directly and retain significant control over coverage decisions through a community-driven governance model.
Insurance blockchain disrupts reinsurance operations
Blockchain implementation is a $5-10 billion cost-saving opportunity for reinsurers worldwide, according to PWC. The nature of reinsurance is close to a chain structure. No wonder it is recognized as the second-largest distributed ledger use case in fintech after payments. The major benefits for stakeholders include reduced verification and validation time, fewer errors, and minimized reputational risks. By using blockchain, a reinsurer won’t have to interact with the insurer to get data provided by the client. For instance, you need to verify several insured events for one health risk reinsurance contract. If all parties are connected by smart contracts, the reinsurer will be able to get direct access to an insurant’s health data.
Another sound idea is the prevention of reinsurer’s loss access. The key problem here is the loss of variability at different stages of claim handling due to complex documentation processing. Blockchain solves the issue by recording the loss estimate history for each contract. It enables better liability tracking and difference solving.
Generative AI for insurance companies
Conversational AI has become a key component of digital insurance operations, helping insurers automate customer service, policy administration, claims intake, and sales support. Insurers increasingly deploy AI-powered virtual assistants across websites, mobile apps, messaging platforms, and contact centers to provide around-the-clock assistance. These systems can answer policy questions, process service requests, collect claims information, deliver renewal reminders, and guide customers through routine transactions.
For example, Lemonade (a US-based property, pet, and casualty insurance company) sells insurance using an AI-first, app-based model, and the most distinguishing feature is the AI-powered virtual assistant Maya, which entirely replaces the traditional insurance intake process. She collects customer data through a natural, adaptive conversation, provides quotes, and handles payments. This is possible because their machine learning model is retrained almost daily.
Maya, Lemonade’s chatbot, sends a quote after asking basic questions.
Recent advances in generative AI have expanded these capabilities beyond traditional scripted chatbots. Modern AI assistants can understand natural language, summarize policy details, generate personalized responses, and support agents in many other tasks.
For example, underwriting and claims teams can use AI copilots to identify missing information, suggest follow-up questions, retrieve relevant policy data, and streamline decision-making. By automating repetitive tasks while keeping humans involved in complex or regulated decisions, conversational AI helps insurers improve operational efficiency, enhance customer experience, and reduce service costs.
Unsurprisingly, specialized AI agents outperform flagship general-purpose models in underwriting data-extraction tasks: Roots AI claims their systems achieve 93 percent accuracy, compared to 80 percent for GPT-5.0 and 84 percent for Gemini 3.0 Pro. Using such “reasoning engines” reduces the average time to decision for standard policies to 12 minutes.
Insurance APIs as an easy pass to innovation
Over-regulation, old-fashioned business models, and the lack of technology talent slow down industry innovation, which is harmful to customer experience. Customers demand a flexible and innovative experience.
The use of insurance APIs (application programming interfaces) addresses this lack of insurer flexibility as they can share information and services with third parties. Companies get an opportunity to suggest better customer experience, create new digital products, increase sales, and try disruptive business models.
For instance, an insurance company with its own API can enter the online travel insurance market and boost sales through the partnership with OTAs (online travel agencies such as Booking.com or Expedia) by cross-selling services to travelers directly from the partners’ website.
On top of that, third-party APIs are used by insurance companies to check customer data to prevent abuse and fraud.
Here are some examples of insurance APIs and APIs that can be utilized by insurtechs:
The Lemonade Public API. The Lemonade Public API is an API developed by the property insurance company Lemonade. The API allows for integration with a variety of digital products (iOS/Android apps and Websites). Lemonade Public API provides a developer with products, quotations, policy creation, and payment functions. The API also suggests a customizable chatbot interface.
Open. Open platform offers a "no-code" and "low-code" approach to embedded insurance, using APIs and SDKs to help brands launch branded insurance products, digital brokers, or comparison marketplaces. It supports bi-directional data flows to enable features like pre-filled forms and indicative quotes.
MyCover.ai. A dedicated embedded insurance API provider that offers "Insurance-as-a-Service" through an all-in-one API and SDK. MyCover.Ai focus on end-to-end insurance experiences, including claim submission, tracking, policy management, and automated renewals, all accessible via clear documentation for developers.
Insurance fraud detection software brings industry to a new level
Fraud is a great calamity of the insurance industry, so fraud detection software is on the rise. A 2022 landmark study conducted by the Coalition Against Insurance Fraud (CAIF) identified that insurance fraud costs Americans at least $308.6 billion every year.
Cloud and mobile technologies can support insurance agents with real-time information to deal with duplicate claims, inflated claims, fake diagnoses, fake dependent family members, mutually exclusive diagnoses, insurant data inconsistency, overpayments, and internal employee scams. For example, a client claims payout for a lost right eye twice or tries to recover from the same property fire by counterfeit documents with a changed date. The system will compare the claim data with the database and identify the fraud. This will reduce costs by increasing operational speed, delivering higher accuracy, and removing the influence of an interested party.
For instance, Shift Technology offers insurance fraud analysts an end-to-end system. The Shift Technology solution goes beyond traditional claim scoring based on probability analysis. Additionally, it provides business users with actionable analytics indicating why the claim looks suspicious. Its SaaS delivery model enables low implementation cost and easy connection to existing operations. According to Shift Technology, their software tool demonstrates 250 percent better fraud identification rate than the market average.
Guidewire's analytics and predictive modeling capabilities help insurers evaluate claims at the point of intake, using machine learning to identify potential fraud, estimate claim complexity, and prioritize cases for review. Low-risk claims may be accelerated through automated processes, while suspicious or high-severity cases can be escalated for specialist investigation.
Insurance marketplace brings product distribution to online space
Long gone are the days when clients had to call or visit the insurance agent to get a policy. The web made people more aware of offerings on the market, and today, anybody can easily compare products, review testimonials, or find special plans that match personal requirements (thanks to AI-driven recommendation engines). Insurance shopping platforms like Insurify, CoverHound, and Policygenius are actively redefining distribution models.
While we’ve discussed multiple technologies that drive disruptive change in the industry, most of them will remain isolated experiments without a solid data strategy and a centralized data management pipeline.
Today, it’s a matter of designing centralized digital pipelines with several core capabilities:
- data ingestion from internal document management systems, enhanced with OCR technologies
- access to third-party and publicly available data via APIs
- powerful ETL (extract, transform, and load) systems capable of structuring data for its further use
- having a single source of truth represented by a data warehouse
- flexible data querying and visualizing capabilities. The best practice here is to centralize data but allow each individual data analyst and data scientist to query this data using the tools that they prefer, those that match the task at hand.
- ensuring HIPAA compliance for life insurance.
Having these steps completed, insurance organizations will have a strong base for the further digital initiatives.
One of examples of analytics providers for the industry is LexisNexis, whose Risk Solutions is a platform aggregating and analyzing large volumes of public records, proprietary datasets, behavioral information, vehicle and property data, and claims histories to generate risk insights and predictive models. Insurers use these solutions to assess applicant risk, verify identities, detect potentially fraudulent activity, automate decision-making, and enhance operational efficiency across the insurance lifecycle.
What should the insurance industry expect?
Digital technologies bring several disruptive trends to the insurance business, such as personalization, the shift to a platform economy, automation, and real-time estimates rather than historical ones. What are the features with the most impact?
- Cost-cutting. A digital insurer gets strong competitive advantages over traditional carriers. Digital transformation can reduce operational expenses and improve efficiency through automation, AI-driven workflows, cloud platforms, and streamlined distribution models. According to BCG, only by using AI-based modernization, Insurers can reduce traditional capital expenditures by approximately 0.5 to 1 percent of gross written premiums (GWP).
- More accurate risk assessment. The digital business model enables better risk assessment and sometimes even aids in preventing insured events when it comes to driving assistance or health and lifestyle monitoring.
- Better customer experience. Digital self-service, conversational AI, omnichannel interactions, instant quoting and real-time claims updates
- Move from reactive to proactive decision-making. Insurance is a data-driven business that ought to consider numerous factors about customers and strongly rely on statistics. However, the industry still leverages historical rather than real-time data. Various wearables and sensors are yet to reach their potential for data streaming and hyper-personalization.
- Expanding portfolio. The wide range of data records allows insurance carriers to cover very specific risks and work with new micro-segments. It also reconsiders the nature of incurrence products (e.g., bring them to a pay-per-use basis).
- Insurers become more insulated from scams. APIs and mobile shorten the time it takes for fraud detection and make the assessment process more rigorous.
The insurance industry is not merely digitizing existing processes—it is fundamentally redefining how risk is assessed, priced, distributed, and managed. Advances in AI, data analytics, connected devices, and digital ecosystems are enabling insurers to move from standardized products and reactive claims handling toward personalized coverage, continuous risk monitoring, and proactive customer engagement.
At the same time, embedded insurance, real-time data sources, and automated decision-support systems are creating new business models and revenue opportunities. The insurers most likely to succeed in the coming decade will be those that can balance technological innovation with regulatory compliance, transparency, and customer trust, transforming insurance from a product purchased after a risk emerges into a service that helps anticipate, prevent, and mitigate risk before losses occur.