Insurance mobile apps: a development and implementation in 2025

Insurance mobile apps are currently the most effective way for insurtech companies to cut costs. As highlighted in research by McKinsey, businesses that use digital tools for their main processes spend 40% less than their traditional competitors. These savings come mostly from automating sales and customer service.

Key findings

• Analysts at McKinsey note that digitisation drops the unit cost of a property policy from €28 to €16. Using artificial intelligence to assess risk improves the loss ratio by 18 percentage points over two years, as seen with Lemonade, whose ratio fell from 85% to 67%.
• Data gathered by the Open & Embedded Insurance Observatory shows that embedded insurance sold through mobile APIs cuts customer acquisition costs by 75%, bringing the price down from £200 to £50. Conversion rates in this model hit 10–20%, compared to just 1–3% in traditional channels, as confirmed by Insurance Curator.
• A report published by Carrier Management confirms that drivers with good telematics scores present a 30% lower accident risk. Edge computing on devices allows companies to calculate premiums instantly.
• McKinsey forecasts that eventually 95% of property policies will be processed straight-through (STP). BCG confirms that automating claims reduces handling costs by 20% and makes the process twice as fast. Bain & Company estimates savings on straightforward claims reach 30–50%.
• Wrapping old systems in an API layer (hollowing out) costs between €10m and €30m and takes 3–5 years, as McKinsey recommends. This offers an alternative to replacing the core system entirely, which would cost €50–150m, and it cuts total cost of ownership in half, as documented by Openkoda.
• Meeting DORA regulations requires a budget of €25–150m, according to estimates from Centraleyes. AI systems used for risk assessment in life insurance face strict audits as high-risk solutions, as analysed by Blue Arrow. Fraud detection systems, which save $80–160bn, fall under lighter rules, as noted by Risk & Insurance.
• Using AI to settle claims offers global savings of around $100bn — mostly by cutting unjustified payouts and process errors by 30–50%, as estimated by Bain & Company.

Insurance mobile apps market: current trends and forecasts

Directions for insurance market development and new digital technologies

The global market for insurance platforms is gaining massive momentum. Its value hit $26bn in 2024, according to IMARC Group, while The Business Research Company forecasts a leap to $254bn by 2030. Grand View Research puts this growth at an exceptionally fast 53% a year.

The scale of this digital shift is massive. By 2025, premiums generated through digital channels are expected to reach $1.19tn, as projected by Mordor Intelligence.

Investment dynamics in insurtech and the real impact of artificial intelligence

The insurtech sector recently faced a tough period, with funding dropping to $4.5bn in 2024, marking an eight-year low, according to the CB Insights “State of Insurtech 2024” report. Yet early 2025 showed a clear recovery — investments jumped by 63% in the first quarter, as the CB Insights “State of Insurtech Q1’25” report confirms.

Investors have completely changed their approach, with capital now flowing mainly to companies where artificial intelligence delivers concrete profits. Statistics compiled by Electroiq confirm that nearly half of implementation budgets in late 2024 went specifically to AI-based projects.

Three main forces shaping competition in the insurance market

Insurance companies today face three main phenomena that dictate the market and force strategic changes. Consumer habits are shifting rapidly — we already buy 47% of policies online, and in 2025 nearly a third of customers decided to switch providers, as reported by ProgramBusiness. This shift turns mobile apps into vital tools for building loyalty, rather than just basic sales channels.

The second factor involves a strong push from technology giants. A report published by the Bank for International Settlements (BIS) notes that out of 18 surveyed BigTech firms, 16 hold broker licences and 5 run their own insurance companies.

We must also consider the rise of embedded finance, which completely alters the distribution model. To illustrate, bolttech generates about $65bn in annual premiums by working with 700 partners, while Cover Genius reports year-on-year growth of 107%.

How an insurance mobile app genuinely increases insurer profits

Main sources of savings in the mobile insurance sales channel

As noted earlier, digitalised insurance firms report 40% lower cost ratios than their analogue rivals, as documented by McKinsey. The difference stems primarily from reduced spending on distribution and support. The McKinsey “Insurance Productivity 2030” report further shows that digitising policy management drops the unit cost of a P&C policy from €28 to €16, roughly a 43% decrease.

The impact of artificial intelligence on improving financial results

Applying unstructured data to risk assessment brings measurable operational benefits. Analysts at BCG show that using this information in underwriting improves the loss ratio by 3 percentage points — the insurer pays out that much less in claims relative to collected premiums, which directly boosts profit.

Root Insurance achieved a gross loss ratio of 58.9% thanks to mobile telematics. Lemonade cut its loss ratio from 85% in 2023 to 67% by mid-2025, an 18-point improvement in under two years. Scandinavian insurers cut their expense ratio by 25% after moving from outdated IT to modern platforms, as McKinsey records.

Much cheaper customer acquisition through embedded insurance

The embedded insurance model cuts customer acquisition costs by 75%. This translated to a real drop in spending from £200 to just £50 per person in the UK. Such efficiency stems from high conversion rates, which hit 10% to 20% here, while traditional channels usually score between 1% and 3%.

Lemonade steadily increases its average premium, which grew from $369 to $403 between 2023 and the third quarter of 2025. The company maintains a customer lifetime value to acquisition cost ratio above 3 to 1. Root Insurance sees similar success in motor insurance, as do Scandinavian insurers in the property segment. These companies successfully combine low marketing costs with better portfolio selection through advanced data analytics.

A low entry cost only solves half the puzzle. The true value of the mobile channel relies on constantly gathering information. Modern analytical models use this information to improve portfolio quality and price risk more accurately. We outline this entire mechanism later in the document.

The technological design of a modern insurance mobile apps

We will look at modern risk assessment methods based on mobile data and fresh approaches to claims reporting below. The text also analyses effective ways to build user habits and the role of the smartphone as an extremely precise source of hazard information.

Technological optimisation forms a major part of this discussion — involving the smart division of computing tasks between the device itself and the cloud.

Modern underwriting and risk assessment in the mobile model

Traditional risk assessment relies almost entirely on information the customer declares when buying a policy. This process is static and often lacks precision. Using a mobile app completely changes these rules because it opens the door to a steady stream of current data.

We gain insight into actual user behaviour and access highly valuable contextual information from external sources. We can monitor changes as they happen and match the offer much better to a person’s actual risk profile.

A modern information gathering system rests on three simple steps. We ask the user to fill out just a few fields, rather than forcing them through a long 20-question form. The system pulls the rest of the data itself from official databases, like vehicle registries or geolocation systems. This allows us to instantly verify the car’s condition or the home address.

At the same time, we tap into the capabilities of smartphones. Sensors like the GPS or accelerometer help us understand how a driver behaves on the road and how often they use the app. We combine all this with real-time external information, checking insurance history, creditworthiness, or even the weather conditions the customer is driving in.

This approach represents the gold standard now, especially in the US, where sensor data already features in over 21 million policies — as the Carrier Management report confirms. Nearly every second new car policy at Progressive relies on this remote analytics. Grand View Research puts the entire telematics market at over $4.4bn, growing by more than 22% a year.

Analytical system architecture and premium pricing speed

One of the biggest technological decisions involves deciding exactly where we process the gathered information. We can run analytical models straight on the user’s smartphone instead of sending everything to distant servers. This setup lets us price a premium in a split second because we avoid any network delays.

Complex processes, like assessing damage with computer vision, need the computing power of the cloud. A hybrid architecture works best here — telematics run on the phone, while deep predictive models operate in the cloud.

Using telematics data to build a lasting market advantage

An insurer with a year’s worth of telematics data holds unique knowledge about a customer that a rival without an app simply cannot copy. This creates an information barrier that marketing budgets alone cannot overcome. This technological edge means the company knows exactly who it is insuring and how to accurately price every single premium.

Mobile claims reporting (FNOL) and handling automation

The way a company handles a claim heavily influences its relationship with the customer. This stage is also the easiest and most effective one to automate. Statistics show, however, that less than 10% of claims currently follow a fully automated path, and 60% of insurers do not use these solutions at all — as Insurance Thought Leadership reports.

McKinsey predicts that 95% of property and motor policies will eventually be processed without human involvement. Dropping manual work on simple events cuts costs by 30% to 50% per claim, as estimated by Bain & Company. These savings directly boost the financial metrics we discuss later in the text.

Four key phases of mobile insurance claim settlement

We split the whole mobile claims process into four main stages. The customer submits the report directly via their phone during the registration phase. Artificial intelligence algorithms help them document the event, instantly analysing photos, videos, and voice descriptions converted to text.

The model then classifies the claim in real-time during the selection phase. Simple, clear-cut cases go straight to the automated track. More complex ones pass to an assessor along with the complete set of gathered data.

The next step is the decision, which often allows for an instant valuation. Image analysis systems linked to repair cost databases can approve and pay out funds in minutes rather than days. The process ends with a monitoring phase, where the app keeps the customer updated on their case status through push notifications. This approach offers complete transparency, something traditional handling methods often lack.

Success metrics and KPIs in claims automation

The time from the incident to sending the report should take less than 5 minutes. The share of automatically processed claims should pass 30% in year one and 60% by year three. Insurers with the best results hit a Net Promoter Score above 50 points in this area. Experts at BCG confirm that automating this process cuts handling costs by 20% and halves the cycle time.

Building consumer habits and effective user retention

Insurers interact with their customers less frequently than anyone else in the financial sector. Contact usually happens just once or twice a year, mainly when paying a premium or renewing a policy. A mobile app must shift this dynamic and give the user a real reason to open it much more often.

Reminders and reward systems as an effective loyalty mechanism

The app sends regular short alerts and calls to action instead of limiting contact to an annual event. A warning about an approaching storm prompts people to secure their homes. Safe driving rankings reward drivers for high scores in the telematics system. The system can also spot major life changes and suggest updating a policy, for instance after moving house. McKinsey analysis reveals that insurers who use artificial intelligence best in customer communication see 20% higher conversion and 15% higher premium income.

Contextual insurance sales in business partner apps

Insurance should appear exactly when a customer makes financial decisions — inside an e-commerce basket, while leasing a car, or when buying a plane ticket. The whole process of picking a policy must take just two clicks and happen instantly, without waiting for a new page to load.

The context of the purchase heavily influences sales success. Conversion in the embedded model reaches 10% to 20%. Traditional digital channels, on the other hand, usually manage between 1% and 3%, as Insurance Curator documents.

The smartphone as a precise, primary source of risk data

A smartphone packs a set of sensors that a customer carries every single day. This gives the insurer access to information that once required installing special hardware in vehicles.

Insurance mobile apps: using mobile sensors to assess driver safety

Data from the device flows into scoring models and is processed as it happens. The accelerometer logs sudden braking, while the gyroscope measures cornering force. The GPS tracks routes, speed, and driving time. The barometer records altitude changes, and the proximity sensor catches when someone uses their phone behind the wheel. The Carrier Management report confirms these measurements work well — drivers with good telematics scores at Progressive cause accidents 30% less often.

Insurance mobile app user behaviour as a new source of underwriting data

How a person uses the app provides key clues about their risk profile. It matters how often they check their policy, how much time they spend reading the coverage terms, and how quickly they report a minor bump. Data shows that people who look after their documents and regularly check their cover tend to cost the company less.

These patterns create an entirely new pool of knowledge to use during risk assessment. The insurer can calculate the premium much more accurately before the first random event even happens.

Processing insurance data: device versus cloud differences

The AI architecture in an app needs a deliberate division of tasks. Where the computing happens decides the speed of the service and the cost of running it.

Fast on-device analysis (edge computing) in mobile apps

Some processes take place right on the device. Telematics scoring evaluates risk while driving. Initial image analysis checks photo quality and spots car parts before sending anything to the server. Interface personalisation also runs locally. This setup guarantees instant reactions and an offline mode. Raw data does not have to leave the smartphone, which makes things safer for the customer. We must remember that model sizes are limited, usually up to 50 MB, and older devices lack processing power.

Advanced cloud computing for the insurtech sector

Complex operations need the cloud. Advanced damage valuation based on images uses models larger than 1 GB. The cloud also hosts extensive underwriting systems that analyse hundreds of variables. It runs generative AI for customer chats and fraud detection systems that rely on combined market data. Scalable computing power and access to full reference databases are big advantages here. Data transfer costs and strict rules like DORA and GDPR remain significant challenges.

Hybrid architecture as the ideal insurance data analysis model

A hybrid architecture is the recommended approach. It relies on a shared feature store synced between the smartphone and the cloud. Lightweight models under 20 MB work straight on the device. They send their analysis results to the cloud in the background to feed global systems.

Drawing on Gartner projections, Accenture expects AI agents to handle 15% of daily operational decisions autonomously by 2028. In the insurance trade, this means approving claim payouts automatically and adjusting policy prices dynamically to fit the current situation.

Managing APIs and the role of event-driven architecture in service digitisation

The mobile app acts as the face for a whole ecosystem of background services. Building an API-first technology base is essential to successfully expand the embedded insurance model. This matters greatly because 74.2% of this market operates exactly this way, as research by Mordor Intelligence shows. This setup lets an insurer smoothly link its services with the platforms of external partners.

The integration layer in modern insurance management systems

A modern system managing processes via API has to handle several core tasks. The product catalogue in such a setup dynamically adjusts the offer to a specific partner and the buyer’s situation. Pricing and issuing the policy must take less than 3 seconds so it does not slow down the sale.

The system also runs the entire claims path — from receiving the report and tracking progress to ordering the final payout. Extra details like geolocation data, IoT sensors, and external scoring databases automatically enrich all these steps.

Instant response to insurance events in real time

Event-driven architecture shines wherever the system needs to react instantly. Any shift in the risk profile, like a new home address or a change in driving style, should trigger a fresh premium calculation automatically. Safety works the same way — when the phone’s accelerometer detects a crash, the system must start the claims process right away.

A lack of user activity for over 90 days should spark an automatic campaign to keep the customer engaged. McKinsey notes that companies applying this event-based personalisation cut the costs of acquiring and onboarding new people by 20–40%.

The importance of DevOps processes and security in building insurance mobile apps

Mobile apps demand a complete break from old working methods. The release cycle shifts from projects lasting many months to short, two-week sprints. Every change has to pass checks in the App Store and Google Play before reaching users in stages. Solid DevOps processes become absolutely necessary in this model to maintain control over system stability.

Change management and advanced testing of financial software

Developing software well requires feature flags. These let teams test different options live in production without risking the whole system. Canary deployments, meanwhile, allow an automatic rollback to an older app version if key metrics start to drop.

We secure the code through constant SAST and DAST tests. We place special focus on mobile-specific defences like iOS Keychain or Android Keystore. Regular penetration testing completes the process, checking how the app handles real hacking attempts.

The impact of strict legal regulations on DevSecOps culture development

Currently, 56% of companies use an integrated DevSecOps approach — as DevPro Journal reports based on GitLab research. The financial and insurance sectors are adopting these standards the fastest. The DORA regulation, active since January 2025, legally requires insurers to make their systems completely stable and secure, as outlined by Centraleyes. In practice, this means smooth operations and top-tier security standards form the foundation of every project an insurance company builds.

Modern technological architecture for an insurance mobile apps

Choosing a technical architecture goes beyond just picking technology. It is a business decision that directly affects profitability and the ability to compete for market share. A modern system structure lets an insurer react to rival moves in days, not months.

Safe modernisation of core (legacy) systems without paralysing the company

Replacing outdated systems brings solid financial gains. It drops IT costs per policy by 41% and can boost revenue by up to 25%. Time-to-market for new products shrinks three to four times over as a result — as Equisoft documents.

McKinsey suggests a strategy called hollowing out instead of the risky move of replacing the whole core system at once. This means wrapping the old legacy infrastructure in a modern API layer. The firm gains the features of advanced mobile and web apps without having to shut down old databases immediately. This method keeps operations running smoothly and gives the business time to migrate data slowly and safely to the new setup.

Cloud architecture and microservices (Composable Architecture) in insurtech

Gartner anticipates that over 70% of businesses will move to cloud solutions by 2027. Modern architecture relies on microservices housed in a cloud-native setup, where an API gateway manages all communication between the different systems.

Upgrading is urgent. Fully 52% of life insurance providers view outdated technology as their main barrier to growth, and just 5% of companies in this sector currently deliver a high-quality user experience — as the Capgemini “World Life Insurance Report 2025” reveals.

Security standards (Zero Trust) and cloud solution deployment

Spending more on technology and changing approaches to security are central to insurers’ strategies. The Zero Trust model, built on the principle to “never trust, always verify,” is now standard for nearly a third of global organisations. The banking, financial services, and insurance (BFSI) sector leads this shift, making up 28% of the market for these tools — as data gathered by Statista shows.

Insurers’ infrastructure is moving to the cloud incredibly fast, with its share growing 23% a year. While the cloud held 59% of resources in 2025, Gartner projects it will handle 72% of operations by 2029, a finding supported by Brightlio. Global IT spending in insurance is set to hit $227.7bn in 2025.

Insurance mobile apps and traditional agents: solving channel conflict effectively

Even though 47% of policy purchases happen digitally, agents still play a major part in selling complex products. A dual-speed operating model bridges these two worlds. The mobile app takes over basic products like motor, travel, or electronics insurance. Agents then focus on advising clients on life and business coverage.

The app does not replace the broker. It becomes their working tool. AI-driven pricing and mobile CRM features help the agent match offers to client needs faster and more accurately during face-to-face meetings.

Legal regulations and risk management in insurance technologies

The solutions described earlier operate within strict rules. This section looks at the regulations directly shaping how we deploy new tech. Compliance costs take up a big part of the transformation budget and directly affect the return on investment (ROI) model we discuss later.

The impact of the DORA regulation on insurers’ operational resilience

These rules took effect on 17 January 2025 with no transition period. This means massive spending for large financial institutions — designing a strategy alone costs €5–15m, while full implementation demands up to €150m, as estimated by Centraleyes. The PwC “Next in Insurance 2025” report confirms the scale of this challenge, noting that 40% of entities already assign over 7 full-time staff solely to handle DORA programmes.

Fines for breaking the rules are harsh, reaching 2% of global annual turnover. Systemically important firms must also pass strict threat-led penetration tests (TLPT) every three years. This brings the mobile app under full scrutiny, from threat modelling down to tight management of cloud service providers.

EU AI Act requirements for risk assessment systems and artificial intelligence

The law has been active since August 2024. The strictest rules for high-risk systems will apply fully from 2 August 2026. An analysis published by Blue Arrow details how these regulations directly classify machine learning models used for risk assessment and premium pricing in life and health insurance as high-risk solutions.

Failing to keep proper documents or oversee these algorithms can lead to fines of up to €15m or 3% of annual turnover. Fraud detection systems avoided this label, making them much easier to roll out. By August 2026, every algorithm used in life and health underwriting must offer fully explainable decisions and guarantee real human control over the entire process (human-in-the-loop).

The NIS2 directive and security in the insurance technology supply chain

Insurers do not answer to NIS2 directly because DORA covers them, as DLA Piper explains. We still see the directive’s effects in the supply chain and when building cyber risk profiles in underwriting. Technology providers working with insurers must meet specific standards or face fines up to €10m or 2% of turnover, as Akamai notes. In practice, this forces mobile and cloud tech suppliers to keep their security incredibly tight.

Financial consequences of data breaches in the financial sector

The average cost of a single data leak in the financial sector sits at $6.08m — 22% above the global average, according to the IBM “Cost of a Data Breach 2024” report. Organisations using AI and automation to defend their systems save about $2.2m per incident, as further IBM research shows. This offers a powerful reason to build in AI-driven security tools right from the start of a project.

Return on investment (ROI) model for insurance mobile apps for 2025–2030

This model estimates the investment profile and expected return on capital for a mid-sized insurer (GWP $1–5bn) adopting a mobile-first strategy. All figures are in euros, relying on industry benchmarks from McKinsey, BCG, Gartner, and Deloitte.

Estimated capital expenditure for app development over 3 years

Building an iOS and Android app along with a backend and API integration takes serious money. The basic option uses an off-the-shelf COTS platform with a custom frontend, costing between €3m and €8m. A more aggressive approach means a full custom build and creating an SDK for partners, pushing the bill up to €15–25m. Gartner data shows normal IT spending for insurers ranges from 3.7% to 5% of GWP. For a firm with €2bn in written premiums, this equals an IT budget of $74–100m, with mobile development usually eating up 5% to 10% of that total.

Costs of modernising technology and maintaining artificial intelligence

Upgrading the core system costs €10–30m using the hollowing-out strategy recommended by McKinsey. Replacing the whole infrastructure runs between €50m and €150m, as documented by Equisoft. Spreading the API layer migration over 5 years is the most sensible path. Building the AI backend covers risk models, fraud detection, and claims automation — needing an initial €2–5m, plus €1m to €3m a year for upkeep. Forecasts from Gartner back up the scale of these investments, expecting global AI spending in insurance to hit $15.9bn by 2027.

Budget required for legal compliance and cloud infrastructure maintenance

Meeting the demands of DORA and the EU AI Act brings costs of €5–15m, as Centraleyes estimates. Keeping up with DORA alone adds €1–3m in expenses every year. The AI Act forces extra spending on documentation and making algorithms explainable — €1–5m in the first year and up to €2m annually after that. Running a multi-cloud setup costs between €2m and €5m a year, while forecasts project the cloud will handle 72% of the insurance market by 2029.

Total budget for a complete digital transformation of an insurer

Modernising through the hollowing-out approach, covering a ready-made app and selected AI tools, costs between €25m and €50m. A full transformation — building a custom core system, a bespoke app, and a massive AI backend — demands an investment of €80–200m.

Profitability analysis and real impact on insurers’ financial results

Adopting a mobile-first strategy directly improves financial performance across several major areas.

Lowering the COR indicator through modern technologies

A modern tech stack helps cut the combined operating ratio (COR) by 3 to 5 percentage points over 3 to 5 years, as both McKinsey and experts at BCG confirm. For an insurer writing €2bn in premiums with a 96% COR, a 4-point improvement creates €80m in extra technical profit every year. This result comes from a mix of factors: distribution costs fall by 40%, underwriting improves and drops the loss ratio by 3 to 18 points (as demonstrated by Lemonade), and claims automation cuts handling expenses by 20%.

Effective reduction of the total operating cost ratio

If the cost base is €200m (a 10% cost ratio), dropping expenses by 15% in year three brings in €30m of savings annually.

Clear reduction in customer acquisition cost (CAC) in the embedded model

The embedded insurance model drops acquisition costs by as much as 75%, as the Open & Embedded Insurance Observatory documents. Imagine a current CAC of €150 and 100,000 new policies a year — dropping that cost to €50 saves €10m annually. At the same time, raising conversion from 2% to 12% secures six times more policies from the exact same pool of potential buyers.

Increasing retention and customer lifetime value (LTV) through the app

A mobile app boosts retention and makes cross-selling work better, raising the average premium per customer by 5% to 10% a year — as results at Lemonade show. With 500,000 customers and a €400 average premium, an 8% increase generates €16m in new premiums.

Multi-million savings thanks to fraud detection systems

AI systems designed to catch fraud save 3% to 5% of total claim payouts, as highlighted by both Risk & Insurance and FintechOS. If yearly claims hit €1bn, this saves between €30m and €50m every single year.

Limiting risk through the hollowing-out strategy

This method requires spending €25–50m and breaks even after 2 to 3 years. By the third year, annual benefits reach €60–100m, with the internal rate of return (IRR) hitting 80%. This option safely manages implementation risks, but it might not deliver total technological dominance over market leaders.

Financial benefits flowing from a full transformation strategy

This path needs an investment of €80–200m. It creates benefits of €130–250m a year, visible from year four. Payback takes 3 to 5 years, with an IRR between 25% and 50%. The company builds a totally unique architecture this way, though it forces a deep reorganisation of processes and a complete overhaul of the working culture.

The future and forecasts for Insurance mobile apps (2026–2030)

Probable directions of insurance market development

Conservative digitisation and slow changes. In this model, tech moves slowly and AI finds only limited use. Apple and Google just distribute the products, while insurers hold onto 85% of the market. The embedded insurance segment grows to $350bn GWP, as the BIS report forecasts. The COR improves by a modest 1 to 2 points. The mobile app stays a simple add-on to normal sales channels.

Hyperautomation of processes using artificial intelligence. This scenario assumes massive use of AI agents, slashing operating costs by 30% to 40% by 2030 — as McKinsey forecasts. Generative AI saves over $100bn in property claims by spotting fraud better and cutting handling costs, according to Bain & Company. Tech leaders see their COR drop below 90%, and the mobile app becomes an independent decision centre that underwrites policies and pays out claims in real-time.

Total dominance of platforms and embedded insurance. Insurance merges into wider digital ecosystems. By 2033, this segment could take up 15% of the global market, reaching $1.1tn GWP — as the Open & Embedded Insurance Observatory projects. Insurers turn into suppliers of capital and infrastructure, while super-apps and e-commerce platforms take over the customer relationship. An insurer’s own app loses value in this setup, with software development kits integrated into external partners’ systems taking its place.

The impact of upcoming legal regulations on digital rollout schedules

DORA and the AI Act set the main boundaries for the sector, especially regarding high-risk systems, as analysed by Blue Arrow. Insurers have until the end of 2026 to rebuild their API infrastructure, with experts at Faegre Drinker warning that the next 18 months offer the final window to get technically ready for open access to customer data.

Main strategic risks tied to technological transformation

Implementation barriers stemming from outdated IT systems. Complex legacy systems and a shortage of machine learning experts often cause budgets to blow out. Breaking projects into short, 6-month stages with clear go/no-go checkpoints solves this problem.

Organisational resistance and lack of skills in insurance teams. Shifting to a mobile-first approach shakes up existing processes and staff skills. Without board backing, or if staff resist, a project will stall regardless of how good the architecture is. A firm must spend money teaching new skills internally and communicate changes clearly.

The risk of depending on one technology supplier (Vendor lock-in). Picking a single cloud provider or an off-the-shelf COTS platform creates a dangerous dependency. To stop costs jumping in the future, companies must build a multi-cloud architecture from day one and use open data exchange standards.

Legal uncertainty surrounding new EU regulation interpretations. Fresh readings of the AI Act or stricter rules on where data must live could force firms to rebuild finished systems. The only defence is building systems in modules, letting a business swap out specific parts quickly without shutting down the whole company.

Summary and recommendations for insurance companies

The most likely path forward mixes high AI automation with a dominant embedded channel. Playing it safe is actually risky — a conservative approach ignores the speed of change pushed by generative AI and the growth of tech giants.

An evolutionary model works best for an insurer writing $1–5bn in premiums. It keeps costs and risks under control. The first step means launching a mobile app built on a COTS platform with a unique custom frontend and starting telematics in the motor segment. The operational focus has to be automating simple claims reporting, aiming for over 30% touchless processes, alongside linking up with the first embedded partners. These steps must happen while meeting DORA standards and getting ready for the AI Act.

The next phase involves adding advanced generative AI models to help settle claims and building the company’s own software development kits for business partners. This will deepen the information advantage by using collected behavioural data like telematics. Pushing the COR down by over 4 percentage points ultimately brings in more than €80m a year. This strategy pays for itself quickly, within 2 to 3 years, and leaves the door open for a massive tech overhaul later depending on how fast the market shifts and what operational results the firm hits.

Frequently Asked Questions (FAQ)

How does an insurance mobile app create financial savings for providers?

An insurance mobile app cuts operational costs significantly by automating sales and customer service. Digitalized firms spend 40% less than traditional competitors. Digitizing policy management drops the unit cost of a property policy from €28 to €16, while embedded insurance cuts customer acquisition costs by 75%.

What is the most efficient process to handle a car insurance claim?

The most efficient mobile claims settlement relies on automation through four key phases:

• Registration: The customer submits the report directly via their phone.
• Selection: AI instantly classifies the incident, routing simple cases automatically.
• Decision: Image analysis systems approve and value payouts in minutes.
• Monitoring: The app updates the customer via push notifications.

How does a mobile device assess risk for car insurance policies?

A mobile device provides convenient access to a steady stream of real-time data to evaluate risk. Instead of relying on static forms, systems use mobile sensors like GPS and accelerometers to monitor driving behavior. This telematics data is combined with external information to accurately price premiums.

What is the recommended technological architecture for an insurance app?

The ideal setup is a hybrid architecture utilizing a shared feature store. Lightweight models under 20 MB run directly on the mobile device for fast, offline analysis. Meanwhile, advanced cloud computing handles complex operations, such as deep predictive modeling, large image valuation, and running extensive underwriting systems.

Can embedded insurance provide a quick and easy way to acquire customers?

Yes, embedded insurance is highly efficient, reducing customer acquisition costs from £200 to just £50 per person in the UK. By offering coverage exactly when a customer makes financial decisions—such as inside an e-commerce basket—conversion rates hit 10% to 20%, far exceeding traditional digital channels.

How does user behavior within the app influence risk assessment?

How a person uses the app provides crucial underwriting data. Analyzing user behavior, such as how often they manage their account, read coverage terms, or check policy documents, helps calculate premiums accurately. Customers who regularly monitor their information pose less risk and cost the company less.

Is an insurance app an easy way to build customer loyalty?

Yes, an application provides an efficient method to increase retention. Instead of waiting for an annual renewal to contact users, the system sends regular alerts, like storm warnings, or rewards safe driving. This convenient access to services boosts conversion and premium income.

When customers download the app for car insurance, does it replace traditional agents?

No, the app does not replace the broker but becomes a quick tool for them. While the mobile platform handles basic cover like motor policies, agents use AI-driven features to advise clients on complex life insurance. This ensures quick and easy access to accurate offers.

If a friend gives me a QR code to download the app and say goodbye to the website, what extra features do I get when I log into my account to view policy documents?

Once you download the software and link your account, managing your documents and insurance policies becomes highly accessible. Rather than needing to contact support, you gain advanced tools. The system provides real-time storm warnings, safe driving rankings, and instant claims reporting directly from your account.

With the app installed via a QR code on my tablet, how does the information provided impact a quote for optional extras, my claims bonus, or my renewal before I sign or cancel?

The platform’s high usability leverages your device’s sensors to assess risk and instantly calculate a precise quote. Instead of waiting to contact an agent to sign or cancel standard insurance policies, you receive dynamic pricing based on your behavior, allowing you to seamlessly manage your account and downloads.

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