Turning Mobility Data into Insurance Value and New Ecosystems

Software‑Defined Vehicles (SDV), AI and digital twins are reshaping how value is created in both mobility and insurance. Vehicles are no longer just assets that depreciate once they leave the showroom; they are evolving continuously updating, data‑rich platforms that can power new services, risk models and ecosystem collaborations across industries.

In this article, we explore three perspectives that matter for automotive and insurance leaders:

1. How SDV and Insurtech trends are structurally converging

2. Why data monetization is still constrained by trust, governance and legacy architectures

3. How NTT DATA is building neutral, governed data platforms and concrete use cases—from vehicle health to brain health and urban safety—to unlock cross‑industry value

Our goal is not only to describe the future, but to show what can be done now to turn mobility data into sustainable business and societal outcomes.

1. Industry trends: SDV as a catalyst for data‑driven value

Over the next decade, SDV will be one of the main growth engines of automotive IT. NTT DATA’s global SDV analysis estimates an addressable SDV IT market of around USD 84 billion by 2030, with a CAGR above 7%. At the same time, digital IT spend in automotive—cloud, software, AI—is projected to grow at around 9% CAGR, steadily gaining share versus traditional IT.  

Technically, SDV architecture centralizes compute and decouple hardware from software. Commercially, they enable:

• Over‑the‑air (OTA) updates and feature unlocks, extending value creation far beyond the initial sale

• Subscriptions and post‑sale services that turn vehicles into recurring‑revenue platforms—an evolution highlighted in recent mobility analyses where OTA and subscriptions are becoming a significant slice of post‑sale revenue.

In parallel, the insurance sector has undergone its own data‑driven transformation. Despite a cooling funding environment since 2021, more than USD 40 billion has been invested in Insurtech over the last four years, shifting the focus from experimentation to measurable business impact. Insurers increasingly see AI, digital twins and connected data as core to profitable growth, not just innovation pilots. This creates a structural convergence:

• New assets and risks – EV batteries, complex software stacks and autonomous functions demand new forms of risk modeling.

• New behaviors – Sharing, subscription and “usage over ownership” require usage‑based, context‑aware coverage instead of static annual policies.

• New data – SDV telemetry, vehicle health, environmental signals and driver behavior can power far more granular, dynamic insurance products—if they can be shared and governed across industry boundaries.

The direction of travel is clear: mobility and insurance are becoming two sides of the same data‑driven ecosystem.

2. Challenges: trust, governance and the monetization gap

If the opportunity is so compelling, why are most cross‑industry data initiatives still limited to pilots?

Data sensitivity and ethical governance

From an individual’s perspective, SDV data is highly sensitive. It can reveal where and when people travel, how they behave in traffic and how their typical driving patterns evolve over time.

For insurers and mobility operators, these signals are extremely valuable for safety, pricing and preventive services. But without robust safeguards, they raise legitimate societal questions:

• How is personal data anonymized or aggregated so that individuals cannot be re‑identified, while preserving predictive value?

• How are fairness, explainability and accountability embedded into AI‑driven decisions that may influence premiums or access to coverage?

Without clear answers, customers and regulators will remain cautious about large‑scale data monetization, especially when health‑adjacent insights—such as long‑term driving behavior changes—are involved.

Data silos and legacy architectures

A second barrier is structural. Most SDV and risk data is locked inside OEM, insurer or infrastructure silos, tied to legacy systems that were never designed for ecosystem‑wide sharing.

The result:

• Bilateral, bespoke integrations that do not scale

• Fragmented, inconsistent data that is hard to reuse across use cases

• Limited ability to build shared “negative data” pools—covering failures, near‑misses and rare events—that could dramatically improve safety and risk models for everyone.

The stakes are particularly evident in climate and catastrophe risk. In 2024, global natural catastrophes generated USD 318 billion in economic losses, of which only USD 137 billion were insured—implying a protection gap of around 57%. Bridging such gaps will require data ecosystems that go far beyond traditional industry boundaries.

In short, the monetization gap is less about technology maturity and more about trust, governance and incentives.

3. NTT DATA’s role: neutral data backbone with built‑in governance

Addressing these challenges requires an actor that is both technically capable and commercially neutral across industries. This is where NTT DATA positions itself.

A neutral, cross‑industry data exchange backbone

NTT DATA operates at the intersection of mobility, insurance, energy and public sector, with long‑standing project experience and domain expertise in each field. Based on this position, we are designing cross‑industry data platforms with four defining characteristics:

1. Anonymization and masking by design
   – De‑identification pipelines that strip or pseudonymize personal identifiers and apply statistical techniques to prevent re‑identification.

2. Fine‑grained usage governance
   – Policy engines that enforce “who can do what, for which purpose, for how long” across multiple data providers and consumers, with full auditability.

3. Embedded ethical and regulatory frameworks
   – Governance models aligned with fairness, explainability and accountability principles from the outset, not as afterthoughts.

4. Neutrality and multi‑tenant architecture
   – Platforms operated by a global IT provider, not by a single OEM, insurer or hyperscaler, ensuring no single player can dominate the ecosystem.

A concrete example is the Battery Traceability Platform showcased at AUTOMOTIVE WORLD 2026, which links OEMs, material suppliers, reuse and recycling players, and regulators around EV battery lifecycle data. It protects each stakeholder’s proprietary information while enabling circular‑economy use cases such as second‑life batteries and compliance reporting.

Insurance‑ready analytics and digital twins

On top of this backbone, NTT DATA brings advanced analytics and digital twin capabilities tailored to insurance:

• Climate and catastrophe risk twins that have achieved 10–20% improvements in prediction accuracy and around 20% reductions in assessment time in real projects

• Crime risk maps for vehicle theft with up to 98% prediction accuracy at 1km grid level

• Operational twins in manufacturing that deliver around 30% downtime reduction and 15% productivity gains, illustrating the broader impact of twin‑driven operations.

These assets can be extended to SDV‑centric risks—such as battery health, ADAS performance and autonomous driving scenarios—while preserving data sovereignty across all participants.

4. Use cases: where shared data turns into real value

4.1 Brain Performance – insights into driver brain health from everyday driving

One of the most representative examples of creating new value from mobility data is Brain Performance. This AI‑based approach analyzes everyday driving behavior data to generate insights into a driver’s brain health and driving‑related performance over time. Its core concept is as follows:

• Leverage SDV‑derived driving behavior data, such as speed and acceleration, to analyze everyday driving patterns

• Establish a personalized baseline of each driver’s “typical” driving patterns

• Identify ongoing changes in driving behavior patterns relative to that baseline, which may suggest shifts in attention, reaction characteristics, or cognitive workload at a behavioral level

While the solution is not designed to perform diagnosis, it provides behavioral insights that can support several key objectives:

• Helping drivers and fleet operators maintain safe driving performance over longer careers

• Enabling insurers, in the future, to potentially leverage these insights in designing preventive or engagement‑oriented services—such as coaching, periodic check‑ups, or route‑planning support

• Supporting societal efforts to enhance the well‑being of aging and professional drivers through a non‑intrusive, data‑driven approach

Multiple pilot projects conducted with partner companies have confirmed the feasibility and usefulness of deriving such brain‑health‑related indicators from real‑world driving data. These pilots have demonstrated that the approach can help organizations monitor long‑term changes more naturally within daily operations, and can be incorporated into broader safety and driver‑support programs

4.2 Digital twins: from vehicle health to systemic risk management

A second family of use cases centers on digital twins:

• Vehicle and component twins predict anomalies before failures occur, optimize maintenance scheduling and support XR‑guided inspections that make checks faster and more consistent.

• For insurers, combining these twins with policy and claims data enables:

• More precise risk selection and pricing based on actual usage and condition

• Faster, more objective claims assessment via simulation of damage scenarios

• Portfolio‑level analytics that reveal systemic exposures, such as shared software vulnerabilities across fleets or regions.

In climate and catastrophe risk, digital twins have already demonstrated error reductions of around 10% and evaluation‑time reductions of around 20%, improving both underwriting and capital allocation.

4.3 Urban safety and sustainable ecosystems

Cross‑industry data also unlocks value beyond individual policies or vehicles:

• Urban safety analytics – Combining SDV telemetry, roadside sensors and satellite data yields city‑level risk twins that can identify dangerous intersections, wrong‑way driving hotspots and flood‑prone areas. In real deployments, traffic and crime risk models have reached prediction accuracies of up to 98% and guided targeted interventions.

• Sustainable urban life – The Battery Traceability Platform links mobility and energy ecosystems, supporting EV reuse, recycling and V2X scenarios in line with global sustainability goals.

• Embedded and parametric insurance – As sensor data and digital twins provide objective, real‑time views of events, coverage can be triggered automatically by observed conditions (e.g., water level, wind speed, traffic disruption), reducing friction and increasing transparency for customers.

Across these use cases, the common thread is clear: data moves from a by‑product of operations to a shared economic asset, governed in ways that respect privacy, sovereignty and regulatory expectations.

5. Call to action: co‑creating the next data ecosystems

For both mobility and insurance leaders, the question is no longer whether SDV‑driven data ecosystems will emerge—it is who will help design and govern them.

• For automotive and mobility players
  The transition to SDV, OTA and ecosystem services is already underway. The next step is to move from isolated pilots to shared, neutral platforms where vehicle data can safely interact with insurance, energy and urban systems to generate new revenue streams and loyalty.

• For insurers and Insurtechs
  The path to more precise, preventive and personalized insurance lies in expanding the data perimeter—beyond traditional internal sources—to include SDV, infrastructure, satellite and urban data, all on governed platforms that regulators and customers can trust.

NTT DATA’s ambition is to act as a trusted, cross‑industry orchestrator:

• Providing neutral exchange platforms with built‑in anonymization and governance

• Bringing together SDV engineering, AI and insurance expertise to co‑design new services

• Embedding ethical and regulatory guardrails so that innovation and societal trust advance together

The opportunity is significant: safer roads, healthier drivers, more resilient cities and more personalized insurance, all emerging from responsibly shared data.

Now is the time for OEMs, insurers and public stakeholders to co‑create that future—turning SDV data into a shared asset that delivers both economic and societal value.