Supercharge

Today, 30% of insurers claim to have AI capabilities, and 70% of insurers plan to deploy real-time predictive AI models within two years, according to the 2024 Report on Insurance Industry Trends. Many of those who are looking for insurance are on board with AI, too. Over 80% of respondents of a consumer survey said that they trust their insurers to handle their data responsibly with AI tools.

Insurance processes, from risk management to adhering to regulatory requirements and consumer needs, are highly complex, which is also why these functions can always benefit from more accuracy, speed and personalization. These are areas where AI technology excels and can therefore provide a competitive edge for an insurance company.

But first, insurers need a modern data platform as a solid foundation that will simplify access to clean, consistent data, making it easier to integrate an AI tool into daily operations.

Utilizing AI in insurance efficiently with a modern data platform

To utilize AI in insurance, everything has to start with your data infrastructure.

For instance, the data has to be spotless and well-connected for fraud detection models. Underwriting models can only be enhanced if the technology in use is able to process vast amounts of complex data points, including IoT, health records, and financial sources.

For high-level and highly efficient data automation and assessment in all insurance processes, insurers need an AI-ready modern data platform.

What is a modern data platform?

A modern data platform (MDP) is the connecting tissue of all your business data, such as claims, policies, and market trends. It’s not another tool your employees need to master, as it seamlessly fits in your company’s existing workflows.

A modern data platform:

Ensures that the data is clear, structured, and ready for cutting-edge AI projects
Supports better risk assessments and informed decision-making with high-quality, real-time data and analytics
Improves operational efficiency by automation and streamlining data processes, which reduces manual effort, increases claim processing speed, and minimizes errors
Enables simpler implementation of the latest data governance and security standards, significantly reducing exposure to risks and ensuring regulatory compliance
Facilitates data-driven performance measurement and reporting, enabling insurers to demonstrate operational efficiency and business success to investors and stakeholders

What makes a modern data platform good (and not stuck in the past)?

A modern insurance business demands faster innovation capability, streamlined operations, and cost-efficient solutions that traditional data systems simply can’t provide.

Here’s how a modern data platform compares to an old data infrastructure:

Benefits of a modern data platform	Without a modern data platform
Cloud scalability that adapts to any business size.	A rigid infrastructure and systems that require expensive upgrades and struggle with spikes in data volume.
Simple & cost-efficient system that optimizes for value, reduces infrastructure overhead, and eliminates unused capacity costs.	Legacy systems that demand dedicated maintenance teams, increasing manpower to stay afloat, and driving up operational expenses.
High-performance architecture that is fast & reliable, and where real-time analytics enables instant risk assessment and fraud detection.	Outdated databases create delays in claims processing and underwriting decisions.
Future-proof solutions like AI-driven analytics and real-time streaming data drive innovation.	Traditional SQL-only systems prevent AI, IoT and unstructured data integration, limiting innovation.
Enables full control, governance, strong security, and compliance.	Lack of automated governance and exposure to security vulnerabilities.

4 AI use cases in insurance powered by MDP

1. Automated underwriting process

In property & casualty insurance, AI-powered underwriting streamlines risk evaluation, accelerates decision-making, and enhances the customer experience. A structured approach includes:

Ensuring standardized, high-quality data for underwriting models
Risk assessment with AI & predictive analytics based on historical claims data, external sources, and behavioral patterns, classifying them as high, medium, or low risk
Automated decision making, where low-risk cases are fast-tracked for auto-approval or rejection, and medium and high-risk cases are sent for human review

2. Enhanced customer service with AI Virtual Assistants

AI-powered virtual assistants enhance customer service of an insurance company by:

Providing 24/7 support for policy inquiries, claims processing, and coverage explanations, resulting in faster, more efficient interactions

Utilizing structured policy data, past interactions, claims history, and external sources to deliver accurate, personalized, context-aware responses
Automating routine queries, they reduce workload for support teams and improve response time and issue resolution

3. Proactive risk prevention and fraud detection

IoT devices and telematics transform risk assessment by delivering real-time insights into vehicle usage and home security. With AI-driven analytics and a modern data platform, insurers can:

Improve risk evaluation and prevention through real-time data utilization
Help fraud reduction, as AI verifies IoT data for inconsistencies
Use AI-driven anomaly detection for proactive risk mitigation and preventive recommendations
Offer personalised premiums, leveraging AI-powered analytics

4. Personalised pricing & real-time premium adjustment

With AI-driven pricing models that continuously analyse telematics, IoT data, and external environmental factors, insurers can offer:

Real-time premium adjustments based on evolving risk factors
More precise, data-driven pricing for policyholders
Enhanced underwriting accuracy, reducing financial risk
Increased market competitiveness through adaptive, personalised pricing strategies

Implementing AI in insurance with MDP: Where to start?

A methodical, structured approach is essential for insurers to transition successfully from legacy systems to modern data platforms.

Here are the key initial steps insurers should take for a swift transition:

1. Define goals and business objectives to ensure alignment with company strategy

Identify the data and AI capabilities you want to develop
Explore how a Modern Data Platform can support your goal
Assess your critical improvement points and opportunities
Determine regulatory and security requirements

2. Analyse the current state and find the gaps

Assess existing capabilities and conduct a data maturity evaluation
Audit data sources, systems, and processes to pinpoint limitations
Identify and document all data quality issues.

3. Develop a phased implementation roadmap

Prioritize initiatives based on business impact, ROI, and feasibility (build a strong foundation before advancing to AI capabilities)
Define a clear data integration and migration strategy (co-existence vs “rip-and-replace”)
Set timelines, define milestones, and establish KPIs to track progress and quantify improvements

IoT-driven home insurance platform

Supercharge supported the Florida-based insurance company VYRD by creating a modern data platform that not only improved their business but was welcomed by Florida residents who have experienced increasing costs and fewer and fewer choices for protecting their homes.

The designed and implemented modern data platform in Google Cloud provided a scalable, secure, and centralized data foundation for VYRD.
The unified data platform sourced, reviewed, and combined data from multiple sources, from claims to IoT sensors, and supported automated KPI reporting and contextual customer communications.
By integrating the marketing platform into VYRD’s data warehouse and adding custom customer segmentation, the company’s marketing operations became highly streamlined.

See the detailed case study here.

The Necessity of a Modern Data Platform for Insurance

As an insurance company, if you have a modern data platform, you can:

Revolutionize the underwriting processes, with the help of AI, by uncovering inconspicuous patterns and risk indicators through analyzing voluminous datasets from varied sources
Engage in more efficient, more personalized interactions with customers via virtual assistants
Improve risk assessment to be quicker and more precise by utilizing a broad range of data sources, such as IoT and telematics
Offer flexible pricing with real-time, premium adjustments based on the changes in risk factors

If you’d like to invest in the transformative digital experience that a modern data platform can offer for your insurance company, make sure to look at the services we offer for the insurance industry.

You can see how we fuse behavior science into designing business processes and how we use our unique, data & AI-focused technical competence in the work we did for other insurance providers.

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AI in insurance: your shortcut to understanding modern data platforms

Transforming the insurance process from customer service to underwriting with a modern data platform