Big Data in Insurance: From Challenges to Strategic Solutions

While data has always been the foundation of insurance, its sources, types, scope, and speed of generation differ significantly from past instances. Nowadays, aspects of insurance like policies, claims, and especially customer interactions are handled with the aid of telematics, digital health records, and connected devices. These technologies help create vast and constantly growing datasets, and the main driver of competition among insurers lies in how those datasets are managed.

Because of new market pressures and rules, insurance companies are turning to advanced analytics and machine learning in order to keep up. These technologies offer several important benefits, such as the discovery of previously undetected fraud patterns, increased efficiency in underwriting instead of manual work, and more accurate, behavior-driven pricing models. The use of these new methods allows insurers to streamline business operations and provide customers with high-quality service.

However, the process of integrating these methods presents a significant challenge. To make sense of the data that is scattered across systems and massive in volume, you must ensure that the data is scalable, compliant with regulations, and, most importantly, protected from unauthorized access. What you need is a unified plan for managing this information; otherwise, investments in analytics might end up becoming disconnected experiments that won’t deliver benefits in the long run.

This article focuses on the reasons why big data is important in the insurance sector, what specific problems it helps solve, and how insurance companies can use it to obtain useful knowledge from unprocessed data.

How Big Data Transforms the Insurance Industry

The primary reason why insurance companies collected data in the past was for day-to-day operations and to meet legal requirements. Policy records, claims information, and customer files were stored for record-keeping and historical purposes, not for active use or deep analysis.
In the present day, data is considered a strategic tool for insurers. It is generated in large volumes from new diverse sources, like digital channels, telematics devices, customer interactions, and external information streams. The data is no longer just for archiving; instead, it has become an active asset used for improving risk models, identifying fraud early on, and creating more customized insurance products.

According to Accenture’s research, companies with AI-led, data-driven processes achieve 2.5 times higher revenue growth and 2.4 times greater productivity. These numbers are a sign of faster and better decision-making. Such improvements are a direct result of predictive modeling and machine learning – techniques that facilitate better underwriting, streamline claims management, and enable more precise pricing. Instead of just analyzing past results, companies are now using data to make smart, balanced decisions.

What makes this change possible is cloud services. While processing large amounts of data on traditional systems is not only expensive but slow to scale, cloud platforms offer a much easier way of managing data storage and analysis. For example, AWS provides different services for different features: Amazon S3 for storage, AWS Glue for integration, Amazon Redshift for analytics, and Amazon SageMaker for building machine learning models. With these instruments, insurers are allowed to process data securely and in compliance with regulations without compromising the scalability and flexibility of operations.

Moving beyond simple record-keeping and toward data-driven decisions allows insurers to boost efficiency and strengthen their risk management. This also helps them deliver services that better meet customer expectations.

Core Benefits of Big Data for Insurers

Analytics is a critical tool for insurers primarily because of its ability to help base decisions on evidence rather than assumptions. What it does is allow leaders to streamline operations, calculate risks with higher levels of accuracy, and most importantly, create products tailored to real-world customer behavior instead of a generic profile. All the improvements regarding profitability, customer trust, and long-term competitiveness bring about a number of positive outcomes, namely:

• Fraud detection and risk prevention. By analyzing large claims and transaction datasets, big data tools detect suspicious patterns, while machine learning models identify anomalies in real time, which minimizes fraud losses and improves underwriting accuracy.
• Faster and more accurate claims processing. This toolkit can automate intake, verification, and analysis. By doing so, it enables direct queries on claims data and high-performance analytics that shorten settlement times and lower administrative costs.
• Personalized policy pricing and customer experience. Pricing models are shaped mainly by telematics data, and customer behavior feeds pricing models. The benefit lies in how predictive models can calculate premiums based on actual risk, which leads to more justified pricing and stronger customer retention.
• Operational efficiency and cost optimization. By centralizing policy and claims data, cloud-native analytics reduces manual work and infrastructure expenses. This optimization allows for easier data access and reporting, which, in turn, improves efficiency across all business units.

Key Challenges in Adopting Big Data in InsurTech

To successfully operationalize big data in the insurance industry, companies must overcome interconnected challenges related to technology, regulations, organization, and finances.

Legacy System Limitations and Technical Debt

One of the most significant issues surrounding big data adoption lies in the fact that the insurance industry majorly relies on decades-old core systems. The systems are created for mainframe or COBOL-based policy administration, claims management, and billing structures that create data silos. Only a small percentage of big insurance companies (10%) have updated more than half of these core systems. In 2024, U.S. insurers were expected to spend over $130 billion in 2024 on legacy system modernization. According to projections, this number might be twice as large by 2029.

Even insignificant integration efforts may result in cascading effects across all interconnected systems. One small modification might demand updates to a number of other applications that use that data. This isn’t a quick fix, as the procedure can take 12-18 months or longer.

Data Quality and Governance Deficiencies

Fragmented architectures and departmental silos result in incomplete or inconsistent data. This problem is relevant for many organizations, as they often don’t have established processes for data validation, cleansing, and management. This issue becomes even more prominent during the integration of third-party data sources. Organizations end up in a position where they have limited control over data collection and validation.

When operations like underwriting, claims analytics, and pricing models utilize flawed data, they generate false insights and erode trust among business users.

Scalability Infrastructure Gaps

Modern data comes in big volumes, high velocity, and vast variety. Such demands are impossible for traditional on-premise data warehouses to manage. This problem occurs because legacy infrastructure was initially designed for batch processing, not real-time analytics or large-scale datasets.

While cloud migration provides scalability, it comes with concerns regarding security, legacy integration, and cost management. The process of transition is complex, as it requires architectural changes and new security protocols. Also, your teams will have to undergo additional training while maintaining operational continuity.

Regulatory Compliance Complexity

Insurance companies must follow tough data privacy rules like GDPR, CCPA, and HIPAA. Because of data residency requirements, global insurers must ensure that data must be stored and processed within the specific country or region where it was collected. The result of these laws is that the data is forced to remain in separate, isolated piles, which makes it impossible for insurers to perform a complete, comprehensive analysis.

Insurers are required to implement model governance frameworks that ensure algorithmic transparency, fairness, and bias prevention in all automated decision-making. This is a critical step to avoid discriminatory outcomes and legal penalties. However, the industry has a long way to go, since only 32% of insurance data leaders work closely with compliance teams today.

Integration and Interoperability Challenges

Modern analytics tools cannot operate with proprietary data formats and interfaces utilized by core insurance systems. As previously mentioned, legacy systems were created to process data in batches and cannot handle continuous data streams from IoT sensors, telematics devices, or mobile applications.

Many insurance companies use software from different vendors, each with its own data schema and API requirements. Because of this lack of standardization, integration is considered to be the most significant challenge for advancing analytics maturity, even more so than the common problem of data quality.

Return on Investment Uncertainties

There is a big divide between initial investments and results: big data initiatives require substantial resources while benefits may only be noticed in several years. According to Chief Data Officers, leadership has unrealistic expectations for instant results and doesn’t view data projects as strategic investments, instead reducing them to simply isolated IT experiments.

While big data implementation provides significant benefits like improved customer experience, enhanced agility, and better risk insights, it’s very difficult to put a precise monetary value on them. Additionally, data projects are at a disadvantage when it comes to funding because they are up against urgent matters like cybersecurity, regulatory compliance, and system maintenance issues.

Organizational Change Resistance

Embracing big data isn’t just about getting new technology; it requires significant changes in the decision-making procedures and organizational structure. The problem in this case lies in the fact that conservative insurance environments tend to emphasize regulatory compliance and risk management instead of innovation. This is reinforced by employees being skeptical of analytics-driven insights, especially when they do not align with their experience-based judgment.

Coordination often is a challenge as well. Big data projects demand close cooperation between IT, business units, compliance, and risk management teams. This issue is especially relevant for organizations with strong functional silos.

Security and Cybersecurity Risks

Personal health information and financial data are prime targets for cyberattacks. By putting all this valuable information into one central location, data repositories create a high-value target for cybercriminals. Moving this data to the cloud adds another layer of complexity, as it introduces questions about data sovereignty and vendor security practices.

When multiple users and systems need access to data, security measures such as data encryption, access controls, and monitoring systems become more difficult to implement. The key is to balance security requirements with analytical accessibility, which can only be achieved with a high level of technical skill and the use of sophisticated systems.

Data Integration Across Multiple Sources

The insurance sector manages various data sources: internal systems, third-party databases, public records, IoT devices, and partner feeds. The main issue is disparity, as data sources differ in update frequency, quality standards, technical interfaces, and other characteristics. To create real-time analytics, the organization must synchronize all these systems without compromising data integrity during outages, delays, and data quality issues.

The solution to this problem is master data management – a set of rules and technologies designed to create a single, accurate version of all this scattered information. In order to realize this objective, master data management must involve such operations as complex data matching and deduplication. This method is indispensable when dealing with customer, policy, and claims information across multiple environments.

Strategic Approaches to Overcome Big Data Barriers in Insurance

Overcoming the hurdles of legacy systems, regulations, and data integration requires a clear strategy. This section provides practical approaches for modernizing your infrastructure through embedding compliance and leveraging cloud platforms to achieve measurable results from your data.

Modernizing the Data Foundation

The first step requires you to replace all isolated systems with a cloud-native data lake. To manage this operation, Amazon S3, combined with AWS Glue and Lake Formation, offers a centralized data repository with built-in governance. Glue or EMR can be used to cleanse and enrich data, while Redshift and Athena enable fast analytics. When it comes to managing variable workloads, elastic services like Lambda and Fargate provide automatic scaling.

To demonstrate, Verisk Argus migrated petabytes of data from on-premises SQL Server databases to Amazon S3. This migration allowed the team to expand compute and storage independently. Because of this newfound flexibility, they can perform data analysis in a way that is much more flexible and cost-effective than before.

Embedding Compliance and Security at Every Layer

Insurance data is placed under strict regulations. For this reason, every layer of your architecture must have embedded compliance controls. Services like AWS KMS and CloudHSM handle encryption while CloudTrail, Config, and Macie provide oversight. Control Tower enforces guardrails across accounts.

Fairness and transparency shouldn’t be overlooked once analytics models are deployed. To address this, SageMaker Clarify enables insurers to test bias models and provide explanations for regulators. Take Root Insurance, for example: the company exemplifies this shift by creating more equitable pricing models based on telematics data, not demographic proxies.

From Proof of Concept to Scale

To successfully adopt big data in insurance industry, teams should follow a plan that involves three stages:

• Proof of Concept. This phase involves starting with a focused use case. It could be claims automation or fraud detection to assess business value.
• Integration. Once you have proven a use case, connect the proven capability to core policy, claims, or billing systems using services like DataSync, Transfer Family, or streaming pipelines with Kinesis and MSK.
• Scale. Lastly, you must extend analytics across multiple business lines and integrate them into three main business processes – underwriting, claims handling, and customer engagement.

The purpose of this strategy is to build stakeholder trust and avoid the risks of large-scale failures.

Delivering Measurable ROI

A big data project is successful only when it delivers a clear outcome. Some of the most high-value use cases demonstrate such results as fraud detection, telematics-based pricing, retention modeling, and automated claims. Also, they must be connected to KPIs, like loss ratio improvement, fraud savings, or reduced claim cycle times.

A prime example is Standard Bank Insurance. By using Amazon Fraud Detector, this company reduced funeral claims payout times from 48 hours to under 6 hours and doubled while doubling fraud detection. Also, customer satisfaction scores improved by 36%. By using agile rollouts and continuously tracking the return on investment, projects stay aligned with business value. This procedure is supported by tools like AWS Budgets and Cost Explorer.

Building the Operating Model and Culture

Successful integration of big data analytics relies not only on technology modernization but also on fundamental changes in the organization. To drive these changes, consider using a Cloud Center of Excellence – this service establishes enterprise-wide standards, enforces security policies, and shares reusable templates that accelerate delivery across business lines. When it comes to execution, DevSecOps pipelines built with CloudFormation or CDK and deployed through CodePipeline guarantee that every new deployment has integrated compliance and security from the very beginning, instead of it being treated as an afterthought.

The final component is the people. To help your team succeed, there are upskilling programs, including AWS certifications and internal bootcamps that help align IT, actuarial, and data teams under a shared digital-first mindset. Through this training, the teams will be able to collaborate effectively around analytics-driven initiatives.

Choosing the Right Partners

There are cases when levels of complexity in big data initiatives require external support. For this reason, consider partnering with an experienced development vendor. This cooperation will help with skill gaps, as well as streamline software delivery and implement compliance and governance across all layers of the application architecture.

The optimal strategy is to collaborate with a partner that offers support on various stages of the project lifecycle. This means providing guidance during such operations as building analytics solutions on AWS, modernizing legacy platforms, consulting on compliance and governance, and especially managing infrastructure for resilience and scalability.

There are several factors that insurers should consider when choosing a partner. Those are:

• Cloud-native expertise: What is the vendor’s specific experience in building and managing data lakes, pipelines, and governance frameworks?
• Compliance readiness: How will they integrate privacy, audit, and security requirements into the system’s design and day-to-day operations?
• Delivery approach: How do they use automation, templates, and DevSecOps practices to accelerate delivery timelines and reduce risk?
• Post-deployment support: What training, knowledge transfer, and operational assistance do you provide after the initial rollout?

By partnering with vendors like Romexsoft, insurers can build a strong big data foundation. The vendor helps implement cloud-native platforms, set up secure pipelines, and provides teams with DevOps practices to help them achieve resilient and scalable solutions.

Practical Use Cases of Big Data in Insurance

Moving beyond theory, big data in the insurance industry is a critical tool for solving the insurance industry’s most pressing challenges. The examples in this section provide a clear roadmap for using analytics to improve everything from risk prediction and claims management to customer engagement.

Claims Fraud Detection with Anomaly Detection & ML

In many insurance companies, big data analytics and machine learning are used to improve accuracy during the identification of fraudulent claims. These technologies analyze large sets of claims data and detect anomalous patterns. Thanks to this analysis, insurers can flag suspicious claims for review, which helps them avoid paying out on illegitimate claims. For this purpose, AWS provides such tools as Amazon Fraud Detector – a managed ML service that specializes in fraud detection and integrates with insurers’ workflows to automate these checks. By using this method, you can accelerate fraud screening and minimize unnecessary expenses.

A strong example of this approach is demonstrated by esure. Once they migrated to a cloud-native AWS platform, the company applied machine learning to improve fraud identification. With advanced technologies, they were able to process millions of data points, which resulted in an increased fraud detection rate. More specifically, the rate went from about 40% of suspicious claims caught to 60% – a 50% improvement. Not only that, the transition to AWS reduced IT expenses by up to 30%. This case shows how cloud-native big data platforms can deliver dual benefits: enhanced fraud prevention and significant cost savings.

Predictive Risk Scoring and Underwriting

Aside from fraud detection, insurers use big data analytics and predictive analytics to enhance risk assessment and underwriting. The companies achieve more accurate risk scores and pricing models through analysis of claims history, credit data, health records, telematics, and other sources. Specifically, machine learning models built on AWS SageMaker incorporate hundreds of variables to calculate likelihood, enable fairer premiums, better loss ratios, and faster policy issuance.

A clear example of big data’s role in underwriting automation is Elevance Health. By using Amazon Textract to digitize medical records, the company automated about 90% of its document processing. This move not only reduced manual effort and sped up reviews but also cut costs and accelerated policy turnaround.

Telematics-Based Policy Pricing (Usage-Based Insurance)

The next example is demonstrated by usage-based insurance (UBI). These models apply telematics to adjust premiums according to real-world driving behavior. The procedure involves gathering data (e.g., speed, braking, mileage, driving times) from IoT devices or smartphone apps and processing it on cloud platforms. Such platforms as AWS IoT, Amazon Kinesis, and Amazon EMR support real-time ingestion and analysis of this information to generate risk scores. What this strategy delivers is fairer, more dynamic pricing and safer driving incentives. It also enables the development of new products like pay-as-you-drive policies.

A prime example of this strategy at scale is Arity. As Allstate’s mobility data subsidiary, Arity has captured a massive amount of data – over a trillion miles of driving. To manage this scale, the company migrated its telematics infrastructure to AWS. Using Amazon EMR, Amazon Managed Streaming for Kafka, and Apache Flink helped Arity minimize monthly infrastructure expenses by 30% and lowered the cost per streaming event by 36%. The additional benefits of this modernization include sped-up product development, which enables new analytics solutions to be delivered in weeks instead of quarters. With this scalability, Arity and Allstate can convert raw driving data into predictive insights for pricing and underwriting with high levels of speed and efficiency.

Claims Automation and Faster Payouts

An important example of how big data analytics is applied in the insurance sector is the automation of claims. Insurers are adopting AI and machine learning to achieve a level of claims handling that requires minimal human input and is almost entirely managed by algorithms. Automation can now be applied to such steps as first notice of loss, document and image analysis for damage estimation, and predictive coverage decisions. Machine learning integrates claims history, policy details, and telematics crash data to resolve straightforward claims quickly. This leads to reduced overhead and faster payouts. Such levels of efficiency allow adjusters to focus on more complex or disputed cases while improving customer satisfaction.

The impact of this strategy is illustrated by Standard Bank Insurance. This company streamlined its funeral claims process using AWS machine learning and Amazon Fraud Detector, which resulted in significant improvements. Low-risk claims (about 94% of cases) were approved almost instantly, cutting payout times from 48 hours to less than 6 hours and raising customer satisfaction by 36%. Meanwhile, the new system enabled the company to double the number of fraudulent claims caught by dedicating more investigator attention to high-risk cases flagged by ML.