Meet us at E-commerce Berlin Expo, February 22, 2024.

Please leave your contacts, we will send you our whitepaper by email
I consent to process my personal data in order to send personalized marketing materials in accordance with the Privacy Policy. By confirming the submission, you agree to receive marketing materials
Thank you!

The form has been successfully submitted.
Please find further information in your mailbox.

Innowise is an international full-cycle software development company founded in 2007. We are a team of 1600+ IT professionals developing software for other professionals worldwide.
About us
Innowise is an international full-cycle software development company founded in 2007. We are a team of 1400+ IT professionals developing software for other professionals worldwide.

The role of data analytics in revolutionizing the insurance industry

Exploring the power of data analytics in the insurance industry

In a data-fueled age, the insurance industry stands at a transformative crossroads. Whilst the industry has been traditionally dependent on historical data and straightforward calculations, data analytics is now revolutionizing it, ushering the insurance vertical into a new age characterized by remarkable accuracy and customization.

Simply put, data analytics for insurance refers to the systematic use of data and advanced analysis techniques to draw insights, make predictions, and perform informed decision-making. By analyzing vast amounts of data from many sources, insurance companies derive meaningful patterns and trends that assist them in various operations, from identifying dubious claims to preventing fraudulent activities.

Data analytics in insurance: market overview

The insurance sector is experiencing a transformative shift with the integration of data analytics, unlocking unparalleled insights into risk assessment, fraud detection, and customer behavior. The following breakdown speaks volumes about its disruptive potential.

Growth

The insurance data analytics market was estimated at USD 11.47 billion in 2023 and is expected to showcase a remarkable CAGR of 15.9%, reaching an astounding USD 27.07 billion by the next five years.

Source: Mordor Intelligence

Usage

86% of insurance firms count on data analytics to harness insights from extensive data reports. Thus, auto insurers are currently shifting from solely relying on in-house loss records to behavior-driven analytics.

Source: Mordor Intelligence

Effects

Life insurers that use predictive analytics reported a cumulative effect of 67% cost reduction and a 60% revenue increase.

Source: Willis Towers Watson

Benefits

Data analytics for insurance helps detect and prevent fraudulent claims, saving $308.6 billion on average every year.

Source: Coalition Against Insurance Fraud

The role of data analytics in insurance

In the insurance industry, data analytics has gradually become a cornerstone for driving transformation, innovation, and efficiency and reshaping operational performance toward crystal-clear precision and overall predictability. Let’s explore its multifaceted impact in numbers, divided across various aspects that make up its ground-breaking effect together.

Top use cases of data analytics in insurance

In addition to reducing uncertainty and streamlining insightful decision-making, data analytics for insurance uncovers new growth opportunities that drive business expansion and customer satisfaction. Here are the primary use cases demonstrating its trailblazing influence on the insurance industry.

Risk assessment and pricing
Fraud detection
Telematics in auto insurance
Customer segmentation
Claim processing automation
Underwriting enhancement
Customer experience improvement
Predictive analytics
Catastrophe modeling
Health and wellness programs
Claim settlement optimization
Cybersecurity and risk mitigation
Fraud prevention in healthcare
Property valuation
Product development

With vast amounts of data available, insurers can derive insights and craft strategies that were previously unattainable. Rather than categorizing individuals into broad risk groups, insurers create more granular risk profiles through risk assessment, ensuring that premiums precisely represent the risk they pose. To accurately price insurance premiums, they analyze historical data, behavioral attributes, demographics, etc. In some cases, they integrate unconventional data sources such as social media activity, purchase history, and even satellite imagery regarding property insurance.

Today’s sophisticated analytical models dive deep into vast datasets, pinpointing inconsistencies and anomalies precisely. For instance, by analyzing historical data (past claims, frequency of inquiries) and externally sourced information (credit scores, etc.), data analytics in insurance help detect unusual spikes in claim amounts or frequency from a particular region or service provider. Further, this information can hint at possible collusion or systematic fraud. What is more, text analytics can examine the narrative details of claims, identifying deceptive wording patterns or inconsistencies and empowering insurers to take preventative measures before they are taken for a ride.

By leveraging real-time data collected from connected devices, such as GPS-enabled smartphones and in-car sensors, insurers gain a thorough understanding of driver behavior and vehicle performance, considering speed, mileage, acceleration, braking, and more. As a result of this data-driven approach, they tailor insurance premiums accordingly and provide accurate and timely information on accident circumstances. This helps insurance agencies settle claims faster, reduce fraud, and avoid disputes, contributing to safer driving behavior.

Rather than treating the customer base as a monolithic entity, insurers categorize it based on age, income, lifestyle, risk appetite, and other characteristics to offer customer-centric services and boost client retention. For instance, a young, single professional in an urban setting might prioritize rental or auto insurance with certain digital conveniences. Whereas a family-oriented individual in suburban locales might be more concerned about comprehensive health or life coverage. Using segmentation, insurance companies can also optimize their marketing strategies to ensure they send the right message to the right individual, reducing the inefficiency of broad-brush approaches.

Historically, the claims process was manual, paper-intensive, and plagued by delays, leaving room for errors and inconsistencies. Many preliminary claims evaluations can be performed almost instantly through sophisticated software and algorithms, assessing the validity and quantifying the appropriate settlement amount. Furthermore, the automation tools can instantly cross-reference claim data with policy details, flagging discrepancies or potential fraudulent activities. This expedites the resolution of straightforward claims, allowing insurers to focus on more complex cases.

In place of static tables and historical data, modern underwriters use analytics to capture a more nuanced understanding of risk. Advanced algorithms analyze vast swathes of data, from conventional sources like medical records in health insurance to more contemporary ones such as telematics in auto insurance. This helps in understanding patterns, predicting future risks, and assessing the genuine value of a policy. Now, the insurer can tailor premiums based on lifestyle, exercise habits, or even genetic predispositions rather than offering a generic policy based on age and primary health data.

In an industry historically seen as impersonal and transactional, the infusion of data analytics for insurance has enabled companies to understand and proactively cater to individual policyholder needs deeply. By analyzing patterns from policy renewals, claims, and interactions, insurers gain insights into the preferences, behaviors, and pain points of their customers. This depth of understanding permits tailored communication, timely service offerings, and personalized product recommendations. The insurer, for instance, may suggest homeowners’ insurance when a new property is purchased or comprehensive medical coverage when a family grows, for example, by analyzing the customer’s life milestones.

At its core, predictive analytics harnesses historical data to forecast future outcomes. For insurers, this means anticipating customer needs, behaviors, and potential challenges before they manifest. By modeling various data points – such as policyholder interactions, claim histories, and lifestyle changes – insurers can predict when a customer might be on the brink of a significant life event, like buying a new home or starting a family. This allows them to proactively offer relevant products or advice, turning potential pain points into seamless transitions. Additionally, predictive analytics can enhance the claims process, forecasting the likelihood of a claim becoming controversial or protracted, and allowing insurers to intervene early with solutions or personalized support.

Catastrophe modeling seeks to anticipate and quantify the financial repercussions of large-scale, unpredictable events, from natural disasters like hurricanes to human-made crises. Rather than relying solely on historical data, which can be limited in scope, data analytics in insurance integrates diverse data streams, including real-time environmental observations, climate trends, and even sociopolitical dynamics. For example, while past hurricane patterns offer some insights, incorporating real-time oceanic temperature data or deforestation rates can provide a more accurate assessment of future cyclone intensities or flood risks. By analyzing urban development patterns, insurers can predict potential property loss concentrations in newly developed areas susceptible to natural disasters.

By harnessing the power of data, insurance companies can tailor their offerings to serve individuals and businesses better, offering personalized recommendations to policyholders and encouraging healthier choices and behaviors. For instance, healthier employees are often more productive, experience fewer sick days, and reduce long-term healthcare costs. By analyzing the data generated by health and wellness programs, insurance companies can fine-tune their risk assessment models and pricing structures, resulting in more accurate underwriting and potentially lower premiums for businesses. Besides, data analytics empowers insurers to identify trends and opportunities for preventive care and early intervention, ultimately reducing claims and overall healthcare expenditure.

Data analytics in claim settlement helps businesses expedite the processing of legitimate claims. Automated claims assessment and predictive models allow insurance companies to identify valid claims and settle them more efficiently, reducing the financial strain on businesses and allowing them to resume operations as soon as possible. This efficiency can foster trust between insurers, policyholders, and businesses and improve overall customer satisfaction. Furthermore, data analytics in the insurance industry aids in the optimization of claims reserves, allowing companies to set aside the appropriate amount of funds for anticipated future claims accurately. This optimization ensures businesses do not overpay for insurance premiums and maintain the necessary financial resources for their operations.

One of the primary ways data analytics aids insurers in cybersecurity is by analyzing historical and real-time data. By scrutinizing vast datasets, insurers identify patterns and anomalies indicative of cyber threats or vulnerabilities, allowing them to address potential issues before they escalate into major breaches. For instance, insurers can detect unusual login attempts or data access patterns, which might indicate unauthorized access attempts, and take immediate action to thwart cyberattacks. Moreover, by analyzing factors such as a company’s industry, size, cybersecurity infrastructure, and historical data, insurance agencies can accurately assess the level of risk a client presents. They can then tailor insurance policies and pricing to suit the specific risk profiles of their clients, ensuring they have adequate coverage without overpaying.

By analyzing vast datasets containing information on healthcare claims, patient histories, and billing practices, insurance companies can pinpoint irregularities and patterns indicative of potential fraud. The use of data analytics can flag instances when a provider bills for services not rendered or when a patient receives an unusually high volume of services in a short period of time. These anomalies trigger investigations, allowing insurers to intervene promptly and prevent further fraudulent activities. This not only saves insurers substantial financial losses but also safeguards the integrity of the healthcare system, as fraudulent activities in healthcare often involve multiple parties colluding to maximize ill-gotten gains.

The analysis of vast datasets encompassing information about property attributes, location, historical claims data, and market trends is one of the primary ways data analytics can aid property valuation. This not only reduces the likelihood of underinsurance or overinsurance but also guarantees that businesses pay premiums that align with the actual assets’ value. Data analytics lets insurance agencies stay updated with real-time property information since market conditions and property values can fluctuate rapidly, making it challenging to keep insurance coverage current. In addition, data analytics allows them to monitor changes in property values and market dynamics, ensuring that policies remain relevant and that businesses are adequately protected.

One of the primary ways data analytics in insurance aids in product development is by mining vast datasets for insights, with insurers gathering data on customer behavior, historical claims, market trends, and emerging risks. Through analyzing this wealth of information, they gain a deeper understanding of customer needs, preferences, and behaviors. This enables them to develop and tailor insurance products to specific demographics and customer segments, ensuring businesses receive coverage that aligns with their unique risk profiles. Data analytics empowers insurers to identify gaps in the market and develop innovative insurance offerings accordingly. By analyzing market trends and emerging risks, insurers can spot opportunities to launch new products that address urgent and evolving needs.

Our data analytics services

Innowise excels in delivering top-notch data analytics services, helping businesses tackle their most pressing challenges with ease.

Transform your data into a profit.

Drop us a line
  • We consult insurance businesses on the best-suited data analytics strategies and approaches to manage information effectively.
  • We help insurance companies collect, organize, protect, and store data, turning disparate information into cleansed, well-structured datasets.

Real-life data analytics examples in insurance

We showcase how notable insurance companies adopt data analytics in their daily routines and benefit from improved outcomes.

Allianz SE: data science to enhance predictive capabilities

Allianz Trade harnesses data science to enhance its predictive capabilities and streamline operations, enabling them to spot subtle insurance-related signals like debt, liquidity, and country and sector risks across various domains. Through these intelligent processes, Allianz Trade pinpoints businesses susceptible to payment defaults or even bankruptcy filings. As well as maintaining cutting-edge technology and methodologies, they continue to refine their models and strengthen the synergy between data science and expert analysis.

Progressive: telematics in auto insurance

Progressive, one of the largest auto insurers in the United States, is well-known for its usage-based insurance program, Snapshot. It employs advanced algorithms to meticulously analyze the telematics data, considering driving speed, acceleration, braking patterns, and more. By crunching this wealth of information, Snapshot assesses driver risk with a level of granularity that was previously unattainable through traditional underwriting methods. This offers policyholders highly customized auto insurance rates based on their driving habits, which results in significantly lower premiums and contributes to road safety.

Unitedhealth Group: predictive analytics to address social determinants of health

UnitedHealthcare has integrated predictive analytics to enhance well-being, reduce costs, and foster engagement in clinical intervention programs. This allowed them to address social determinants of health among individuals covered by select employer-sponsored benefit plans. Using predictive analytics, UnitedHealthcare expands on its existing efforts to address social determinants of health for Medicare and Medicaid beneficiaries. It proactively identifies individuals at a higher likelihood of requiring assistance, encompassing areas such as access to nutritious food and affordable housing. Advocates at UnitedHealthcare's call centers then assist individuals in navigating challenging life events or circumstances through real-time interactions and access to curated community resources.

Data analytics implementation roadmap

A smooth data analytics process requires a meticulous approach to developing disruptive solutions that customers highly anticipate.

01
Requirements analysis
Our BAs determine a data analytics solution's specific needs and objectives, ensuring it aligns with business goals and user needs.
02
Data architecture design
We focus on creating a framework for organizing and managing data within the system, including data storage and access methods.
03
Data modelling
We design the structure and relationships of data to facilitate efficient querying and analysis through entity-relationship diagrams and schemas.
04
Data ingestion
Then, our well-rounded developers collect and import data from various sources into a centralized repository, making it accessible for analysis.
05
Data cleaning
To ensure data quality and reliability, we identify and rectify errors, inconsistencies, and inaccuracies that might hamper the process efficiency.
06
Data lake building
This stage involves creating a centralized repository for storing vast amounts of raw data in its native format, providing flexibility.
07
ETL/ELT pipeline implementation
An ETL/ELT pipeline consists of designing and setting up processes to extract data, transform it for analysis, and load it into a target system.
08
Quality assurance
Our QA specialists conduct rigorous testing and validation of data analytics processes, ensuring accurate and reliable outcomes.
09
Automation and deployment
Innowise implements automated processes for data analytics and deploys market-ready solutions into production environments.
10
Data analytics and data visualization
Finally, data is analyzed using various techniques and tools, providing valuable insights, patterns, and trends for actionable decision-making.
01 Requirements analysis
Our BAs determine a data analytics solution's specific needs and objectives, ensuring it aligns with business goals and user needs.
02 Data architecture design
We focus on creating a framework for organizing and managing data within the system, including data storage and access methods.
03 Data modelling
We design the structure and relationships of data to facilitate efficient querying and analysis through entity-relationship diagrams and schemas.
04 Data ingestion
Then, our well-rounded developers collect and import data from various sources into a centralized repository, making it accessible for analysis.
05 Data cleaning
To ensure data quality and reliability, we identify and rectify errors, inconsistencies, and inaccuracies that might hamper the process efficiency.
06 Data lake building
This stage involves creating a centralized repository for storing vast amounts of raw data in its native format, providing flexibility.
07 ETL/ELT pipeline implementation
An ETL/ELT pipeline consists of designing and setting up processes to extract data, transform it for analysis, and load it into a target system.
08 Quality assurance
Our QA specialists conduct rigorous testing and validation of data analytics processes, ensuring accurate and reliable outcomes.
09 Automation and deployment
Innowise implements automated processes for data analytics and deploys market-ready solutions into production environments.
10 Data analytics and data visualization
Finally, data is analyzed using various techniques and tools, providing valuable insights, patterns, and trends for actionable decision-making.

We live in the data-driven era, and Innowise is leading the way by transforming fragmented information into structured datasets that enable strategic planning and informed decision-making. By tailoring solutions to specific business needs, Innowise empowers organizations to navigate the evidence-based landscape, fostering innovation, efficiency, and competitive advantage.

Top 5 data analytics platforms for insurance

In insurance, Tableau is widely used for data visualization and business intelligence, providing an easy-to-understand display of complex data, allowing underwriters and claims professionals to identify trends and assess risk quickly. Tableau's real-time dashboards also enhance customer engagement by providing insights into policyholder behavior and preferences.

Pros

  • User-friendliness for non-technical users
  • Vibrant user community and extensive online resources

Cons

  • Costly licensing
  • Limited data preparation capabilities

Insurance companies use Power BI for data analytics and reporting. It allows them to create interactive reports and dashboards and provide a comprehensive view of their operations. Power BI enables insurers to monitor key performance indicators, track claims processing efficiency, and optimize customer service, ultimately enhancing decision-making and operational efficiency.

Pros

  • Integration with Microsoft Stack
  • Scalability across both small businesses and large enterprises
  • Flexible customization with built-in charts

Cons

  • No support for macOS and Linux
  • Performance issues with big datasets
  • Learning curve for those new to data analytics

SAP S/4HANA is an ERP system utilized in insurance for end-to-end business processes. This feature-rich platform helps insurance businesses by providing a unified and real-time view of operations, including policy management, claims processing, and financial management. S/4HANA's advanced analytics capabilities enable insurers to forecast risks, optimize resource allocation, and improve customer service, supporting more agile and data-driven decision-making.

Pros

  • Real-time analytics based on up-to-the-minute information
  • Extensive suite of integrated enterprise management functions

Cons

  • Costly implementation
  • Extensive training for users to effectively utilize its features

Qlik Sense is a data analytics and visualization platform employed in the insurance industry to improve data discovery and insights. It benefits insurance businesses by offering self-service analytics, enabling users to explore and visualize data independently, while its AI-driven natural language query capabilities make it easy to create sophisticated reports. Qlik Sense aids insurers in claims analysis, fraud detection, and customer segmentation, leading to more effective risk assessment and improved efficiency.

Pros

  • Associative data model to explore data relationships intuitively
  • User-driven analytics to create visualizations and reports

Cons

  • Licensing costs, particularly for larger organizations
  • Learning curve for individuals new to the platform

Alteryx is used in insurance for data blending, preparation, and advanced analytics. It helps insurance companies automate data workflows and analytics processes, significantly reducing the time required for tasks like underwriting, claims analysis, and fraud detection. Alteryx enhances efficiency by simplifying data manipulation and predictive modeling, empowering insurance professionals to make data-informed decisions more accurately.

Pros

  • Advanced data preparation
  • Workflow automation to streamline repetitive tasks

Cons

  • The cost might be prohibitive for smaller businesses or individual users
  • Advanced features may be overwhelming for beginners

FAQ

Insurance companies can harness the power of data analytics to up their game. With it, they can sharpen their risk assessment, create tailor-made insurance packages, smoothen the claims process, and fine-tune pricing models, increasing efficiency and accuracy.

Yes, data analytics can identify patterns, anomalies, and suspicious activities in insurance claims data. These high-tech tools can expose fraudulent claims and behaviors, giving insurers the upper hand in stopping fraud and saving money on phony payouts.

Big data is a game-changer for insurance that helps insurers understand how customers tick, look at risks from all angles, and make the process of underwriting and claims smoother and more intelligent for informed decision-making.

The insurance world is getting a tech makeover, welcoming advanced technologies like AI, machine learning, and IoT. They assist in speeding up insurance paperwork, defining risk profiles, and, ultimately, offering insurance services that stand out in the ever-evolving digital landscape.

author
Dmitry Nazarevich Head of Mobile

Share:

Table of contents

Contact us!

Book a call or fill out the form below and we’ll get back to you once we’ve processed your request.

    Please include project details, duration, tech stack, IT professionals needed, and other relevant info
    Record a voice message about your
    project to help us understand it better
    Attach additional documents as needed
    Upload file

    You can attach up to 1 file of 2MB overall. Valid files: pdf, jpg, jpeg, png

    Please be informed that when you click the Send button Innowise will process your personal data in accordance with our Privacy Policy for the purpose of providing you with appropriate information.

    What happens next?

    1

    Having received and processed your request, we will get back to you shortly to detail your project needs and sign an NDA to ensure the confidentiality of information.

    2

    After examining requirements, our analysts and developers devise a project proposal with the scope of works, team size, time, and cost estimates.

    3

    We arrange a meeting with you to discuss the offer and come to an agreement.

    4

    We sign a contract and start working on your project as quickly as possible.

    Thank you!

    Your message has been sent.
    We’ll process your request and contact you back as soon as possible.

    This website uses cookies

    We use cookies to enhance your browsing experience, serve personalized ads or content, and analyze site traffic. By clicking “Accept all”, you consent to our use of cookies. Check out our Privacy Policy.

    Thank you!

    Your message has been sent.
    We’ll process your request and contact you back as soon as possible.

    arrow