“If the data isn’t ‘big,’ the machine will function as a calculator, not an oracle.” This is a quote from the insurance company, Lemonade’s article discussing AI and big data in insurance.

Here’s an often overlooked truth: We only look at insurance companies to help us out whenever we find ourselves in grave trouble.

Here’s another unfortunate truth: Traditionally, insurance companies have relied on outdated demographic data, some dating back 40 years, to create their products.

Why should this concern us? This archaic approach often leads to incorrectly priced policies and missed financial opportunities.

However, there is a light at the end of this tunnel! 

The application of big data in insurance is proving to be a game-changer for the industry.

Here are some improvements brought about by the implementation of big data in the insurance industry: 30% better access to insurance services, 40-70% cost savings, and a whopping 60% higher fraud detection rates.

This blog aims to illuminate the important role of big data technology in the insurance industry and the innovative ways in which insurance companies can leverage big data.

 

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Table of Content

  1. Big Data’s Impact On The Insurance Sector’s Growth
  2. 7 Key Uses of Big Data in Insurance
  3. Common Pitfalls to Avoid While Using Big Data in Insurance
  4. Future Trends for Big Data in Insurance in 2024
  5. Trend 1 – Increased Adoption of AI and Machine Learning
  6. Kanerika: Your Partner in Data Implementation
  7. FAQs

 

Big Data’s Impact On The Insurance Sector’s Growth

The insurance sector is now on the cusp of a revolution, thanks to big data in insurance. Anna Maria D’Hulster, Secretary-General of The Geneva Association, aptly stated, “Going forward, access to data, and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry.

The challenge in the insurance sector has always been twofold. 

On one end, customers grapple with questions about the reliability, offers, and market reputation of insurance companies. On the other hand, insurers struggle to comprehend customer behavior, fraud, policy risks, and claim sureties. 

These challenges, however, are being addressed head-on with the application of big data analytics in insurance.

Investment in big data in the insurance industry is escalating, with projections of up to $4.6 billion by 2023. 

Let’s delve into the 7 use cases where big data and the insurance industry’s collaboration is reshaping the sector for insurers and customers.

 

7 Key Uses of Big Data in Insurance

 

Top use cases of big data in insurance

 

Use Case 1: Customer Acquisition Strategies

Did you know that data-driven organizations are 23 times more likely to excel in customer acquisition? According to the McKinsey Global Institute, data-driven entities are six times as likely to retain customers and an impressive 19 times more likely to be profitable.

The adage ‘knowledge is power’ has never been more pertinent. 

In today’s digital era, every online action generates a wealth of data. This data, emanating from social networks, emails, and customer feedback, forms a colossal pool of unstructured data, a vital component of big data. For the insurance sector, this is a goldmine.

Using big data analytics in insurance transforms this unstructured data into actionable insights. Imagine moving beyond traditional surveys and questionnaires with frequent misleading insights

Instead, insurance companies can now use big data insurance strategies to analyze online customer behaviors, uncovering patterns and preferences that were previously hidden.

The result? A more streamlined, effective, and customer-centric approach to acquisition. 

 

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Use Case 2: Boosting Customer Retention Efforts

 

customer retention

Big data in insurance helps insurers understand their customer preferences better and can lead to an increase in customer retention 

 

While acquiring new customers is important, there’s another step in the funnel that is even more pivotal: customer retention.

Here’s a cold fact: the probability of selling to an existing customer ranges between 60% and 70%, significantly higher than the 5% to 20% likelihood of selling to a new prospect. 

In the context of the insurance industry, where the average customer retention rate hovers around 83%, the question arises: how can big data in insurance help catapult this figure into the coveted 90s range?

A high customer retention rate is a hallmark of success, and this is where big data analytics in insurance comes into play.

The application of big data in the insurance industry allows for the creation of sophisticated algorithms that can detect early signs of customer dissatisfaction. 

These insights enable insurance companies to swiftly respond to customer queries. This is while also improving their services in real-time to resolve specific customer grievances. 

 

Use Case 3: Improved Risk Assessment Techniques

How does big data transform traditional risk assessment in the insurance sector? 2 short answers – unparalleled precision and depth of analysis.

Big data in insurance enables companies to delve deeply into variables like demographics, claims history, and credit scores, revolutionizing how they predict and manage risks.

Utilizing machine learning algorithms and predictive modeling, insurers develop detailed risk profiles for policyholders, leading to more tailored coverage and premium rates.

Here’s an example of how Kanerika’s team used financial modeling and forecasting to reduce risk. 

 

Case Study - Improved Financial Modeling And Forecasting To Reduce Risk (2)

 

When a leading insurance company faced challenges like limited financial health access and vulnerability to fraud, we implemented advanced AI data models and Machine Learning algorithms, including Isolation Forest and Auto Encoder. 

This strategic intervention not only reduced the company’s risk exposure by 25% and financial losses by 33% but also expanded its customer base by 37%. This case highlights the transformative impact of technology on optimizing insurance operations and enhancing customer trust.

 

Use Case 4: Enhanced Fraud Prevention and Detection

The Coalition Against Insurance Fraud reveals a staggering loss of over $80 billion annually by U.S. insurance companies due to fraudulent activities.

This affects everyone across the industry, from the insurers to the stakeholders. Therefore, fraud detection in big data and the insurance industry is easily a priority to the sector. 

The solution to this pervasive problem? The strategic application of big data.

By analyzing data on policyholders, claims, and other pertinent factors, insurers can identify behavioral patterns indicative of fraud. For instance, big data enables the examination of claims data to spot inconsistencies or anomalies, such as unusually high or frequent claims. 

Kanerika’s work with a global leader in the insurance sector illustrates this impact vividly. 

 

Case Study - Seamless Data Integration by Generative AI_ (1)

 

We tackled challenges like time-consuming manual data integration and the complexity added by emerging data sources like wearable devices and electronic health records. 

The solution? An innovative approach utilizing Kafka for automated data extraction, Talend for data standardization, and Gen AI models TensorFlow and PyTorch for seamless data integration. 

The outcomes were remarkable – a 22% increase in customer satisfaction, a 29% reduction in fraud, and a 37% decrease in claim processing time.

 

Use Case 5: Cost Reduction Initiatives

A primary business goal of any organization is to cut down its costs without compromising the quality of work. 

The integration of big data technology in insurance is a powerful tool in this regard. 

One of the primary areas where big data in insurance proves invaluable is in the handling of claims and administration. The traditional manual processes are not just time-consuming but also prone to errors, leading to financial losses and inefficiencies. 

An example of this in action is our work at Kanerika with a leading global insurance company

 

Case Study - AIML-Powered RPA for Fraud Detection (1)

 

We faced challenges such as manual processes in insurance claims leading to inefficiencies and financial losses, a lack of robust fraud detection systems, and inflexible processes hindering effective data analysis.

Our solution was to implement AI/ML-driven Robotic Process Automation (RPA) for enhanced fraud detection in insurance claims, significantly reducing fraud-related financial losses.

And, the outcomes were significant: a 20% reduction in claim processing time, a 25% improvement in operational efficiency, and a 36% increase in cost savings. 

 

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Use Case 6: Personalization of Services and Pricing Models

The insurance industry is shifting its focus from offering generic services to providing tailoring experiences and products to meet the unique needs of each customer.

What’s the secret? 

Analysis of unstructured data, which allows companies to craft services that resonate more closely with individual customer profiles.

Take, for instance, life insurance. By leveraging big data, these policies can now be customized by taking into account a customer’s medical history and lifestyle habits, tracked through activity monitors. 

A practical example of this approach is seen in our work at Kanerika with a leading global insurance company

 

Case Study - Efficient Data Integration for Insurance

 

We faced challenges such as manual processing of claim files leading to delays and errors, a lack of standardized data formats, and reactive processes causing bottlenecks. 

Our solution involved automating bordereau processing, which included data transformations to enhance efficiency and claim accuracy. 

The outcomes of these initiatives were transformative: the processing speed escalated from weeks to minutes, partner engagement improved by 50%, and partner onboarding became 65% faster.

 

Use Case 7: Optimizing Internal Processes for Efficiency

 A study by McKinsey and Company highlighted that automation could save up to 43% of the time for insurance employees. 

This is where the power of big data in insurance becomes evident. Big data technology in the insurance industry enables rapid processing of customer profiles. Insurers can swiftly access a customer’s history, determine the appropriate risk class, devise a suitable pricing model, automate claim processing, and thus, deliver superior services. 

An example of such optimization is evident in our work at Kanerika with a leading insurance company. We faced challenges with diversely formatted data from over 280 Managing General Underwriters (MGUs), which included information on policies, premiums, claims, and other services.

Our solution was to implement an AI/ML algorithm for auto-detecting data mapping from various sources to insurance systems. 

The outcomes were significant, with a 94% accuracy in AI-based mapping and automation, a 30% reduction in new onboarding processes, and the ability to support 38% more business with less staff.

 

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Common Pitfalls to Avoid While Using Big Data in Insurance

While big data holds immense potential for revolutionizing the insurance industry, it’s essential to navigate potential pitfalls to maximize its benefits. Here are some common pitfalls to avoid while using big data in insurance:

 

1. Overreliance on Data

One of the significant pitfalls of utilizing big data in insurance is relying solely on data without considering other factors. 

While data-driven insights are invaluable, it’s crucial to analyze them along with human expertise and industry knowledge. Failing to do so may result in overlooking critical nuances and making flawed decisions.

 

2. Lack of Data Security Measures

Failing to implement robust security measures can expose insurers to significant risks, including data breaches and regulatory penalties. 

Insurance companies must invest in encryption, access controls, and regular security audits to safeguard customer data and maintain trust.

 

data security

Data security has become a priority for insurers due to the growing risk of security breaches

 

3. Ignoring Data Quality Issues

The quality of the data used in big data analytics directly impacts the accuracy and reliability of insights. 

Overlooking data quality issues, such as inaccuracies, incompleteness, or inconsistency, can lead to incorrect conclusions and flawed decision-making. 

Regular data cleansing, validation, and maintenance processes are essential to ensuring data integrity and validity.

 

4. Neglecting Ethical and Privacy Considerations

As insurers gather and analyze vast amounts of personal data, it’s important to follow ethical standards and respect customer privacy. Neglecting ethical and privacy considerations can damage reputation and trust, leading to regulatory scrutiny and legal repercussions. 

Insurers must strictly follow:

data protection regulations

obtain consent for data collection

transparently communicate their data usage policies to customers.

 

5. Failure to Adapt to Changing Regulatory Landscape

Failure to stay abreast of regulatory changes and adapt compliance practices accordingly can expose insurers to compliance risks and penalties. 

It’s essential to establish stringent compliance frameworks, conduct regular audits, and engage with regulatory authorities to ensure adherence to evolving regulatory requirements.

 

Future Trends for Big Data in Insurance in 2024

As we look ahead to 2024, several trends are poised to shape the future of big data in insurance:

 

Big data trends for insurance in 2024

 

Trend 1 – Increased Adoption of AI and Machine Learning

Artificial intelligence (AI) and machine learning (ML) technologies are expected to play a more prominent role in insurance operations. 

Insurers will leverage AI and ML algorithms to analyze vast amounts of data quickly. 

This will help them identify patterns, and automate processes such as underwriting, claims processing, and risk assessment. 

This increased adoption of AI and ML will enhance efficiency, accuracy, and decision-making capabilities across the insurance value chain.

 

Trend 2 – Personalized Customer Experiences

Insurance companies will increasingly focus on delivering personalized customer experiences with the abundance of data available. 

By leveraging big data analytics, insurers can gain deeper insights into customer preferences, behaviors, and life events. From personalized pricing models to proactive risk management solutions, insurers will strive to enhance customer satisfaction and loyalty through personalized experiences.

 

customer experience

With growing competition, personalized customer experiences help insurers stand out and attract new customers

 

Trend 3 – Integration of IoT Devices for Risk Mitigation

The Internet of Things (IoT) presents vast opportunities for insurers to gather real-time data and mitigate risks effectively. 

In 2024, we can expect to see a surge in the integration of IoT devices, such as telematics sensors, smart home devices, and wearable technologies, into insurance offerings.

Insurers will leverage data from IoT devices to assess risk more accurately, offer usage-based insurance policies, and provide proactive risk prevention services. 

 

Trend 4 – Emphasis on Data Privacy and Ethical Use

As concerns about data privacy and the ethical use of data continue to rise, insurance companies will place greater emphasis on ensuring compliance with regulatory requirements and ethical standards. 

In 2024, insurers will invest in robust data privacy frameworks, transparent data governance practices, and ethical AI principles to safeguard customer data and maintain trust.

 

Trend 5 – Collaborative Data Ecosystems

Collaboration and data sharing will become more prevalent in the insurance industry as insurers recognize the value of utilizing external data sources and partnerships. 

In 2024, we can expect to see the emergence of collaborative data ecosystems, where insurers, technology providers, and other stakeholders exchange data and insights to drive innovation and address industry challenges collaboratively.

 

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Kanerika: Your Partner in Data Implementation

Big data in insurance benefits both customers and insurers. 

If you are an insurance company trying to reduce fraud and costs to offer better services, Kanerika stands as your ideal partner.

Whether it’s understanding policy trends, attracting customers, building loyalty, or adapting to societal needs, Kanerika’s team employs the latest in AI/ML, big data processing, and cloud-based integration. 

Let’s connect today for a free consultation!

 

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FAQs

What is insurance big data?

Insurance big data refers to the vast volumes of varied data that insurance companies collect, process, and analyze. This data, which ranges from personal information to claim history, is used to gain insights into customer behavior, risk assessment, and market trends.

How is data used in insurance?

Data in insurance is used for various purposes including risk assessment, fraud detection, customer segmentation, personalized policy pricing, and identifying new market opportunities.

How is big data used in underwriting?

In underwriting, big data allows for more accurate risk assessment by analyzing a wide range of factors such as past claims, social media behavior, and even telematics data from vehicles.

How big data technology affects the advancement of the insurance industry?

Big data technology advances the insurance industry by enabling more precise risk assessments, improving fraud detection systems, enhancing customer experience through personalization, and optimizing internal processes for efficiency.

How is big data used in insurance companies?

Insurance companies use big data for customer profiling, claim processing, fraud prevention, predicting future trends, and crafting tailored products and services.

For which function big data can be used by insurance company?

Big data can be used in various functions including marketing, underwriting, claims management, customer service, and fraud detection.

What data do insurance companies need?

Insurance companies need data such as personal details of policyholders, claims history, policy details, risk factors (like driving records), and external data like weather patterns for certain types of insurance.

Why is data quality important in insurance?

High data quality is crucial in insurance as it impacts risk assessment accuracy, policy pricing, fraud detection capabilities, and overall decision-making processes.

How does data analytics help the insurance industry?

Data analytics helps the insurance industry by providing insights for better decision-making, identifying patterns and trends, enhancing customer experience, and improving risk management and fraud detection.

Is there a database for insurance?

There are multiple databases for insurance, ranging from internal databases of insurance companies to shared databases like CLUE (Comprehensive Loss Underwriting Exchange), which provides claim history information.