The Role of Big Data in Modern Insurance Software

July 8, 2024

1995 "Casino" movie main character, Sam "Ace" Rothstein was a brilliant bookie, always winning. And he managed that by knowing everything. He knew which horse is being doped, which team is struggling internally etc. This way he almost cheated the risk.

And insurance is all about risk. The more you know, the lesser the risk. And now in the era of digital revolution "knowledge" equals "data". And to handle all this data, insurance companies use Big data.

Big data is changing the insurance industry in insurance software by allowing insurers to make data driven decisions, improve customer satisfaction and manage risk.

In this article, you'll read:

  • How to use big data in modern insurance software?
  • How much money can it add to your bottom line?
  • How to implement big data seamlessly into your business?

Big Data for Predictive Analytics

Big data can be harnessed, especially with predictive analytics. By analyzing historical and current data through statistical models and machine learning algorithms companies can predict outcomes. In the insurance business, this means being able to forecast customer needs, risks, optimize operations, and make better decisions.

it also takes into account structured and unstructured data:

  • structured data – Policyholder details Claims records Financial transactions,
  • unstructured data – Social media posts, Customer emails, Images and videos from accident scenes.

By combining data from telematics devices, connected home technologies, social media and customer records, insurers get a 360-degree view of their customers. This allows them to identify potential policy cancellations, detect fraud and tailor marketing.

Read more about 360 Customer View: Insurance Software: The 360 Customer View Module

Advanced algorithms can process large data quickly, predict claims and detect fraud, refine risk assessment and pricing and reduce underwriting costs. Insurance organizations using predictive analytics can catch early signs of customer dissatisfaction and improve retention and streamline workflows.

Fraud Detection and Prevention

Fraud is a big problem for the insurance industry resulting in huge financial losses and higher premiums for honest policyholders. Investigating every claim for fraud is impractical and bad for customer experience. That’s where big data comes in, with a sophisticated approach to fraud detection and prevention.

The 2021 study by the Coalition Against Insurance Fraud found that 80% of respondents use predictive modeling to detect fraud, up from 55% in 2018.

Insurers can flag potentially fraudulent claims with high accuracy by identifying patterns and anomalies in data. Predictive analytics and machine learning algorithms are used to scan through large amounts of data from various sources including social media and public records to identify inconsistencies and suspicious behavior. 

Personalized Offers for Better Customer Satisfaction in Insurance Sector

We'd risk a statement that it's not the quality, speed, price, or brand that will make the client stick with you. It's the experience. 

Customer experience leaders in the insurance industry outperformed their peers in Total Shareholder Return (TSR) by 65 percentage points for Property and Casualty (P&C) insurers from 2017 to 2022.

46% of customers say customer experience is a top factor when selecting an insurance provider .

At the top of the industry where the scale is immense, and a small change can mean huge difference, big data is a game changer.

Insurers are using big data analytics to:

  • Understand customer behavior, preferences and needs
  • Present insurance products and services that delight customers
  • Customize insurance offers using individual customer data

Insurers can now:

  • Predict which customers will cancel their policies and offer them better product options
  • Retain customers and build stronger relationships
  • Offer add-ons to frequent travelers

Claims Automation

How your company is handling claims is the make-or-break mechanism. A customer happy with your claim processing will stay almost forever, if unbothered. An unhappy one will become an anti-ambassador.

And claims are the biggest pain point for any insurance customer, especially in terms of the time it takes to resolve the case.

Big data is helping with claims management by:

  1. Fraud detection and prevention: Big data analytics can spot patterns and anomalies in claims data and flag suspicious claims for further investigation. Machine learning can be trained on historical data to improve detection rates.
  2. Real-time claims processing: By processing large volumes of data in real-time, insurers can automate many parts of the claims process and speed up the whole thing.
  3. Predictive analytics: Big data can predict the likelihood of a claim being fraudulent based on historical data and patterns, so you can be more proactive with fraud prevention.
  4. Better risk assessment: By analyzing vast amounts of data from multiple sources, insurers can get a better handle on the risks associated with different types of claims and make better pricing and underwriting decisions.
  5. Customer experience: Big data analytics means you can process claims faster and more accurately, so customers are happier.
  6. Automated data extraction and verification: Technologies like Optical Character Recognition (OCR) and Natural Language Processing (NLP), powered by big data and AI, can extract and verify information from documents, reducing manual data entry and errors.
  7. Personalized claims handling: By analyzing customer data and claim histories, you can tailor the claims process to individual customers and potentially get lower risk claims resolved quicker.
  8. Catastrophe management: Big data helps you predict the likelihood and impact of natural disasters more accurately so you can allocate resources better during a disaster and speed up recovery.
  9. Process optimization: Big data analytics can find bottlenecks in the claims process and help you streamline operations and improve productivity.
  10. Preventive maintenance: By using IoT data and AI-powered predictive analytics, you can notify customers when their insured property needs maintenance and potentially prevent claims before they happen.

Leveraging Big Data in Different Insurance Segments

Big data serves as a crucial tool in enhancing profitability and revenue for insurance companies. By utilizing data analytics, these companies can accurately predict safe driving behaviors, streamline life insurance applications, detect fraud, automate claims, and offer personalized pricing. This has allowed insurance companies to increase operational efficiency, reduce costs, and better cater to customer needs, thereby boosting profit and revenue.

Auto Insurance

  • Nationwide: Uses data for safe driving predictions.
  • Progressive: Dynamic pricing based on driving behavior.

Health and Life Insurance Companies

  • Ladder: Streamlines life insurance applications with predictive outcomes.
  • Haven Life: Prices policies using mortality risk metrics.

Property and Casualty Insurance

  • Allstate: Detects potentially fraudulent claims.
  • Amica: Integrates GIS for real-time disaster response.
  • Allianz: Personalized travel insurance offers.

Challenges and Future of Big Data in Insurance Industry

Wherever technology emerges, challenges emerge with it. 

The top challenges regarding big data in the insurance industry are:

  1. Organizational and cultural challenges – Many insurance companies are still to become data-driven businesses. There is often a lack of a data-driven and organizational structure to support the use of big data. Companies need to evolve their leadership roles and strategies to bring data across the whole business.
  2. Legacy systems and siloed data – Insurance companies have multiple legacy systems and vendors which creates data silos that are hard to break down. This means data is fragmented across different systems built for specific purposes and it’s hard to get a holistic view of the data.
  3. Data quality and consistency issues – With data coming from multiple sources like call centers, online forms, mobile apps, etc., there are inconsistencies in how data is defined and collected across different departments. This leads to data quality problems and makes it hard to integrate the data.
  4. Lack of talent and tools – Many insurance companies don’t have the necessary talent, skills and tools to leverage big data. There’s a shortage of data scientists and analysts who can extract insights from large datasets.
  5. Data privacy and regulatory concerns – As insurance companies collect more personal data, there are growing concerns around data privacy, security and regulatory compliance. Companies need to balance data usage with ethical considerations and legal requirements.
  6. Real time data processing – Processing and analyzing data in real-time to enable quick decision making is a challenge for many insurers.
  7. Accuracy of data – There are concerns around the accuracy of big data sources which can impact the insights derived from the data.

However, we can't stop the progress.

Big data has the potential to transform the insurance industry across the entire value chain, from underwriting and pricing to claims processing and customer service.

Insurers are expected to create new business lines and models to capture the value of data and analytics. Companies that can monetize their data assets will gain significant competitive advantages.

Big data will enable more accurate risk assessment and personalized pricing. Insurers will be able to offer usage-based policies and tailor products to individual customer needs.

AI is helping fraudster, but it also serves insurers. Advanced analytics and AI will enhance fraud detection capabilities and streamline claims processing, making operations more efficient.

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