Big Data & Analytics:

The New Face of Insurance and How It Impacts You


One out of every 10 insurance claims in the United States is a fraudulent claim and according to the Federal Bureau of Investigation, the total cost of non-health insurance fraud alone is estimated to be more than $40 billion USD per year. That translates into an increase in annual premiums for the average U.S. family of between $400 and $700.1 Imagine then, the global impact.

It is no secret that the insurance industry has long been afflicted by challenges such as insurance fraud, policy lapse prevention, risk assessment, litigation and a host of other pressures that impact insurer revenues, underwriting decisions and premium rates assessed to consumers. But there is a new dynamic that is opening the door for long overdue transformation and helping reshape an ever-changing industry that is notorious for playing catch-up with technology to minimize its challenges. The game changer? Big Data and Analytics.

"The total cost of non-health insurance fraud alone is estimated to be more than $40 billion USD per year."

– “Insurance Fraud.” Federal Bureau of Investigation,

New Sources of Data

Big data and analytics are drastically changing business models across industries, and in an ecosystem driven by real-time data, insurance is finding ways to transform traditional models too. From data gleaned from online behaviors to sensors in consumer goods, new sources of data are everywhere. “[The] rapid integration of technology in [everyday] life has created a proliferation of data, presenting unprecedented opportunities to use advanced analytics to leverage new information – about potential markets, risks, customers, competitors and natural disasters.”


Researchers have identified two new sources of data that are particularly relevant to the insurance industry:

  1. Auto-Generated and Stored Data – Data that is directly linked to our online behavior. This includes data shared via social media channels, online shopping, and personal search and browsing activities.

  2. Sensor Data – Data that streams from sensors built into consumer goods such as appliances, automobiles, tech wearables, and drones. This data tends to be more fragmented and specific to real-life functions. 3 This data may also be drawn from IoT-connected devices.  

So, what does each of these new sources of data tell us and how is insurance impacted? Personal data tied to online behavior can reveal information about habits and lifestyle. This data may be used to complement or substitute more traditional forms of data collected by insurers and insurance agents and may in turn be used in scenarios such as reducing the time and effort involved in risk assessment and underwriting decisions.

Sensor data is key to helping insurers expand their service capabilities by delivering a better overall customer experience. One such example is using data from an IoT-connected automobile to provide parking or roadside assistance to customers. For a connected home, automated responses to sensor-detected issues such a water leak could be an insurer provided service. A water leak would, as another example, automatically dispatch a plumber to the home.4


Searches: Google has and approximate 90% market share in searches.

User Penetration: Facebook has a penetration of about 89% of Internet users.5

5 Zingales, L. and Rolink, G. (2017) “A Way to Own Your Social-Media Data”. New York Times, June 30, 2017.

How it Impacts You

Impact on Carriers

The promise big data and analytics holds for insurance carriers is, well, big. Consider these numbers: A 1% improvement in the loss ratio for a $1 billion insurer is worth more than $7 million on the bottom line.

Opportunities for carriers can be best defined using three general categories:

  1. New propositions: Data has a direct effect on the development of new products and alternative business models, including peer-to-peer insurance, on-demand insurance, usage-based insurance, product bundling, as well as insurance products covering new types of risk.

  2. New Engagement and Distribution Models: Improving customer interaction by means of virtual assistants, digital brokers, chatbots and robo-advisers and using big data and artificial intelligence for enhanced customer segmentation, targeted marketing and dynamic pricing helps carriers create a connected ecosystem with consumers.7

  3. Process automation: The goal of process automation is to automate or improve efficiency of internal processes using big data and artificial intelligence. Straight-through processing enables the automation of parts of the value chain or even the entire value chain, including underwriting, claims handling, risk management, finance and investment management as well as litigation, regulatory reporting and compliance.

“The growing adoption of Big Data technologies has brought about an array of benefits for insurers and other stakeholders. Based on feedback from insurers worldwide, these include but are not limited to an increase in access to insurance services by more than 30%, a reduction in policy administration workload by up to 50%, prediction of large loss claims with an accuracy of nearly 80%, cost savings in claims processing and management by 40-70%, accelerated processing of non-emergency insurance claims by a staggering 90%; and improvements in fraud detection rates by as much as 60%.”

-Market Report:

Big Data in the Insurance Industry: 2018 – 2030 – Opportunities, Challenges, Strategies & Forecasts

Impact on Brokers and Agencies

For agencies, big data means customer acquisition tools and insight into key business performance improvements such as pinpointing tasks that need to be automated to optimize operations and increase margins. “It’s also super useful for aligning staff towards overall goals and giving them an indication of how they’re doing day-to-day quantitatively in reaching those goals.”9

Impact on Re-Insurers

Enhancements in technology and data are bringing new light to the concept of risk assessment for re-insurers. One of the most transformational areas of risk assessment is the ability to determine “emerging risk,” such as climate change, genetically altered crops, and a host of other factors that have the potential to impact claim rates.  However, despite the opportunities big data and analytics offers re-insurers, according to a recent Deloitte reinsurance administration survey, data quality, technology and analytics are among the top four pain points for reinsurers.10 


As a result, many reinsurers are more focused than ever on getting their data story straight. With proper implementation and tools, re-insurers have the opportunity to use new sources of big data to move past data silos and open up insights to save margins and streamline operations.

Impact on the Consumer

61% of organizations say that forming better relationships with their customers provides a competitive advantage.11 It’s no surprise then that carriers, brokers, agencies, and re-insurers alike have their eye on improving the customer experience. Using data and analytics, consumers can expect benefits such as: 

  • Lower premiums

  • Opportunity to correct premium-impacting behaviors to avoid increased premiums (thanks to digital monitoring and communication by the carrier or broker/agent)

  • Products tailored to meet the specific needs of consumers

  • Customizations

  • On-demand insurance

Going Beyond Data

Market Perspectives

In the Excellence in Risk Management XVI survey conducted by Marsh LLC in early 2019, improving the use of data and analytics in insurance was the top risk management priority for nearly half of survey respondents (47% to be exact). But other industry surveys signal that while big data and analytics is of key importance, 50% of CEOs do not believe their organizations are able to innovate effectively.