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Why Data Science is a Game Changer for Insurance Industry Today and Tomorrow

This article is Part 1 in a 5-part series titled "Data Science Use Cases in Insurance."

Today everyone is talking about Artificial Intelligence (AI). But what is AI? AI is a science that enables computers to think like human beings.

Is AI a new concept in the field of computer science? No, AI has been around for more than half a century now. (The term “Artificial Intelligence” was coined in 1956).

The only things which have changed in the 21st century are:

  • The computing power at the disposal of the common person
  • Data available for analysis

In the 1980s, developing even a mildly complicated computer program required supercomputers, but now the same power is available on laptops. This has enabled commercial organizations to exploit the concepts of AI for getting more output from their businesses.

AI is a vast field and covers many aspects such as computer vision, robotics, structured and unstructured data like text, images, and voice.

For this article, we will focus on a related field called Data Science.

What is Data Science?

DataRobot defines Data Science as “a field of study that combines domain expertise, computer science concepts, and knowledge of mathematics and statistics to extract meaningful insights from data.”

Data science practitioners apply machine learning algorithms and AI techniques to gain valuable insights from data in various forms: numbers, text, images, video clips, audio clips, and more.

An example of the use of Data Science is the prediction of how a given individual will behave - based on analysis of past behavior of multiple individuals. These insights are used by businesses for commercial decisions and are thus translated into tangible business value.

To make this happen, we need data. Let us have a look at how the data is moving in the 21st century.

Growth in Data

The world is now producing enormous amounts of data, with exponential growth in data generation seen in recent years. According to Statista, “in 2020, the amount of data created and replicated reached a new high – reaching more than 64 zettabytes. Over the next five years up to 2025, global data creation is projected to grow to more than 180 zettabytes.

What are the sources of all of this data?

Each one of us is providing and adding to this data knowingly and unknowingly! We are contributing to this data through the sites we visit, our online transactions, the pictures we take, our social media postings, and so forth.

So how do we store and access all of this data that is generated?

Data Storage

Data storage also traversed a long journey from punch cards of the 1930s, floppy diskettes of the 1980s, pen drives, in house data storage to data servers and data farms of today.

On the accessibility side, there is a huge improvement in communication technology, allowing the transfer of large amounts of data quickly. Data speed was discussed in KBps (kilobytes per second) in the not-too-distant past. Today, we talk about speeds only in MBps (megabytes per second) or GBps (gigabytes per second).

That’s an improvement of more than a thousand times!

The latest talk is about connectivity offered by satellite communication in the coming years, with an experimental offering planned for 2022.

These technological leaps forward in data storage and access speed have enabled organizations to store more data and analyze it much quicker than in the past.

Against this backdrop, there has been much recent commentary on Big Data and Data Science: the ability to process and draw valuable conclusions from large quantities of data, from various sources, much faster than ever before.

Data Science and Humanity

Data Science is already starting to transform many aspects of modern life and spread its wings from health care, science, and research to politics, sport, and daily purchases. Dreaded diseases like cancer are treated using image analysis and AI; the spread of CoVID-19 and the infrastructure needed is estimated using predictive models running on AI principles. Predictive models of organizations like Amazon are slowly and silently influencing our choices of daily essentials.

Data Science and Insurance

When Data Science is transforming all aspects of modern human life, does it have any applicability in the insurance business, the business of managing probabilities of events or happenings? The answer is obviously “yes.”

Data consumed in the insurance industry is collected in numerous ways that include:

  • Telematics devices installed in motor devices
  • Wearable fitness devices digitized medical records submitted for claims
  • Customer feedback – explicitly recorded or based on their postings on social media, etc.

As a result, all this data is being used and driving innovations in the insurance industry for:

  • Advanced risk management and personalized pricing
  • Offering customized customer services and products
  • Managing distribution and reach to customers
  • Bringing in process and cost efficiencies in the organization
  • Meeting compliance and tracking fraudulent transactions

Conclusion

These innovations are forcing insurers to become hyperintelligent, AI-driven organizations. To stay relevant, insurers have to bring in support systems in their daily life to design and meet the expectations of evolving customers of this century.

In the next article in the series, we will talk about how AI and Data Science principles are helping insurance carriers to offer personalized products and meet customer expectations in the current era of the digital world. So, stay tuned.

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Yogesh Dhavale
Yogesh Dhavale
Yogesh is a part of the data science team at Aureus. He holds a Bachelor’s Degree in Engineering and Technology from Veermata Jijabai Technological Institute (VJTI), Mumbai.

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