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Data Science in the Insurance Industry

  • July 05, 2023
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Meet the Author : Mr. Bharani Kumar

Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of Innodatatics Pvt Ltd and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 17 years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.

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Introduction

Data is the engine that powers the insurance sector. In 1688, a coffee shop in London that is today known as Lloyds of London served as the insurance industry's first location. It is the market that first began keeping data and using it to assess and analyse marine dangers.

Data and the law of big numbers have long been important in the insurance sector. The businesses with the best data make effective use of it for pricing and underwriting.

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Role of an Actuary and a Data Scientist

Actuary in an Insurance company works with historical data to anticipate an event and to quantify the value of that event. It would not be wrong to say Actuaries were/are the original data scientists as they have been working with data for centuries now. However, the tools, techniques, and technologies used by the Data Scientist today are more advanced than the traditional methods used by Actuaries. Data scientists use Coding, Visualization, and Machine Learning techniques whereas Actuaries mostly rely on the old models and tables for their workings. Having said that, the essence of what they both do is the same which is to extract the truth from data and to predict something about the future.

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Digitization in the Insurance Industry

Due to its enormous size and outdated systems, the insurance business has a reputation for being infamously sluggish to adopt new technologies. It's interesting to note that this industry's digital development is being driven by its consumers. Insurance businesses are changing and adapting to meet the needs of the millennial generation as a result of their expectations.

Customers are what are accelerating this industry's digital growth. Insurance businesses are being forced to alter and adapt as a result of millennials' high expectations.

Customers now prioritise buying the greatest experience above the best product, which is causing insurance firms to stop creating products that are just based on risk and instead focus on building more preventative and predictive goods.

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How Data Science can be used in an Insurance Company

An example would be a Health Insurance policy. Traditionally health insurance policy is sold on a pure risk model where any medical claim is reimbursed after hospitalization. The only touchpoint usually in this traditional model is when the product is sold and at the time of claim payout. However, in a modern health insurance policy, the focus is shifted to keeping the customer healthy so that the claims rate is minimized. In this model, the touchpoint with the customers is constantly maintained from selling the policy till the claim is paid through the renewal of the policy. This communication is maintained through the various Health APPs and trackers which record the customer’s physical activity and vitals. The customer not only gets the best experience in this case but also get rewarded for staying healthy by way of reduced premiums at the time of renewal.

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Uses of Data Science in the Insurance Industry

In the insurance sector, data science is mostly used to support timely decision-making by the firm. Broadly, Data Science will help Insurance companies in:

  • Understanding the risks better
  • Acquiring the right risks
  • Personalize risk pricing
  • Pay the rightful claims
  • Personalize claims service
  • Customer acquisition and retentions
  • Fraud detections
  • Enhance operational efficiencies
  • Development of new products

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Challenges

Insurance businesses are still having trouble completely embracing technology and transitioning to a data-driven industry. An insurance firm may have problems such as a lack of exact and thorough transactional data, inconsistent data, and a lack of interest in spending money on technologies that may improve the quality of the data. The lack of skilled workers is another major issue that businesses must deal with. Finding resources with a strong grasp of the company as well as data, arithmetic, and algorithms is difficult.

Conclusion

As a Data Scientist, you have more opportunities and capabilities today of gathering more data be it from Sensors, IoT devices, Smart Cars, Smart Homes, etc. which will help enable in creating more innovative insurance solutions.

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