The Use Of Data Science Inside The Insurance Industry

Data drives the insurance industry. Insurance had its humble beginnings in 1688 in a coffee shop in London known as Lloyds of London. It is a marketplace that began storing data and using it to analyze and evaluate maritime risks. Data and the law of large numbers have always been important in the insurance industry. Companies with the best data use that data to improve underwriting and pricing.

Actuary and Data Scientist Roles

An actuary in an insurance company uses historical data to forecast an event and quantify its value.Because of their long history with data, actuaries may rightfully claim to be the first data scientists. However, the tools, techniques, and technologies used by Data Scientists 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 old models and tables. Having said that, the essence of what they both do is the same, which is to extract the truth from data and predict the future.

Insurance Industry Digitization

Because of its massive size and legacy systems, the insurance industry is notoriously slow in digital transformation. Surprisingly, it is the customers who are driving this industry's digital growth. The expectations of millennials are forcing insurance companies to change and adapt to meet their needs.

Customers are the ones driving this industry's digital growth. Insurance companies are changing and adapting to meet the expectations of millennials. Customers are no longer looking for the best product; they are looking for the best experience, which is forcing insurance companies to shift away from developing pure risk products and toward more preventive and predictive products. (Refer the data science course for more information on this.)

How Can Data Science Be Used in the Insurance Industry?

A Health Insurance policy is an example. Traditionally, health insurance policies are sold on a pure risk basis, with any medical claim reimbursed after hospitalization. The only touchpoints in this traditional model are when the product is sold and when the claim is paid. In a modern health insurance policy, however, the emphasis is shifted to keeping the customer healthy to reduce the claims rate. In this model, the customer touchpoint is maintained from the time the policy is sold until the claim is paid and the policy is renewed.

In this case, the customer receives the best experience possible and is rewarded for staying healthy by receiving lower premiums at the time of renewal.

Data Science Applications in the Insurance Industry: Data Science is primarily used in the insurance industry to assist companies in making the right decisions at the right time.

Challenges

Insurance companies are still struggling to embrace digitization fully and fully transform into data-driven enterprises. A lack of detailed and precise transactional data, inconsistencies in the available data, and a lack of appetite for investing in systems that will help improve the data quality are some of the major issues an insurance company faces. Another significant challenge that businesses face is a skills shortage. It isn't easy to find resources that understand statistics, math, algorithms, and business.

Conclusion

As a Data Scientist, you now have more opportunities and capabilities to collect more data, whether from sensors, IoT devices, smart cars, smart homes, or other sources, to help you create more innovative insurance solutions. Do you have ambitions of becoming a data scientist? Students enrolled in the finest data science course in Pune, get experience by contributing to real-world projects developed by industry experts.