The Emergence of Data Science and Analytics Assignment 2022

The emergence of data science and analytics has brought a drastic change in the way
companies plan and execute their business initiatives. With the availability of all kinds of
data and artificial intelligence (AI) based tools to analyse it, the managers have been
empowered as never before. They can access a huge amount of data on any sector on a click
of a button.
The data science and analytics have led to innovative models and completely transformed
businesses and industries. Nowadays business decisions are driven by data, governments and
companies have engaged data scientists for carrying out research and analysis to extract
relevant and meaningful information, which can be used to make better decisions. (Warnock,
2019)
Data analytics is particularly useful where companies have to deal with a large amount of
data that springs from a diversified customer base. Data analytics techniques are immensely
helpful for high-risk business portfolios. Data techniques even bring out the risk profile,
personal preferences, behaviour and other detailed information about customers, which helps
to chalk out suitable strategies to target them.
It is very useful for the insurance industry has been employing various data analysis
techniques even before the digital era. It has been dependent on statistics and using
mathematical models for data analysis to predict property loss and damage for centuries. The
insurers have been collecting massive data and maintaining a record of about their customers,
which was updated every time a customer made a claim. Thus, the emergence of data science
and analytics has proved a boon for the insurance industry, it has not only to improve
efficiency but also helped to widen and diversify the database that facilitates personalised
approach towards customers.
With the advent of data analytics, insurance companies have got access to a much wider
range of information sources for more accurate risk assessment. They are exploiting the full
potential of data analytics to improve customer experience, settling claims expeditiously and
at lower costs and eliminating frauds. Armed with AI-based data techniques, insurers are
using smarter predictive analytics to predict risks and process and settle claims with minimal
human intervention. They are also turning to external data sources to gather more details
about claimants, particularly for matters like verification of identity.
The data analytics can be effectively used to predict new emerging risks in cyberspace,
supply chain disruption and other areas. Proper assessment of risks helps ineffective
management of these risks and make insurance business more rewarding. The insurance
industry is venturing into new areas and premiums are registering a healthy growth across the
world. Big Data technologies are being effectively used to monitor, analyse the customer data
to attract new clientele and retention of existing customers.
Insurers are now in a position to deliver highly personalised and relevant insurance
experiences with the help of artificial intelligence and advanced analytics. They have access
to a wide range of demographic data, including individual preferences, behaviour, lifestyle,
interests, and other details. All such information is very useful as these days the consumers
look for personalised tailor-made offers, loyalty benefits and wider options. ("What is
customer analytics (customer data analytics)? - Definition from WhatIs.com", 2019)

Further, it is possible to carry out segmentation of customers based on their financial status,
sophistication, age, location and other relevant characteristics using appropriate data analytics
tools. This kind of segmentation allows insurers to offer specialised products and services by
taking into consideration coincidences in their preferences, attitude, behaviour and other
traits. It also makes it possible to resolve issues and find solutions relevant to particular
groups of customers. Predictive tools even provide information about the future capacity of
customers to invest in various insurance products and they can tap their potential by
maintaining strong communication using automated artificial intelligence-based tools. These
tools are very helpful in detecting fraudulent activity, suspicious conduct and identifying
links between suspicious activities to spot the fraud schemes not noticed earlier.
It is hardly surprising that the insurance sector is benefitting the most from data science.
However, no technology is without its negative aspects. The data science and analytics also have its
pitfall. Some researchers maintain that the quality of commercial analytical models may not be
trustworthy. They may produce misleading or even incorrect outcomes if the assumptions of
their theoretical foundation do not fit the data. As such, these tools could lead to wrong
decisions and may cause losses to the insurers.

References
Warnock, C. (2019). Understanding Data Science and Analytics in Marketing. Retrieved 20
September 2019, from https://lab.getapp.com/marketing-analytics-data-analysis-in-
marketing/
What is customer analytics (customer data analytics)? - Definition from WhatIs.com. (2019).
Retrieved 20 September 2019, from
https://searchbusinessanalytics.techtarget.com/definition/customer-analytics
Top 10 Data Science Use Cases in Insurance. (2019). Retrieved 20 September 2019, from
https://medium.com/activewizards-machine-learning-company/top-10-data-science-use-
cases-in-insurance-8cade8a13ee1