The world is a conglomerate of emerging technologies, and to operate these complex gadgets, there is a significant reliance on tech-savvy professionals and great problem-solvers. To become a great solver of problems, one must acquire a set of specific skills. These skills are congruent with analytics. The extent of one’s analytical prowess defines their inevitable capability to work their way around these waters.
The rise of data science has been disrupting every industry since its birth, given the wide range of benefits data offers. One such industry is insurance, which has undergone a significant transformation, with data analytics blending into it.
Data analytics defies how traditional insurance is done and provides a vast deal of growth opportunities previously never uncovered. Organizations worldwide are adapting to this paradigm shift and enjoying the benefits it has to offer.
Well, you might be wondering how data analytics is related to insurance? What skills are required for a job in insurance data analytics? Is it a single skill or a collection of skills? How does it operate in practice? The answers to these – and many other questions –are sown in this article below.
What is Insurance Data Analytics?
Insurance data analytics refers to the process of collecting, analyzing, and processing the insurance-related data to extract valuable insights from it to manage risks and calculate the optimal insurance contracts to firms.
In essence, it’s a combination of data analytics and the insurance industry that helps insurance companies leverage the power of data and employ the big data techniques to quickly study the available data and convert it into valuable insight.
Related: How Data Analytics works in Finance?
Insurance has long been a data-driven industry, and data has always played a significant part in the operations of an insurance company. While every insurance company holds a considerable amount of data, most fail to gain the actionable insights they could achieve from all that data. And you can’t really blame them for it since the volume of data they possess isn’t easy to process and tame.
The fusion of data analytics into the insurance company serves just the purpose described. Not only does it help insurance companies tame the data they have, but it also helps them get actionable insights and deliver the correct data to the right person at the right time.
What Do Data Analysts Do in Insurance?
Conventionally, insurance firms relied on mathematical models to forecast outcomes and information acquired during health plan member onboarding to inform consumer interactions before the term “big data” was created — perhaps even before data as we now know it existed. Data is still necessary, but the sheer volume of data and how it is acquired and evaluated has changed dramatically.
This brings us to the subject of big data and insurance analytics. Insurers would be stupid not to embrace big data analytics to solve one of the insurance industry’s significant difficulties, especially with modern technology and such a vast amount of data at their disposal. Health insurance businesses, for instance, can no longer compete solely based on their health plans; today’s client demands complete transparency and a great experience throughout the member’s lifetime.
As a result of this transition in the market, health insurers must deliver more insightful suggestions to members based on their data to make better coverage and overall health decisions.
Responsibilities Of an Insurance Data Analyst
A good insurance data analyst needs excellent technical and quantitative skills to manipulate data and draw analysis. The capacity to detect patterns in data and understand what those patterns represent is most vital.
This is a brief yet complete definition of what is expected from an insurance data analyst. The following points summarize the responsibilities in detail:
- Create, modify, and run computer programs that extract, manipulate, and summarize data to be used as input in studies and reports.
- Examine the accuracy of insurance firms’ data, both in transactional detail and in aggregate, and assist them in correcting inaccuracies.
- Understand and own our business processes, and make recommendations for data management, error detection, quality control, process automation, or analytic methodologies.
- Support cloud computing strategic efforts by assisting with the migration of code, data, and processes and reconciling results with existing procedures.
- Create reports that are simple to comprehend. Present your internal boss and coworkers your analysis, ideas, progress, and results. Effectively communicate with customers and insurance authorities.
- Encourage a collaborative, high-performing culture. Continuously engage peer-review with coworkers to learn, teach, and ultimately deliver high-quality results.
- Assist with developing detailed project plans to ensure that projects are finished on time.
Data Analytics in Insurance: Use Cases
Improving Customer Service
Customers today expect customer service 24 hours a day, seven days a week. Insurance businesses can react to consumer inquiries instantly with automated responses via chatbots. Chatbots can provide contextually relevant insights, educate customers on process flows, compare policies, and recommend appropriate insurance products in addition to answering customer questions.
A typical vehicle claims experience, for example, entails a customer calling his insurer’s call center for assistance, waiting a long time, and being transferred to multiple customer service personnel before receiving the assistance he requires. Insurers may optimize this process by quickly obtaining the customer’s profile, accessing GPS locational data, and providing a customized and frictionless experience using modern data analytics. Such prompt and effective service makes the customer feel valued and increases brand loyalty.
Implementing Targeted Marketing Strategy
Various new marketing strategies, such as emails, texts, social media, and apps are being used to attract and engage potential customers due to the rise of digital media. This customized marketing method keeps insurers competitive and provides a monetary incentive.
Customers are more likely to respond to messages or advertisements tailored to their specific wants and requirements. In fact, according to a 2019 study, 90% of consumers say they don’t interact with messages that aren’t particularly relevant to them, while roughly 50% are likely to move to brands that personalize their communications better. Additionally, targeted marketing boosts conversion rates by 22%.
Data analytics can assist insurers in becoming more personalized and targeted when it comes to marketing. One approach to achieve this is by combining customers’ search data and analyzing their buying patterns to send personalized messages and promote good products to the target market in the appropriate channels to boost customer conversion rates. Insurance marketers can also use customer lifetime value (CLV) data to improve their focused marketing efforts and examine their results.
Assess Potential Risk
Insurers can use data analytics to better detect and assess each applicant’s risk before issuing a policy to them. Insurance risk managers now have better access to internal and external data and analytics than ever before, allowing them to undertake complete risk assessments. A customer’s risk profile may now be quantified over a set timeframe by examining past data from credit agencies, third-party vendors, and social media. Underwriters formerly relied on specific, set rules, basic statistical models such as profile and scoring models, and their intuitions for analyzing the risk of insurance policies. Underwriters may now create more accurate data-based predictions about a customer’s risk profile and apply these insights to premium pricing thanks to the availability of predictive analytical models.
Prevent Fraudulent Claims
According to the FBI, the entire cost of insurance fraud, excluding medical insurance, is more than $40 billion each year. Fraudulent claims have become more sophisticated in recent years. Typically, fraudsters would use various tools and strategies to try to game the system. It is critical to have an expert analyze client data and enhance current sub-optimal conditions to prevent fraud and protect customers’ genuine interests. Data specialists can determine the possibility of a bogus claim and spot patterns of suspicious activity by using predictive analytics and reviewing past data and customer habits. Artificial intelligence (AI) and machine learning algorithms can uncover fraud patterns and correlations likely to outperform human intellect.
Top 3 Professional Tools for Insurance Data Analytics
While the usual data analytics software can be used conveniently in the field of insurance data analytics, there are some specialized software available in the market. These software make it quite convenient and time-saving to analyze insurance-related data, which could otherwise take a considerable amount of time.
Let’s take a look at some of the best-known tools available in the industry right now.
1. Tableau Insurance Analytics
Tableau is a well-known software in the data analytics world, and if you have a data analytics background, you’d probably know of it already. Turns out, Tableau offers a specified software for dealing with insurance data. Not only does it let you deal with complex insurance data very conveniently, but it also lets you explore the insurance data in real-time and play around with it.
With Tableau, you don’t face any difficulty dealing with insurance data, and you can analyze it just like a regular dataset.
2. Perfect Quote
As the name suggests, Perfect Quote is a company that helps brokers and general agents have well-aware quotes for benefits. The great thing about this software is that it’s cloud-based and works for every insurance carrier.
If you’re an independent agent and want to offer your clients the omnichannel experience without any hassle, InsuredMine is the way to go. It’s a digital platform of integrated technologies that help grow your business by getting more leads, engaging more clients, and so on.
It’s a whole suite for an insurance agent and provides everything they need under a single roof. For example – CRM, Goal Management, Task Management, Charts, Marketing Automation, and so on.
How To Start a Career in Insurance Data Analytics?
To start a career in insurance data analytics, you need to set strong feet in the insurance industry first. The skills related to data analytics are only good once you’ve had a solid grasp of the concepts that make you a good insurance analyst first.
So, getting a major in insurance is always the recommended path to get into the industry. Once you have a solid understanding of how the industry works, you can have a minor in big data tools to study the insurance data and find your way of analyzing large insurance datasets.
The best thing about data analytics is that even if you don’t have a degree in it or you’re not skilled enough, there are hundreds of bootcamps and online courses available on the internet that you can study for, sometimes for free as well. Also, there are many software available that don’t require high skill in the domain and will let you use big data techniques by only having a hang of it.
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To conclude, data analytics is and will always remain a vital part of an insurance company, even from a business point of view. Like every other business, the insurer needs to foresee any future transitions or adaptations possible in the market to guide them through any forthcomings. Being a pro-consumer industry and providing better financial services to potential clients, an educated decision needs to be made; data analytics can solely be made possible to study the trends and improvise, hence avoiding any inconvenience.
Throughout the article, we have seen how data analytics plays a vital role in the insurance industry and how it’s drastically changing how insurance companies go about their data. We also saw some of the most critical use cases of data analytics in the insurance industry and some of the most popular tools that professionals currently use in insurance companies.