5 Ways to Apply Data Analytics in Insurance Business
Data analytics can be understood as the analysing of the raw data available so as to draw conclusions from the same which can be used for setting strategiesand making business decisions on the basis of data.
And big data analytics is about the sorting of the large volume of data which might be structured, unstructured or even semi-structured and collected from different varied sources for making use of the same in making informed decisions for business or other scenarios.
To assess a risk and estimate the chance of that risk recurring in the future, the insurance industry has typically relied on historical data. Data is collected at every touch point of the consumer buying journey as a result of digitization and the advancement in the use of digital technology across insurance company activities. Insurers now have access to a massive amount of data, including real-time data, that they may use to supplement past data and gain a better knowledge of the risk or customer with whom they wish to do business. Insurers may use data analytics to derive deep insights from their data, which they can then apply to their products or the processes, or even the services to drive business value across the insurance value chain.
Forward-thinking insurers are embracing AI or the Artificial Intelligence, data analytics, and machine learning to make better business decisions in a much faster way, along with ensuring of offering novel services and products, and improve the outcomes earned by the business. Let’s take a deeper look at five unique ways to use data analytics in the insurance industry:
Designing of Customized Products
In today’s competitive insurance business environment, customer-centricity is critical and vital. Today’s customers want every product and service to be individualised and fitted to their specific requirements where comes the prominence of data and data analytics.
Data acquired about every client which might be the publicly available data along with the data collected from consumer interactions with the carrier can be useful in determining the customer’s requirements and preferences for the business and development of products and services.
Natural Language Processing which is also known as text analytic scan, for example, create data engines that search many social media platforms and review websites, as well as create their own digital content, in order to analyse and comprehend what an insurer’s consumers are saying about the products and services provided to them. Insurers can leverage meaningful insights produced from such data to improve the consumer insurance experience, from delivering new, personalized insurance products or the services to an easy-to-use user interface for purchasing those products to personalize client communications.
Improving the Customer Service
The ability to provide good and outstanding customer service will have a multiplied and deep relevance or impact on retention and client loyalty. Customers today expect customer service 24 hours a day, seven days a week, and instant gratification. Insurance businesses may react to consumer enquiries instantly with appropriate automated responses via chatbots thanks to an effective analysis of customer purchase and behavioural data.
Chatbots can provide contextually relevant insights, compare policies, educate customers on process flows, and even make recommendation about appropriate insurance products in addition to answering customer questions. Data and data analytics are used to power the conversational chatbots, which deliver personalised experiences, new product quotes, and are even programmed to handle a customer’s insurance claims. A typical vehicle claims experience, for example, would entails a customer calling his insurer’s call centre for waiting a long time, assistance, and being transferred to multiple customer service personnel before receiving the assistance he requires.
Insurers may optimise this process by obtaining the customer’s profile quickly, gaining the access to the GPS locational data, and providing a customised and frictionless experience using modern data analytics. Such effective and prompt service makes the customer feel treasured and valued which would help in increasing the brand loyalty and credibility. And this can only be made possible using data and the big data or data analytics.
Implementing the Targeted Marketing Strategies
Various new marketing strategies, such as social medias, texts, emails, and apps, are being used to attract and engage potential customers as a result of the rise of digital media and technology. This customised marketing method not only keeps insurers competitive, but it also provides a monetary incentive.Customers are more likely to respond to advertisements, emails or messages that are tailored to their specific wants and requirements.
In fact, according to a study conducted, it was reported that more than 90% of consumers say they don’t interact with messages that aren’t particularly relevant to them, while roughly around 50% are likely to move to brands that personalise their communications better. Additionally, targeted marketing boosts conversion rates by more than 20%.
AI or Artificial Intelligence powered big data and data analytics can assist insurers in becoming more personalised, proactive, and also targeted in their marketing activities.
Insurers can use the big data and data analytics to improve the efficacy of their marketing initiatives by using a focused marketing approach.
And for boosting customer conversion rates, one can adopt the approach of consolidating customers’ search data and analysing their buying behaviours in order to send personalised messages and promote acceptable products to the target market through the right channels. Customer lifetime value or the CLV data can also be used by insurance marketers to improve their focused marketing efforts and examine their results.
Assessing the Potential Risks
Insurers can use the big data analytics or even the data analytics to better detect and assess each applicant’s risk before they are being allotted or issued with a policy or a product. The Insurance risk managers now have an improved and a much better access to both external and internal data and also data analytics than ever before, allowing them to undertake complete risk assessments.
By examining past data from credit agencies, third-party vendors, and social media, a customer’s risk profile may now be quantified over a set timeframe. For analysing the risk of insurance policies, underwriters formerly relied on specific, set rules, basic statistical models such as profile and scoring models, and their intuitions.Thanks to the availability of predictive analytical models, underwriters may now make more accurate data-based predictions about a customer’s risk profile. They can then apply these insights to premium pricing increasing consumer base and profit margin.
Prevention of Fraudulent Claims
As per studies conducted and reported, the entire cost of insurance fraud, excluding medical insurance, is more than INR 40 billion each year. Fraudulent claims have become complex and well-orchestrated in recent years. Typically, fraudsters would use a variety of tools and strategies to try to game the system. It is critical to have an expert analyse client data and enhance current sub-optimal conditions in order 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 or AI and machine learning algorithms can uncover fraud patterns and correlations that are likely to outperform human intellect.
Premium costs for other claimants are influenced by fraudulent claims. Insurance fraud costs the average family more than INR 30,000 per year, according to the studies conducted. Addressing fraud through data analytics and eliminating false claims will save the company a large amount of money in pay-outs, allowing the company to offer competitive insurance prices to all consumers.
Hence, we can conclude that data, bug data, data analytics and the big data analytics hold high importance in today’s world. Be it for business or otherwise, data is the new gold and everyone are behind this for a good reason.
But business should ensure that there is ethics established in the manner in which data are obtained and utilized. As majority of this include the sensitive information and thereby should not trespass the privacy of the person.
Insurance business is one of the sensitive industry due to which personalisation is important and the probability of frauds are also high. So,keeping this in mind the obtaining, maintaining and utilisation of the data is of high virtue demanding the keeping up with the technology and changes along with maintaining privacy and safety.