How Data Analytics is Changing the Game for the Insurance Industry

• Read original article here

Datafloq enables anyone to contribute articles, but we value high-quality content. This means that we do not accept SEO link building content, spammy articles, clickbait, articles written by bots and especially not misinformation. Therefore, we have developed an AI, built using multiple built open-source and proprietary tools to instantly define whether an article is written by a human or a bot and determine the level of bias, objectivity, whether it is fact-based or not, sentiment and overall quality.

Articles published on Datafloq need to have a minimum AI score of 60% and we provide this graph to give more detailed information on how we rate this article. Please note that this is a work in progress and if you have any suggestions, feel free to contact us.

The age-old insurance industry is finally ripe for transformation given the use of technologies such as data analytics, AI-ML, etc. ensuring an overall digital-first approach. The powerful new combination of technologies and design can enable businesses to drive decision-making with accurate real-time insights and optimize operational efficiency. 

With so much market uncertainty, tight regulations for insurance providers, and changing demographics and expectations of insurance buyers, it is important to build capabilities with data analytics and AI-ML in order to stay profitable, relevant and sustainable throughout the entire value chain. 

Change is the Only Constant 

In addition to these changes, we are also witnessing a change in consumer behavior. Consumers are increasingly becoming more digital-friendly – comparing prices and services online, assessing risks, claims management, fraud detection, etc. thereby leaving decision-making in their hands. With more and more customers moving online for their insurance needs, new data points are getting created which in turn improves future interactions. The opportunity to leverage this customer shift in behavior is huge. 

This shift is increasingly enabling insurance companies to move online to not only ensure greater efficiency, but also push the industry towards becoming more customer-centric and personalized. The next generation of customers seek personalization and efficient service when and where they need it. 

Technology in insurance is growing at an exponential pace, driving overall digital transformation for the industry, resulting in evolution across the entire value chain. However, when we speak of digital transformation, we see how much the application of data analytics has changed the industry; right from underwriting inspection, assessing risks, policy pricing, claim settlement and management to customer relationship management. Here we examine some innovative ways in which data analytics is digitizing the insurance industry; 

Operations: Right from claims automation to text mining and optimizing resources, analytics ensures a delightful approach to the entire life cycle of an insurance buyer. Claims filing automation, IVR call responses, grievance redressals and chatbots help in people management, resource allocation and identify areas of improvement. 

Improve claims management and processing: The most important touchpoint in the insurance industry is claims management and processing, and by automating it with analytics can help in not only settling claims, but also staying aware of possible fraudulent claims. Analytics helps with fraud detection, reduction of claim cycles, automation of FNOL – First Notice of Loss, tracing non-credible claims and much more. With text mining, it is possible to go through troves of data created by customers and identify early interventions to reduce costs of the company. 

Pricing: By using customer segmentation analytics, insurtech companies can ensure high conversions rates by targeting the right insurance policies. This results in customized pricing and highly personalized rate management and pricing policy. Also, it helps with staying on track of competitor pricing and helps determine insurance premiums which are most suited. Predictive analytics can help make data-based predictions on what pricing models to implement, thereby improving customer loyalty. 

Customer acquisition and retention: The industry is largely shifting towards becoming more customer-centric. When it comes to customer acquisition, data analytics can help with gauging the effectiveness of a marketing campaign, upselling offerings and services, spend optimisation, analyze marketing trends and budgets and much more. For example, business intelligence tools can help understand pain points of different target demographics and segments resulting in the implementation of a much more strategic approach. 

When we look at customer retention in the insurance industry, today, chatbots help connect with customers where they are and can enable more meaningful interactions with insurance companies. Not only chatbots, but innovative products/ solutions as well as apps are increasingly changing the nature of engagement between the age-old insurance industry and consumers. For example, instant claims disbursal aided by AI helps lower operational costs and improve customer experience. Value-added insurance products have also become popular; for example, insurtech companies now offer bundled offers and packages on lower premiums and with value add ons.  

Risk analysis: When it comes to risk management, predictive analytics can help classify policy-holders into low or high risk and help better assess risks in a more personalized and streamlined manner. What-if analytics can also further help an insurtech company mitigate future risks like expense allocation, fraud detection etc. and prepare for a safer tomorrow. 

The Future of the Industry 

The current market conditions are pushing the industry towards an overall change, and with opportunities such as blockchain, AI, analytics as well as automation, it is increasingly becoming more and more digitized. However, with opportunities come challenges, and in this case with more personalization comes the need for a responsibility to the consumer with transparency and privacy.