Top 5 Analytics Use Cases For Insurance Companies

1. Top 5 Analytics Use Cases For Insurance Companies Two patterns have typically stuck with the insurance industry in general. Firstly, the insurance industry has, for long, remained very static. Bobby Bowden, Chief Distribution and Marketing Officer at Allied World, aptly captures this perception with his following quote. "If you could wake up an underwriter or broker from 1686, it would take only a few days to get them up to speed." Secondly, positive perceptions of trust, honesty and integrity have long baffled insurance agents, and the insurance industry in the global marketplace tends to be associated with public distrust. One nationwide Australian poll in 2013 saw Australians rank sex workers as more trustworthy than the insurance salespeople, with only politicians and door-to-door salespeople deemed lower the list. However, the Insurance Industry is at the cusp of transformational changes today as a result of technology in insurance. The tides are changing faster than ever before, and most of the assets that legacy insurance players have built over the years, are now becoming liabilities. It is reasonable to expect that the insurance industry will look much different in the next ten years than it has been in the past several decades.

2. So, what is causing the tectonics to shake so fiercely today? The answer is fusion of technology in insurance. Digital transformation such as analytics applications in insurance are bringing out radical innovations in product delivery and operating business models. For example, transformational tap and pay features are now becoming standard, in an industry where it used to take on average several weeks, or even months to settle claims. In an Accenture's Technology Vision For Change Survey, 86% of insurance executives surveyed said that the inroads of technology, such as analytics in insurance, is growing in an exponential, rather than a linear fashion. The verdict is unanimous - The survivors and winners over the next decade will be those who can innovate and integrate technology into their value offerings rapidly. It is not going to be insurtech Vs non-insurtech anymore. Technology will drive transformation and evolution across the entire value chain, from underwriting inspection, assessing hidden risks, policy pricing, to customer service management, claim settlement, and customer relationship management. A General Overview Of Insurance - Current State And Critical Pivots The rapid digitization has led to consumers becoming more demanding today, as well as the new competitors emerging. Moreover, in the post-2008 market, government pressures require insurance companies to store data which would be needed for regulatory purposes These factors are entirely changing the dynamics of the market in which insurance players have used to operate. With data analytics, insurance players are better able to assess risk by factoring in data that was not previously collected, such as data from smartwatches, telemetry, historical claims data, satellite imagery etc. New age insurance players are setting up innovative data infrastructure to capture data and apply data analytics, artificial intelligence and machine learning algorithms to deploy effective analytics in insurance. Here, public and private cloud data warehouses and data lakes have emerged as a vital part of their strategy to lower

3. the cost of processing claims and bring greater operations efficiency and business agility at lower prices. IoT is another technology that is disrupting insurance in a big way. Objects and people can now be monitored remotely, and data fed into applications of analytics in insurance to proactively manage risk exposure and expand usage-based insurance policies as well as better price the policy with accurate risk assessment. While analytics in insurance and other transformations open up exciting new possibilities, the accelerated pace of change threatens to give rise to unknown risks that organizations must learn to manage proactively. As the writer, Stewart Brand famously said, "Once a new technology rolls over you, if you are not a part of the steamroller, you are part of the road". Legacy insurance players know that what we are going to experience in the coming years is a significant changing of guards, and digitization needs to become both tightly integrated into the product as well as into the end-to-end customer value chain. Below We Will Take A Closer Look At Some Of The Innovative Approaches That Analytics Is Driving. 1. Improve Risk Scoring And Claims Processing Telematics data can help insurtech devise pricing based on the likely behavior of categories to pricing based on the actual behavior of individuals leading to a highly personalized rate management and behavioral policy pricing. Using data to calculate and assess risks enables better risk management, fraud detection and customized pricing, which can have a direct impact on the bottom line. Technology, such as analytics, IoT, cloud computing and blockchain, can deliver insurance companies/ insuretech unprecedented scalability to reach out to new target segments. Bots are replacing brokers in offices. Bots in the back office are actively gathering information, such as from drones, IoT, telematics, and social media data about prior insurers before payout. With so much data already being collected, bots can deliver massive benefits by quickly settling claims at the tap of a button.

4. However, to be instrumental data must be integrated across a diverse variety of sources and the ecosystem of platform users. This implies insuretech companies need to be able to manage the operational aspects, such as harmonizing processes and integrating systems. As an example, crop insurance companies are using drones to collect farm data that is difficult to access and monitor by human agents, and are developing unique AI solutions to tackle crop insurance and credit challenges. Cognitive credit farm scoring applications can automatically and accurately calculate how much yield a farmer is likely to make and are being employed to make accurate estimates of their creditworthiness. 2. Design Attractive Bundled Offerings Value-added insurance products are becoming more and more popular among insurers since they help drive more customer centricity and encourage more frequent touchpoints with customers. For example, risk prevention services and technologies are being served by insuretech companies as a bundled offer for lower premiums. In the area of Life and Health insurance, health management apps are designed to help people live with a disease or health condition, adhere to medication or take up a healthier lifestyle. In Non-Life insurance as well, services such as roadside assistance, travel agency services or home monitoring via smart home sensors by leveraging the 'Internet of Things technology are valuable add-ons. These packages are typically offered as an add-on to traditional car, travel or home insurance products, but are at times provided to non-customers as well. Customers are willing to procure these value add services as well as share more data with their insurers to avail lower premiums. Such remote data monitoring and value-added services will encourage healthier habits and lifestyles among the customers, such as safer driving, more exercise, etc. 3. Deliver Seamless User Interactions Social media can be integrated with the customer portal to offer ease of use, as well as collect important customer data points. For example, Kroodle, a Dutch insurance company, has enabled its customers to interact with and login directly with their Facebook credentials, and request for services, providing seamless customer connectivity. Social media data can also be utilized by insuretech companies to

5. investigate fraud - by comparing the social media activity of insurers with claims records. 4. Improve Customer Service 24*7 chatbots are helping people who use the app have engaging and hassle-free experiences with insurtech companies. It is estimated that by 2025, 95% of all customer-facing interactions across industries will be done with the help of chatbots. Legacy insurance companies are also rapidly innovating, to expand beyond their core offerings, and addressing the rapid explosion in consumer needs, demands and digitalization by devising innovative products and delivering superior customer experience. Instant and quick claims disbursal processes aided by AI, transforms the customer experiences and lowers the operational costs. Disruptive insuretech players, such as Lemonade, are using automation, chat bots, and analytics in insurance to process vast amounts of customer historical data for effectively handle underwriting inspections, representatives in settling claims in a matter of minutes, and helping them to be really well aligned with their consumers aid the customer service 5. Streamline Sales, Marketing And Distribution Across the board, the ability to capture and analyze data will remain a top priority for insurtech companies because it is the backbone of customer segmentation and identifying cross-sell and up-sell opportunities. Data such as telecalling data and customer data can be used to identify effective channels, compare profitability across different channels and drill down to identify the drivers of performance. Data analytics in insurance is helping capture the diverse customer data points, companies can also identify reasons for attrition, analyze campaign effectiveness and devise effective and targeted marketing strategies. Insurance analytics will continue to transform the business context, processes and the entire insurance product portfolio. Insurance Companies will have to become more forward-thinking and future-focused by investing in cutting edge technology and applications.

6. They must build deeper relationships on a substrate of trustworthiness and transparency, which will be aided by better data capturing, monitoring and analytics in the insurance industry. Such an approach will help define a successful strategy and align offering, processes and capabilities with the expectations and needs of the target customers along their end-to-end journey. It will require the companies to shift away from a traditional, product-focused set-up to deliver superior customer-centricity. How Are Companies Adapting And What Are The Biggest Challenges With Making This Pivot The current market environment is pushing the players in the insurance sector to rethink their strategies and operations and develop effective use cases of analytics in insurance to improve their business value and forge a loyal customer base. Insurers of the future will require the six key capabilities, albeit in varying degrees: data, customer centricity, technology, ecosystems management. Whatever the chosen strategy, insurers will have to make all their processes digital-enabled and data analytics in insurance will become a critical part of the toolkit. innovation, talent, partnership and As of tomorrow, insurance will be digital, or it will not exist. While insurance companies lag behind most other service industries including banking, insurers that wish to thrive in the future must improve and ramp up their processes quickly using cutting-edge analytics in insurance. Companies must adapt fast and learn how to use data analytics in insurance along with cloud, AI, and blockchain as enablers to pivot effectively to a more streamlined response to the changing market conditions. Companies that fail to understand it, and take effective measures risk getting left behind and this applies even to large legacy players who will be labelled anachronistic if they don't move fast enough. Change management is going to be very crucial. Companies need to be aware that developing the capability and recognizing the value from it will take time - Progress will Necessarily Precede True Success.

7. Innovative and forward-looking insurance companies which implement data analytics in insurance as a key-value enabler will continue to reap benefits and stay on top in the emerging market dynamics of the future. At Polestar Solutions, we help insurance companies derive success on top of their data from the stage of data capture and management, cataloging, profiling, automating pipelines, till the stage of deriving insights