Unlocking the Power of Data Analytics for Insurance

Data Science helps find innumerable patterns and stories in every form of data, be it decoding consumer behavior and what they’re likely to do next, to information about their health, wellness, financial and non-financial assets. The ones who can convert this data into valuable insights have hit a gold mine. For the insurance industry, it is no different. 

Whether it is the most basic descriptive and diagnostic analytics to advanced predictive and prescriptive analytics, the use of data analytics in the insurance sector is creating a seismic shift by unlocking the power of technology and leveraging untapped business opportunities.

Insurers have been collecting precious data about their customers through proposal forms, claim settlements, health check-ups etc. Adopting newer and smarter ways to analyze the data gathered can assist insurers in making better decisions, improve business processes, and enhance user engagement. Understandably, insurers are increasingly relying on data transformation services to tackle the wave of data and tap into new possibilities. 

New-age insurers are investing in data and then incorporating analytics into their business operations to build a more streamlined response to the changing dynamics with the consumers. As organizations become more proficient in integrating cutting-edge technologies such as Artificial Intelligence (AI) and Machine Learning (ML) to get real-time insights, implementing the results in insurance-related transactions like claims management, underwriting, and policy administration will enable better predictive analysis. Some significant benefits will include: 

• A deeper understanding of customer behavior with an accurate risk assessment, personalized premiums, and added value on a sustainable basis for a better customer experience. A person’s social media and other online activity can support the underwriting process by determining whether the information provided by the policyholder is authentic, and supporting risk assessment
• Improve business outcomes in cost optimization, faster product time to market, quicker and better claims settlement, market competitiveness, and newer business models. A state-of-the-art Policy Administration System (PAS) with a Product Configurator can ensure new products’ time-to-market of less than 1 day
• Create deeper granularity in individual risk profiles and protect insurers from emerging risk exposures. Patterns can be identified in case of a common link among the insured, similar to the fact that people with co-morbidities and people over sixty years were considered at high risk to the COVID-19 infection
• Dynamically segment users and needs, model behaviors, identify exceptions, adjust policy prices, and optimize business strategies. A pattern where we consider multiple factors like a person’s age, sex, geography, lifestyle, medical history and all other data which can zero in on personalized premiums for increasing the win rate
• Identify newer horizons of growth opportunities using advanced analytical techniques like neural networks and clustering. Using various methods and the data from social media and search engines, Neural Networks and Machine Learning techniques can help increase insurance policy sales
• Accelerate the process of claim settlement systems and make it more user-friendly with the help of digital technologies. Mobile app-based paperless claims along with supporting data science algorithms which can partially or fully analyze information and assist insurers in verifying the authenticity and process claims in record time

Understandably, data analytics has a transformative impact in the insurance sector, from pricing to claims, from distribution to underwriting. In the end, it’s all about turning data into a competitive business advantage for insurers, and those who can create a roadmap for effectively leveraging data will have an edge going into the future. The transition to digital business models and tech-powered customer experiences is now more of an expectation than an added value for consumers. And that’s just the tip of the iceberg for the insurance industry. 

Leveraging customized insurance platforms powered by the intelligence of predictive analytics, AI, and ML to optimize all areas of their operations is imperative. This will act as a key differentiator when it comes to staying two steps ahead in this rapidly evolving digital age and creating more tailored services. Fundamentally, insurers need to:

• Establish sufficient data infrastructure, expand their analytics capabilities, predict, and govern the risks involved
• Build a data lake for scalable management of all types of data with quick access for analysts 
• Streamline analytics with decision-making mechanisms to drive concrete actions
• Allocate the right resources to the technology functions and hire the right expertise 
• Automatically track the outcome of each action

In a nutshell, insurers must harness digital disruption to better connect with consumers and cater to their evolving needs. Data analytics in the insurance sector has evolved dramatically over the years and will continue to do so. So, the faster we make data and analytics central to our business, the earlier we will build a resilient, adaptable, and data-empowered organization that drives positive business outcomes.