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Unlock Actionable Insights with Insurance Data Analytics

Oct 10, 2022
Data Analytics in Insurance

Insurance companies are looking to mitigate risk, optimize performance and meet rising consumer expectations to stay competitive in a data-rich world. While aggregating more data, they still face many challenges that may prevent them from harnessing the true power of analytics. With important data siloed in PDFs and spreadsheets, underwriting is still a paper-first process. To evaluate risk, underwriters still have to scroll through different documents, looking for data.

Implementing Data Analytics in the Insurance Industry

Data have always driven insurance; it’s time to rethink how data aggregation and analysis are optimized in underwriting processes. Over the years, the industry has tried to make processes around underwriting easier; yet not many efforts have been taken to focus on improving the data science aspect of underwriting. If insurers want greater efficiency and informed risk & pricing decisions, we can’t only focus on offloading work from the underwriter. We need to look for solutions that help underwriters extract and gather all the data in one place that can be later used to provide deeper insights. Underwriters should focus on what they’re best at analyzing data, uncovering patterns and making informed decisions based on the holistic view of the customer.

  • Implement solutions that gather all the siloed data underwriters need, collecting data from PDF and spreadsheets into one place, eventually reducing that mode of communication. 
  • Manage data around the critical underwriting decision steps of triage, pricing and risk evaluation.
  • Integrate data-driven, analytics approach into existing workflows for a seamless experience.
  • Set up quality controls and other measures to augment the quality and consistency of the underwriting process.

Data Challenges in the Insurance Industry

Constantly evolving business environments are increasing competition and risk. Several challenges like theft and fraud, further demand insurers to generate insights from data to understand customers, analyze risks, prevent fraud and enhance pricing mechanisms.

The industry has greatly benefitted from advanced data and predictive analytics to make data-driven business decisions. Artificial Intelligence (AI) in Insurance core system has empowered companies with high-level data and information leveraged into improved insurance processes and new opportunities. Let’s discuss an auto insurance example. A new range of product lines and business functions focusing on data analytics are emerging. Rather than just concentrating on internal data sources like loss histories; auto insurance started using behavior-based analytics and credit score in their analysis.

Challenge 1: Unstructured data and legacy system obstructs from making actionable insights

In a conventional insurance setup, seamless integration is often difficult among different data depositories, preventing insurers from utilizing the full potential of data. Insurance companies are currently grappling with unstructured data and legacy systems, which prevents these companies from extracting value and making the data actionable.

Solution: Build a single integrated platform that integrates new and existing data sources and makes data actionable by leveraging advanced analytical tools.

P&C insurance carriers must plan to meet emerging customer needs by integrating data sources to improve analytics in claims, underwriting, pricing and customer service. Integration of these sources will enable the development of new channels—such as using sensor data to improve underwriting/pricing on properties—and analytical capabilities like applying predictive modeling to reduce the probability of claims and losses.

Challenge 2: Deployed actionable data at the product level and not at customer level

Customer insights are often lost in silos– by business or product lines. Every insurance business has its processes for capturing and managing data and, more often at the product level rather than at the customer level leading to inconsistencies. Moreover, companies fail to recognize customers at different stages of the policy life cycle due to lack of predefined terms on customer insights.

Solution: Build a customer-centric analytics solution for customer retention and increasing profit, enhancing customer value across the policy lifecycle.

Insurance companies are now recognizing the requirement for new data sources. However, with more data increasing daily, it becomes challenging for analytics executives to find valuable insights. Addressing the challenges arising from significant data volumes requires an enterprise data management strategy and implementing analytics tools and platforms that can further use data analyzing and processing to achieve meaningful insights. 

How Data Analytics Can Improve the Customer Experience.

Data analytics is a powerful tool that companies can use to improve the customer experience. By analyzing customer data, companies can gain valuable insights into customer behavior, preferences, and needs. This information can then be used to make informed decisions about products, services, and overall customer experience.

Here are some specific ways that data analytics can improve the customer experience:

  1. Personalization: Companies can use data analytics to personalize the customer experience by understanding each customer’s unique preferences, needs, and behaviors. This can include personalized recommendations, targeted marketing, and customized customer service experiences.

  2. Improved product offerings: Data analytics can help companies identify customer preferences and needs, leading to the development of products and services that are better aligned with customer expectations. This can result in higher customer satisfaction and loyalty.

  3. Faster and more efficient customer service: Data analytics can help companies identify and resolve customer service issues more quickly. For example, companies can use data analytics to identify common customer service issues and proactively address them before they become widespread problems.

  4. Better understanding of customer behavior: Data analytics can provide companies with a deeper understanding of customer behavior, including how they interact with products and services, what factors influence their purchasing decisions, and what motivates them to engage with a brand.

  5. Optimized marketing strategies: Data analytics can help companies optimize their marketing strategies by identifying which channels are most effective in reaching and engaging customers. This can lead to more targeted and effective marketing campaigns, resulting in a better return on investment.

Overall, data analytics is a critical tool for companies looking to improve the customer experience. By leveraging data and insights, companies can make informed decisions that drive customer satisfaction, loyalty, and engagement.

Role of Technology in Data Analytics

Technology plays a crucial role in data analytics and is essential for collecting, processing, and analyzing large amounts of data. Without technology, data analytics would be a manual, time-consuming, and error-prone process. Here are some ways in which technology supports the data analytics process:

  1. Data Collection: Technology allows organizations to collect vast amounts of data from a variety of sources, such as customer transactions, social media, and website interactions. The data can be stored in databases or data warehouses for further analysis.

  2. Data Processing: Technology provides the tools and infrastructure needed to process and clean the data, removing any inconsistencies or errors. This includes data integration, data normalization, and data cleaning.

  3. Data Analysis: Technology provides the tools and algorithms needed to analyze the data, uncovering insights and trends. This includes statistical analysis, machine learning, and artificial intelligence.

  4. Visualization: Technology provides tools for visualizing the data, making it easier to understand and communicate insights. This includes dashboards, charts, and graphs.

  5. Automation: Technology automates many of the manual tasks involved in data analytics insurance, freeing up time and resources for more strategic tasks. For example, data can be automatically extracted, processed, and analyzed, with results presented in real-time.

  6. Collaboration: Technology enables collaboration and sharing of insights and results across different departments and teams, ensuring that everyone is working with the same data and insights.

In conclusion, technology plays a crucial role in data analytics by providing the tools and infrastructure needed to collect, process, analyze, visualize, and automate data. By leveraging technology, organizations can gain insights into their data, making informed decisions that drive growth and success.

Conclusion

Data itself has no value, and it needs to be thoroughly processed and analyzed to turn it into actionable insights. With data generation expanding at an alarming rate, deploying new strategies and tools to make data work through actions becomes crucial. At KMG, we partner with clients to turn extensive data into actionable insights using dashboards built using Tableau, PowerBI, & SSRS, among other tools.

Please visit to know more about our Insurance Data Analytics capabilities.

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