Create Dynamic Solutions When You Combine Insurance Analytics and AI

The insurance industry tends to have the reputation for being slow to consider and adopt new technologies. In some ways, it’s understandable since traditional approaches to pricing, rating, and engaging customers worked well in the past, and many insurers didn’t see the need to shake up the status quo.

Yet today, there’s a new opportunity to take advantage of new innovations and capabilities. AI now plays a crucial role in insurance analytics due to its ability to process vast amounts of data, identify patterns, make predictions, and automate tasks.

Insurance analytics using AI can also provide several important advantages, especially over past approaches to attempting to analyze data quickly and effectively enough to deliver real business value. For example, AI algorithms can now handle vast amounts of structured and unstructured data much more efficiently than manual methods or basic statistical tools. This allows for deeper analysis, identification of complex patterns, and extraction of valuable insights that might not be apparent through conventional means.

Why Haven’t More Insurers Adopted AI?

Many insurers are still struggling with old technology or legacy systems. For example, traditional pricing and rating systems tend to be individual siloed applications that still require far too much time and manual effort to attempt to make them work. This “cobbled-together” approach often leads to significant delays, internal inefficiencies, and missed opportunities to improve results and gain a competitive edge.

Why is this? Legacy systems were typically designed for structured data processing and lack the computational power and scalability required to handle high volumes of data, especially so much unstructured data common available to insurers in today’s increasingly digital environment.

These outdated systems also have a hard time with data analysis – especially the need for advanced analytics – making it extremely challenging to derive timely, meaningful insights for decision-making. These older systems simply weren’t built to take advantage of advanced AI and deep learning capabilities necessary in order to build complex predictive models and detect subtle patterns and anomalies in data.

Unfortunately, attempting to integrate these systems isn’t the answer. The rigid, usually monolithic architecture and siloed nature of legacy systems can’t be integrated with today’s modern insurance analytics and AI tools and platforms.  

Current Challenges: Personalization

Here’s one real-world example of how old technologies and status quo approaches may be holding insurers back: product personalization. The ability to create highly personalized offers enables banking and insurance companies to develop and deploy compelling, AI-powered offers and bundles – all to provide a more streamlined customer journey that improves the bottom line.

Yet until now, insurance companies have relied on basic demographic data and historical claims information for product personalization. This inevitably led to generic offerings that didn't meet individual customer needs effectively.

Traditional approaches lacked the flexibility and real-time adaptability required to provide personalized experiences tailored to specific customer preferences and behaviors. Manual processes and limited data analysis capabilities made it challenging to capture dynamic changes in customer requirements and market trends, resulting in stagnant and less relevant product offerings.

Overcoming Pain Points with Advanced AI Analytics Solutions

There is a better solution. Modern, best-in-class insurance analytics now enable insurance carriers and financial institutions to take advantage of data, modeling, and AI-driven recommendations to better understand their customers’ goals and meet them at the right time.

The first way that insurance analytics and AI-powered analytics help insurance companies is in managing high volumes of data. Insurance carriers and banks must deal with massive amounts of data related to policies, claims, customer information, risk factors, and market trends. AI systems can efficiently process structured and unstructured data, perform advanced analytics, and extract valuable insights that humans might overlook or take longer to analyze.

AI can also help automate routine tasks such as data entry, document processing, claims processing, and risk assessments. This frees IT and other employees to focus on more strategic, higher-value initiatives, activities for increased operational efficiency and cost savings.

In the case of improved decision-making, AI-powered insurance analytics enable insurers to build predictive models for various scenarios, such as predicting claim likelihood, estimating claim amounts, and forecasting market trends. These models improve decision-making, optimize resource allocation, and enhance business strategies.

Integrating AI with analytics helps insurers and banking institutions in three important ways:

1. Connect, integrate, and deploy the best models possible into pricing and product personalization frameworks.

2. Leverage even more data and use it to simplify more third-party integrations.

3. Empower all users to use data to make more accurate decisions, faster than ever before.

To revisit our example of product personalization described above, AI has revolutionized product personalization in insurance through the use of advanced analytics, AI-powered algorithms, and real-time data processing capabilities. AI enables insurers to analyze vast amounts of structured and unstructured data from multiple sources, including customer interactions, online behaviors, social media sentiment, and external market data.

This comprehensive data analysis helps insurers understand individual customer preferences, risk profiles, and life events more accurately, allowing for highly targeted and personalized product recommendations, pricing strategies, and coverage options.

Turn AI-Driven Analytics into a New Competitive Edge

AI delivers several advantages over past approaches to managing insurance analytics, primarily due to its ability to handle large volumes of data, automate processes, and generate actionable insights.

AI-driven personalization not only improves customer satisfaction and retention but also enhances insurers' ability to manage risks effectively and stay competitive in a rapidly evolving market landscape.

By leveraging insurance analytics and AI, insurers can overcome challenges and deliver highly tailored products and services. Advanced insurance analytics allows for the real-time processing and analysis of vast datasets, enabling insurers to identify unique customer patterns and preferences, thereby creating more personalized and compelling offerings.

Next Steps

Interested in learning more AI capabilities, and how abo how Earnix is now delivering Intelligent insurance operations? Watch our on-demand webinar, “The Price is Right: Using Predictive Analytics to deliver personalized insurance,” today.