16 Ways Insurance Companies Can Use Data and AI
How insurance leaders can use the power of data and AI to transform the industry, from claims analytics to risk selection and beyond
There is a growing recognition that insurers can introduce data, analytics and AI into virtually all of the important insurance functions and workflows, including product development, pricing and risk selection, underwriting, claims management, contact center optimization, distribution management, reinsurance, and understanding and shaping customer journeys.
Here are some of the exciting ways insurance companies can put data to work. To learn more about insurance data trends, download the full ebook.
Underwriting and risk selection
For personal and small commercial lines, best-in-class insurers are connecting their first-party data — including Internet of Things data, such as telematics and wearables — to a growing collection of third-party demographic and firmographic data to create a more comprehensive profile of a person or commercial business. These broad customer profiles are being used as inputs to machine learning models to better inform risk selection. Coupled with low-code configuration and automation, organizations are driving toward a more automated, standardized and objective underwriting process while improving pricing accuracy and loss ratios, cutting costs and shortening quote-to-bind times.
In addition, these broad profiles can also be used as data prefill to streamline an organization’s quote process, reducing the number of questions the agent or customer needs to respond to, and creating an exceptional customer experience.
Rating and product development
As insurers look to innovate faster and launch new products or bring existing products into new geographies, being able to ingest data more efficiently from core systems and beyond into their rating models is critical for capturing market opportunities before the competition does. With all the data at their fingertips, actuaries and data scientists are empowered to more rapidly model frequency, severity, loss cost, and enable insurance product managers to file new rates with regulators.
Claims analytics and claims copilots
The claim function provides a fantastic set of use cases for the application of AI, ML and generative AI capabilities. By leveraging first- and third-party data, organizations can positively impact administrative and loss adjustment expenses, and overall take advantage of significant opportunities to improve efficiency, enhance customer satisfaction and reduce fraudulent activities.
There are several ways to surface claims analytics to claims adjusters, including the use of claims copilots. Leveraging an insurer’s data and AI, copilots are interactive, virtual assistants that can help complete routine tasks. Copilots can provide claims summarization insights, as well as guidance and recommendations that can improve adjuster productivity, and free claims teams up to focus on providing empathy and support to customers when they need it most.
10 examples of claims analytics opportunities
Predictive modeling for claims frequency and severity
Forecast the likelihood and potential cost of future claims based on historical data, enabling better financial planning and reserve allocation, and ensuring that insurers are prepared for future payouts.
Claims triage and prioritization
Machine learning models can help automatically triage claims, allowing insurers to prioritize high-value or high-risk claims for faster processing, improving efficiency and customer satisfaction.
Claims outlier detection
Predictive models can attempt to identify less obvious high-cost claims early in the process, alerting claims professionals to the potential need to direct the claim to appropriately skilled resources. Such early intervention can help lower claim severity.
Fraud detection
Advanced analytics and machine learning models can detect unusual behaviors or inconsistencies in claims submissions, helping to flag and investigate suspicious claims before payouts are made.
Cost containment and leakage reduction
Analytics can identify areas where costs may be leaking due to unnecessary expenditures or inefficiencies in the claims process. Insurers can use this information to tighten their processes and reduce overall claims costs.
Text and sentiment analysis
Gen AI capabilities can help analyze unstructured data from claim notes, customer emails and call transcripts to glean insights into claim complexity, customer sentiment and potential dissatisfaction or fraud.
Litigation risk detection models
If a claim is flagged as being more likely to result in litigation, insurers can focus on negotiation and settlement strategies more aggressively, prioritize where claims can benefit from more experienced adjusters or develop more informed allocations of claim resources.
Claims settlement optimization
By analyzing historical claims settlement data, insurers can identify optimal settlement strategies that balance cost efficiency with customer satisfaction. This can include identifying cases where early settlement might be beneficial or where alternative dispute resolution methods could be more effective.
Customer experience and satisfaction
Analytics can help insurers understand the claims process from the customer’s perspective, identifying bottlenecks or pain points. Such insights can drive claims process improvements, enhancing overall customer experience and loyalty.
Integration with external data
Integrating claims data with external sources — such as weather data for natural disaster claims or telematics data for auto insurance claims — can provide deeper insights into claim circumstances and validity, aiding in more accurate and fair assessments.
Customer analytics
As insurance shifts toward more digital-centric models, the role of data and analytics in understanding and engaging customers has become more crucial than ever.
With more touchpoints (websites, mobile apps, social media, etc.), there’s a richer data pool to draw from for customer analytics. Coupled with raised customer expectations for personalized experiences, customer analytics is central to delivering a greater level of personalization by enabling businesses to understand and predict customer behavior and preferences.
Common focus areas for customer analytics include:
Customer engagement strategies: The digital era has expanded the ways in which customers interact with businesses, from social media to chatbots and beyond. Analytics helps in understanding the effectiveness and customer satisfaction across these varied channels, allowing businesses to optimize their engagement strategies.
Customer segmentation analytics: Customized experiences often result in higher customer satisfaction and loyalty. By segmenting customers into distinct groups based on their behaviors, preferences and needs, insurers can tailor their services and communications more effectively.
Sentiment analysis: Gen AI capabilities are changing the way insurers can infer customer sentiment, helping gain insights into customer emotions and opinions from every touchpoint and channel. This insight allows insurers to gauge overall customer sentiment, identify areas of dissatisfaction, and make targeted improvements to enhance the customer experience.
Journey analytics: This involves tracking and analyzing every touchpoint a customer has with the insurer, from initial inquiry and policy purchase to claims and renewal. Insights from journey analytics help insurers identify bottlenecks or friction points in the customer journey, enabling them to optimize processes for a smoother, more satisfying customer experience.
Secret to success
Insurance companies don’t need to fly solo on all this, though. The right solution can help you build the necessary foundational data and analytics capabilities so you can confidently — and profitably — meet the future head-on.
To learn more about how Snowflake can help, please visit snowflake.com/financial-services