Best Use Cases for Machine Learning in Group Insurance

Machine learning (ML) is a powerful tool for group insurers. Already, almost three-quarters of insurers surveyed for Deloitte’s “2022 Insurance Industry Outlook,” said that they are increasing their spending on artificial intelligence (AI). 

By leveraging the vast amounts of data they already have, group benefits insurers can use ML to create tangible benefits. The first priority: apply ML algorithms to the processes most closely defined by top-line or bottom-line impact and to improving customer experience.  

Here’s an overview of the short-term opportunities available to group insurers today: 

Improving quotes and proposals with machine learning

Within the quote and proposal process, RFP intake, interpreting, and data cleansing present considerable opportunities to increase process efficiencies and revenue growth through intelligent automation 

Success in this area puts insurers on the way to better submissions, more accurate underwriting, and a better user experience. Even a small percentage-point shift in close rates can significantly impact profitability.  

Automated proposal generation and dynamic pricing 

Group insurers can use ML to analyze an employer’s needs, preferences, and other relevant factors, like the number of employees and their industry, to offer a risk assessment and generate customized coverage options and pricing. 

Group insurers can use ML algorithms to analyze past proposals and quotes, then predict the types of policies most likely to be accepted by potential customers. Logically, the next step is to automatically craft quotes and proposals that are more likely to be accepted. 

More sophisticated insurers could even use ML to analyze real-time data on market conditions, customer behavior, and other factors and dynamically adjust pricing for quotes and proposals. By responding more quickly to changes in the market, insurers can become more competitive and profitable.   

Improving claims handling with machine learning  

Claims generate a wealth of data and offer many opportunities to increase process efficiencies and cost savings via ML, which can speed claims payouts and lead to improved customer retention due to higher customer satisfaction. 

Many carriers already route claims based on their complexity and the skill of their claims handlers, either based on the organizational knowledge of senior line managers or with ML.  

An ML model can leverage that organizational knowledge and automatically classify claims on a spectrum from routine to complex, or automatically approve low-dollar claims that meet specific parameters.  

Claims that fall outside those parameters could be automatically flagged for further review or attention by more senior claims professionals. This can reduce costs and improve the speed and accuracy of more complex claims processing. Over time, the insurer may identify additional details and patterns that help them move to a more advanced multiclassification model. 

Fraud detection using machine learning

Insurers can use ML to analyze claims data and identify inconsistent patterns, which may indicate fraudulent activity. For example, an insurer might use ML to analyze the type of treatment or service provided, the provider who submitted the claim, or the timing and frequency of claims.  

By identifying potentially fraudulent claims early in the process and applying appropriate resources, insurers can reduce costs and further improve claims processing accuracy.  

Predictive fraud detection using machine learning 

This data also can work for you to train ML models to recognize and identify patterns in future claims that suggest fraudulent behavior and generate alerts that a claim should be investigated.  

Predictive modeling can help insurers anticipate and plan for claims more effectively, reducing costs and improving the overall claims experience for policyholders. For example, an insurer might use ML to analyze data on the types of claims most common in a particular industry, geography, or demographic and then use this information to anticipate future claims in that group. 

Customer sentiment analysis 

ML can be used to analyze customer service feedback on social media, review sites, and other channels and identify patterns in customer sentiment related to slow response times or lack of communication. That knowledge can help insurers identify areas where they need to improve their claims-handling processes and make changes to improve customer satisfaction.  

Machine learning for continuous improvement

Group insurers can’t afford to sit on the sidelines when it comes to real-world applications for ML and AI.  

The benefits can be startling. According to QuantumBlack AI by McKinsey, traditional ML and analytics offer the insurance industry a potential annual value of $888 billion for marketing and sales and $91 billion for risk. 

By leveraging the vast amounts of data they already have, group benefits insurers can offer tangible benefits for internal and external customers and create momentum to further their journey into ML. 

To learn more, download “You Already Have What it Takes: Machine Learning for Group Insurers.”  

Then contact FINEOS to start your ML journey.  




2022 Insurance Industry Outlook by Deloitte

QuantumBlack AI by McKinsey

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