Carriers must respond rapidly and appropriately to customers at every stage of the insurance process, from policy selection to billing to claims processing to renewals. Customers expect fast, personalized, and accurate responses to queries. Failure to meet those expectations increases the likelihood of churn, lost revenue opportunities, and increased marketing costs. Addressing these challenges increases customer lifetime value (CLV) and reduces marketing costs.

Insurers use Altair RapidMiner insurance analytics solutions to analyze transaction histories, customer interactions, and social media activity to understand customer preferences and needs. These models automatically identify behaviors that indicate dissatisfaction, including complaints, reduced engagement, or changes in purchasing habits, allowing insurers to take proactive steps to address issues before they lead to churn.

Warranty fraud – including falsification of purchase dates, exaggerated damage claims, and claims for non-existent issues – can cause financial loss, increase operating costs, and impact brand value. Product warranty fraud is increasingly difficult to spot among millions of claims. It’s also unique in that first-party and third-party fraud are both common; purchasers can be dishonest about the nature of claims and third parties can file false claims on behalf of innocent consumers.

Insurance analytics models incorporating Altair’s patented decision trees are particularly useful in analyzing warranty claims data since they can rapidly assess claims by evaluating product types, price points, claim amounts, time since purchase, customer demographics, claim histories, and many other factors to highlight claims requiring further analysis. Decision trees are computationally efficient and can process large datasets quickly, which is crucial when timely identification of questionable claims can prevent further losses. They’re also highly explainable, since the tree structure provides a clear, visual representation of the decision-making process.

An efficient claims process improves customer satisfaction and loyalty. Insurance companies use Altair RapidMiner insurance analytics solutions to automate critical claims processing and approval workflows without sacrificing accuracy.

GenAI models can read, summarize, and interpret accident and damage reports, medical records, weather data, and other unstructured data sources to reduce (or eliminate) the need for manual data entry, minimize errors, and speed the initial stages of claims processing. Predictive AI models can spot potentially fraudulent claims for further investigation while fast-tracking low-risk claims. Knowledge graph-powered chatbots can provide customers with real-time updates on claim status, freeing up personnel to handle more complex issues. Robotic Process Automation (RPA) can automate repetitive tasks within the claims processing workflow, including data entry, document verification, and payment processing.