Predictive analytics for claims management

Utilizing data and analytics to predict the likelihood of claims and enhance claims management efficiency.

Implemented functionality:

• Gathering and analyzing claims data from various sources, including CRM systems, claims management subsystems, external sources, and social media.
• Utilizing machine learning to develop predictive analytics models that forecast the probability of new claims and help identify potential causes.
• Integration with policy management and claims management systems to automate monitoring and claims prevention processes. Using data collection and analysis tools such as SQL, Python, R, and Tableau.
• Implementing mechanisms for automatic optimization of claims management processes based on predicted data models.
• Implementing monitoring and auditing mechanisms for the predictive analytics system to ensure its effectiveness. Integration with other company systems, such as customer service and anti-fraud systems, to ensure consistency and process control.
• Employing data visualization tools, including diagrams, graphs, and maps, to present data and models in an understandable format.
• Implementing mechanisms for automated notification and alert systems for company operators and managers regarding potential claims.
• Implementing mechanisms for automated distribution of claims to responsible departments and individual managers.