Underwriting based on machine learning

Utilizes machine learning algorithms to assess risk and determine insurance premium rates.

Implemented functionality:

• Integration with data sources such as CRM, analytics systems, social networks, and others to collect customer data.
• Use of machine learning methods, including clustering, classification, and recommendation systems for data analysis and customer profiling.
• Development of models to forecast customer behavior, such as the likelihood of churn or making a purchase.
• Integration with marketing and sales systems for targeted advertising and offers.
• Integration with analytics systems to track effectiveness and optimize models.
• Use of data collection and analysis tools such as SQL, Python, R, and Tableau.
• Integration with A/B testing systems to optimize models and improve underwriting results.
• Implementation of mechanisms for automatic model optimization based on customer behavior data and underwriting results.
• Generation of reports and analytics to assess the effectiveness of the underwriting system and identify trends and patterns.
• Ability to update and modify the system based on changes in data and machine learning technologies.