There are 1.1 million insurance agents, brokers, and service personnel in the United States. For every carrier, this translates to highly diluted distribution channels that are composed of relationships with thousands of individual agents. As a result, insurance carriers are faced with the challenges that come with trying to optimize revenue across their distribution channels. This includes having too many agents to manage manually, limited sales staff that reduces direct management, and performance that varies widely across the entire channel. While maximizing the performance of a few sellers is manually feasible, it becomes difficult, if not impossible, to maximize the performance of a few thousand sellers without harnessing data.
AI helps you maximize the performance of your large volume of agents and brokers by predicting the profitability of each agent throughout the next year. Unlike looking at data retrospectively, AI enables mutual insurance carriers to leverage vast amounts of data to develop a forward looking view on performance, also offering them with the top reasons why an agent is predicted to perform high or low. Carriers can use this information to make decisions on the field that positively impact revenue, such as conducting training and mitigation for agents predicted to perform poorly, as well as investing more focus on the agents who are predicted to have a positive growth in sales. Overall, these predictions allow you to manage your distribution channels by having an unparalleled view on performance.
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Insurance companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from underwriting to marketing in order to make data-driven decisions to lead to increased profitability.