The increase in the transparency of pricing because of the ease of quoting with the help of digital channels, comparative rating vendors and aggregators presently require companies to have the ability for personalizing and pricing auto policies accurately along with being able to adjust prices with a higher frequency than before. For attracting and retaining the best auto insurance customers in and highly competitive market insurance companies need to set policy packages which include premiums and coverage which can meet the needs of both the customer and the business. Insurance analytics software solutions can assist insurers to assess the underlying loss cost and market dynamics for personalizing and pricing policies for customers belonging to different risk classes while retaining their internal business objectives. Insurance companies are therefore increasingly using the auto finance portfolio optimization software which is allowing them to determine the ideal policy packages they can offer to customers without compromising the objectives of their business as well as the customer.
The solution being offered by Earnix has a number of highlights which will provide the insights insurance companies need to understand model risk, conversion and retention. The auto finance portfolio optimization solution helps insurance companies to understand risk variables like age and driving behaviors which are combined with market factors such as the competition. Earnix can enable insurance companies to have a holistic view of their portfolio by combining predictive models across different dimensions to inspire confidence that their selected rating strategy and the coverage selection that is offered will achieve their business goals.
Filtering through large data volumes is a concerning factor for most insurance companies. Earnix can provide an analytical framework which will allow insurers to filter through data volumes that are large. They can also filter through telematics data for providing a robust approach to set pricing and product strategies. Insurers can understand the volume to earnings trade-offs needed to expand their market share by leveraging this risk, conversion and retention models, among many others along with what-if scenarios. This will enable the insurer to understand the lifetime value of the customer.
Insurance companies can also optimize their market position with machine learning. They will be able to understand the competitive environment which will be key to enhancing their book of business. Machine learning makes it possible for them to create accurate models and forecasts comprehensively to predict competitive position to ensure they have full information of the segments and customers they should pursue.
The auto finance portfolio optimization solution from Earnix will provide a multi-user enterprise solution that can empower collaboration with all the teams across the organization. The solution enables managers at every level to oversee the work of the members for establishing a consistent and transparent pricing process.
Earnix is an enterprise-grade software which can be deployed in the cloud or on-premise to provide flexible options for meeting immediate and future requirements. Using the cloud option provided by Earnix insurance companies can instantly make the solution available across their organization to establish a unified analytical process.
Insurance customers receive support at every stage of the implementation and deployment process from Earnix professional services. The industry experts at Earnix that have been acknowledged across the world are available to provide users to extract the highest value from the platform. Their experience in financial services ensures that they have understood the data and analytical process that can translate into growth.
Earnix professional services are supporting auto insurance customers during all stages of the implementation of insurance analytics software solutions. The model is designed to provide maximum or minimum assistance as needed by the insurance customer. At the end of the implementation process insurance companies will have the analytical tools and the know-how needed to make data-driven decisions that are optimized.
