Systems and methods for predictive model generation

A method of generating a predictive model comprising:

• retrieving, with one or more processors, transaction data, wherein the transaction data is updated to include at least one selected from the group of mileage data and rental data;
• training, by the one or more processors, a predictive model based on the updated transaction data;
• transmitting, by the one or more processors, one or more requests for a plurality of parameters including at least one selected from the group of location data, date data, time data, name data, and age data;
• receiving, by the one or more processors, the one or more requests including the plurality of parameters;
• transmitting, by the one or more processors, one or more responses based on the one or more requests, the one or more responses including at least one selected from the group of location data, date data, time data, name data, and age data;
• updating, by the one or more processors, the predictive model based on the one or more responses;
• producing, by the one or more processors, a dynamic rate based on the updated predictive model that is configured to apply one or more thresholds;
• executing, by the one or more processors, a display of an output of the dynamic rate;
• receiving a selection of the outputted dynamic rate in real-time;
• transmitting the selected dynamic rate;
• upon receipt of the selection of the dynamic rate, updating the selected dynamic rate to reflect one or more determinations by the predictive model, the one or more determinations including at least one event from a plurality of events occurring during or after transit associated with rental insurance coverage, the plurality of events including a predetermined number of accidents encountered and a route undertaken based on a traffic condition;
• and
• generating a rental insurance coverage policy including the updated selected dynamic rate.

A computer readable non-transitory medium comprising computer-executable instructions that are executed on a processor and comprising the steps of:

• retrieving transaction data, wherein the transaction data is updated to include at least one selected from the group of mileage data and rental data;
• training a predictive model based on the updated transaction data;
• transmitting one or more requests for a plurality of parameters including at least one selected from the group of location data, date data, time data, name data, and age data;
• receiving the one or more requests including the plurality of parameters;
• transmitting one or more responses based on the one or more requests, the one or more responses including at least one selected from the group of location data, date data, time data, name data, and age data;
• updating the predictive model based on the one or more responses;
• producing a dynamic rate based on the updated predictive model that is configured to apply one or more thresholds;
• executing a display of an output of the dynamic rate;
• and
• receiving a selection of the outputted dynamic rate in real-time;
• transmitting the selected dynamic rate;
• upon receipt of the selection of the dynamic rate, updating the selected dynamic rate to reflect one or more determinations by the predictive model, the one or more determinations including at least one event from a plurality of events occurring during or after transit associated with rental insurance coverage, the plurality of events including a predetermined number of accidents encountered and a route undertaken based on a traffic condition;
• and
• generating a rental insurance coverage policy including the updated selected dynamic rate.