1. A computer-implemented method comprising: receiving data about vehicle operator behavior via a computer network, wherein the data about vehicle operator behavior is generated by a motion sensing device adapted to monitor movements of a vehicle operator, and wherein the data about vehicle operator behavior is stored on a computer-readable medium; categorizing the data about vehicle operator behavior by one or more processors executing a first specialized computer routine configured to categorize the data about vehicle operator behavior, wherein categorizing the data about vehicle operator behavior includes clustering the data about vehicle operator behavior, generated by the motion sensing device, into a plurality of groups of data, each of the plurality of groups of data representing a type of movement of the vehicle operator; determining, by the one or more processors, a numerical level of risk corresponding to each of the plurality of groups of data by executing a learning routine, wherein the learning routine is a second specialized computer routine specifically trained to determine the numerical level of risk based on learned correlations between detected movements of vehicle operators and levels of risk, and wherein the learning routine is trained by executing the learning routine with a set of training data about past vehicle operator behavior; and generating a risk index corresponding to the vehicle operator by the one or more processors executing a third specialized computer routine configured to generate the risk index corresponding to the vehicle operator, the risk index being a collective measure of risk based on the numerical levels of risk corresponding to the plurality of groups of data.