A method and system for generating a decision-making algorithm for an entity to achieve an objective

Abstract

An analytics processing system for generating a decision-making algorithm based on a prescribed set of pre-defined data points describing one or more characteristics of an entity to achieve an objective. The objective is modelled by an underlying base algorithm. The system includes a user interface to receive initial data concerning the objective from a client. It also includes a decision engine comprising a pipeline of modules including a validation module, a retrospect module, a refinement module and a comparison module. These modules perform the following functions in iterative phases: (i) derive a base algorithm to best match a candidate-entity to a known model having regard to the initial data; (ii) input select data related to the candidate-entity from a source of data; (iii) produce an output score being a function of the base algorithm; (iv) derive a predicted probability from the output score; (iv) compare the predicted probability with an actual outcome based on actual data derived from the source data at a subsequent period of time relative to the select data; (v) generate a variant of the base algorithm based upon the results of the comparison; (vi) create a new decision-making algorithm based on the variant; and (vii) periodically perform the aforementioned steps using the new decision-making algorithm as the derivative of the base algorithm after the prescribed period of time. The select data is prescribed to characterise a plurality of pre-defined data points associated with the base algorithm selected to provide a qualitative measure of performance to achieve the objective; The output score is derived from applying the select data for each data point and running the base algorithm thereon. The predicted probability is a weighted variable of the data points that is used to predict the likelihood of the objective being achieved.

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