Convergent construction of traditional scorecards

摘要

A neural model for simulating a scorecard comprises a neural network for transforming one or more inputs into an output. Each input of the neural model has a squashing function applied thereto for simulating a bin of the simulated scorecard. The squashing function includes a control variable for controlling the steepness of the response to the squashing function's input so that during training of the neural model the steepness can be controlled. The output of the neural model represents the score of the simulated scorecard. The neural network is trained to behave like a scorecard by providing plurality of example values to the inputs of the neural network. Each output score produced is compared to an expected score to produce an error value. Each error value is back-propagated to adjust the neural network transformation to reduce the error value. The steepness of each squashing function is controlled using the respective control variable to affect the response of each squashing function.