  In Artificial Intelligence, interpreting the results of a Machine Learning
technique often termed as a black box is a difficult task. A counterfactual
explanation of a particular "black box" attempts to find the smallest change to
the input values that modifies the prediction to a particular output, other
than the original one. In this work we formulate the problem of finding a
counterfactual explanation as an optimization problem. We propose a new
"sparsity algorithm" which solves the optimization problem, while also
maximizing the sparsity of the counterfactual explanation. We apply the
sparsity algorithm to provide a simple suggestion to publicly traded companies
in order to improve their credit ratings. We validate the sparsity algorithm
with a synthetically generated dataset and we further apply it to quarterly
financial statements from companies in financial, healthcare and IT sectors of
the US market. We provide evidence that the counterfactual explanation can
capture the nature of the real statement features that changed between the
current quarter and the following quarter when ratings improved. The empirical
results show that the higher the rating of a company the greater the "effort"
required to further improve credit rating.
