With the ever-increasing use of complex machine learning models in critical
applications within the finance domain, explaining the decisions of the model
has become a necessity. With applications spanning from credit scoring to
credit marketing, the impact of these models is undeniable. Among the multiple
ways in which one can explain the decisions of these complicated models, local
post hoc model agnostic explanations have gained massive adoption. These
methods allow one to explain each prediction independent of the modelling
technique that was used while training. As explanations, they either give
individual feature attributions or provide sufficient rules that represent
conditions for a prediction to be made. The current state of the art methods
use rudimentary methods to generate synthetic data around the point to be
explained. This is followed by fitting simple linear models as surrogates to
obtain a local interpretation of the prediction. In this paper, we seek to
significantly improve on both, the method used to generate the explanations and
the nature of explanations produced. We use a Generative Adversarial Network
for synthetic data generation and train a piecewise linear model in the form of
Linear Model Trees to be used as the surrogate model.In addition to individual
feature attributions, we also provide an accompanying context to our
explanations by leveraging the structure and property of our surrogate model.

本文在财务领域探讨复杂机器学习模型的解释方法。通过使用生成对抗网络生成合成数据并训练一种新型的分段线性模型，能够提供更好的局部后续模型无关解释，其中包括对单个特征的归因以及对其背景的解释。