  Credit assessments activities are essential for financial institutions and
allow the global economy to grow. Building robust, solid and accurate models
that estimate the probability of a default of a company is mandatory for credit
insurance companies, moreover when it comes to bridging the trade finance gap.
Automating the risk assessment process will allow credit risk experts to reduce
their workload and focus on the critical and complex cases, as well as to
improve the loan approval process by reducing the time to process the
application. The recent developments in Artificial Intelligence are offering
new powerful opportunities. However, most AI techniques are labelled as
blackbox models due to their lack of explainability. For both users and
regulators, in order to deploy such technologies at scale, being able to
understand the model logic is a must to grant accurate and ethical decision
making. In this study, we focus on companies credit scoring and we benchmark
different machine learning models. The aim is to build a model to predict
whether a company will experience financial problems in a given time horizon.
We address the black box problem using eXplainable Artificial Techniques in
particular, post-hoc explanations using SHapley Additive exPlanations. We bring
light by providing an expert-aligned feature relevance score highlighting the
disagreement between a credit risk expert and a model feature attribution
explanation in order to better quantify the convergence towards a better
human-aligned decision making.
