  Corporate mergers and acquisitions (M&A) account for billions of dollars of
investment globally every year, and offer an interesting and challenging domain
for artificial intelligence. However, in these highly sensitive domains, it is
crucial to not only have a highly robust and accurate model, but be able to
generate useful explanations to garner a user's trust in the automated system.
Regrettably, the recent research regarding eXplainable AI (XAI) in financial
text classification has received little to no attention, and many current
methods for generating textual-based explanations result in highly implausible
explanations, which damage a user's trust in the system. To address these
issues, this paper proposes a novel methodology for producing plausible
counterfactual explanations, whilst exploring the regularization benefits of
adversarial training on language models in the domain of FinTech. Exhaustive
quantitative experiments demonstrate that not only does this approach improve
the model accuracy when compared to the current state-of-the-art and human
performance, but it also generates counterfactual explanations which are
significantly more plausible based on human trials.
