Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification

Explainability is critical in high-stakes financial decisions: The paper demonstrates that in sensitive domains like mergers and acquisitions—which involve billions of dollars—AI systems must not only be accurate but also provide trustworthy explanations to users, as unexplainable decisions can undermine confidence in automated systems.

Current explanation methods often produce unrealistic outputs: The research identifies a significant problem with existing explainable AI approaches for financial text classification: they generate explanations that are implausible and potentially misleading, which actually damages rather than builds user trust in the system.

Counterfactual explanations can be made more realistic: The paper proposes a novel methodology that generates counterfactual explanations (showing what would need to change for a different outcome) that are significantly more plausible according to human evaluation, suggesting this approach is more useful for real-world deployment.

Better explanations don't require sacrificing accuracy: The proposed method achieves improved model accuracy compared to current state-of-the-art approaches while simultaneously generating more trustworthy explanations, demonstrating that explainability and performance can be complementary rather than competing goals.

Adversarial training strengthens financial AI models: The research shows that adversarial training techniques provide regularization benefits that enhance language models' robustness in the FinTech domain, offering a practical pathway for developing more reliable AI systems in finance.