TL;DR
A novel Ante-hoc Explainable AI framework designed to bridge the interpretability-accuracy trade-off in high-stakes financial prognosis, specifically within credit scoring systems, which provides a verifiable and robust solution for modern, regulatory-compliant financial environments.
Invertible Logits Transformation (InvLT), which applies a learned scalar MLP element-wise to the pre-softmax logits, consistently outperforms a broad set of post-hoc baselines on standard calibration metrics.
CreditR1 delivers calibrated PDs with evidence-grounded reasoning that supports internal model validation and human review that supports transferability beyond the Chinese A-share market remains an open empirical question.
A model-agnostic metric, the EPC score, which is an extension of the Explainability-Performance Coefficient (EPC), that quantifies explanation quality by explicitly balancing the trade-off between feature selection sparsity and preserved model performance is proposed.
Findr, short for flexible, interpretable deep regression, is introduced, a semi-structured framework for binary credit risk modelling that decomposes the logit into an interpretable structured component and an orthogonal neural residual.
This paper proposes Adaptive Event Driven Labeling (AEDL), a novel neurosymbolic framework that synthesizes unstructured semantic data with formal causal inference to disentangle simultaneous supply and demand shocks and introduces a Heterogeneous Multiagent Discussion (HAD) architecture.
This study proposes TRuE-XAI (Transparent, Rule-based, and Explainable Artificial Intelligence), an integrated framework combining imbalance-aware ensemble learning, automated hyperparameter optimization, rule-based explainability, visual analytics, and causal inference for transparent earnings-growth forecasting.
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