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.
The rapid adoption of deep learning models in high-risk domains has intensified the need for trustworthy Explainable Artificial Intelligence (XAI). However, objectively evaluating explanation fidelity and aligning XAI metrics with human-centered understanding remain critical open challenges. In this work, we propose 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. Through an empirical validation across tabular, text, and image modalities, we show that the EPC score effectively uncovers operational dependencies among network activations, data dimensionality, and explainer performance. Furthermore, we validate the EPC score against independent human-based explanations, proving that higher EPC scores strongly align with human lexical sentiment judgments and spatial visual annotations.
Credit risk models increasingly need to combine predictive accuracy with transparent explanations and auditable fairness constraints. Logistic regression remains attractive because its coefficients are easy to interpret, but it can miss nonlinear structure. Flexible models can improve prediction, but their explanations are often post-hoc and may not describe the decision rule itself. We introduce $\texttt{findr}$, short for flexible, interpretable deep regression, a semi-structured framework for binary credit risk modelling that decomposes the logit into an interpretable structured component and an orthogonal neural residual. The orthogonalisation separates coefficient-based effects from residual nonlinear variation, while an in-processing Wasserstein penalty mitigates group disparities by comparing score distributions during training. The framework also includes diagnostics that measure the structured component's contribution to logit variation, decision agreement, and local directional consistency. We evaluate $\texttt{findr}$ in a simulation study and on eight public credit datasets using score-level accuracy-fairness frontiers. The results show that $\texttt{findr}$ behaves close to logistic regression when the signal is approximately linear, while recovering much of the predictive gain of neural models when nonlinear structure is relevant. The diagnostics identify when coefficient-based explanations remain close to the full fitted model and when residual variation must also be examined. These findings support semi-structured modelling as a practical way to make performance, fairness, and interpretability trade-offs explicit in credit risk decisions.
Identifiability in structural causal models remains a persistent challenge in high dimensional nonstationary environments where latent shocks are obscured by information asymmetries and regime shifts. Traditional econometric methods often rely on rigid recursive assumptions or sign restrictions that falter during periods of extreme volatility. 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. We introduce a Heterogeneous Multiagent Discussion (HAD) architecture, wherein Large Language Model agents with distinct analytical personas engage in dialectic consensus protocols to generate continuous intensity weighted narrative instruments. To mitigate hallucination and enforce economic coherence, the framework integrates a symbolic verification layer and a Reflexion mechanism that iteratively updates causal priors based on posterior market deviations. Empirical validation on global energy market data from 2020 to 2025 demonstrates that the proposed framework significantly outperforms standard set identification techniques, achieving a supply shock identification F1 score of 0.89. Specifically, the model successfully decomposed the February 2021 Texas Freeze by identifying a supply shock magnitude of 0.8 and a demand shock magnitude of 0.7, which attributed 60.0% of the immediate price spike to supply constraints. Furthermore, the Reflexion mechanism corrected initial physical supply estimates for the 2022 Ukraine invasion from 0.9 down to 0.4, while accurately isolating a geopolitical uncertainty premium of 0.95. By rigorously bridging natural language reasoning with time series econometrics, this work advances the state of the art in automated causal discovery for macro scale complex systems.
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.