DTD-VAE: Disentangled Temporal Dependencies VAE for Credit Risk Prediction

Evaluating customer creditworthiness is crucial for retail banking operations, as it impacts marketing strategies, customer relationship management, and credit risk control. Traditional methods often struggle to capture complex temporal dependencies and extract pertinent information from customer data, crucial for accurate risk assessment. Specifically, they fail to differentiate between temporal patterns indicative of credit risk and those reflecting general customer behavior or preferences, leading to suboptimal risk predictions. In this study, we introduce the Disentangled Temporal Dependencies Variational Autoencoder (DTD-VAE), an advancement over conventional VAE, designed to disentangle temporal dependencies and distinguish credit risk-related features from past customer preferences. The feature inference module of the DTD-VAE incorporates an autoregressive temporal dependency learning mechanism that adeptly captures the temporal dependencies among latent variables, enriching the model's comprehension of the inherent data structure. Furthermore, the feature generative module utilizes an element-wise gating mechanism that assigns independent weights to each dimension of the expert models, enabling a finer-grained disentanglement of latent variables, particularly those relevant to credit risk prediction. Extensive experiments on six real-world datasets demonstrate that the proposed framework consistently outperforms existing methods, achieving performance gains of 3.2%-4.86% in ROC-AUC and 6.41%-9.71% in Accuracy Ratio.

$\texttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions

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.

Statistical Properties and Power Analysis of Divergence Measures for Credit Risk Model Monitoring

Divergence measures are essential tools for detecting distributional shifts in model monitoring, particularly crucial given the volatility of financial data. While the Population Stability Index is the most widely used measure, Jensen-Shannon Divergence and Kullback-Leibler Divergence offer distinct advantages. Jensen-Shannon Divergence handles mixture models, addresses zero-binning problems, and is symmetric, while Kullback-Leibler Divergence excels in Bayesian model comparison. This study extends the work of Yurdakul and Naranjo (2020) with two primary contributions. First, we derive the statistical properties and chi-square benchmark values for Jensen-Shannon Divergence and Kullback-Leibler Divergence. Second, we demonstrate their applicability by detecting distributional changes in credit default probabilities from Merton, Merton with jump, and stochastic volatility with jump models. Our results establish that Jensen-Shannon Divergence and Kullback-Leibler Divergence follow chi-square distributions and reveal important practical trade-offs. Jensen-Shannon Divergence exhibits superior Type I error control, maintaining rejection rates closest to 5%, thereby minimizing false positives. However, this conservatism reduces statistical power at small samples (27% versus 32% for Population Stability Index and Kullback-Leibler Divergence at n = m = 200), requiring larger samples for reliable detection. This trade-off enables practitioners to select measures based on whether minimizing false alarms or maximizing detection sensitivity is the priority.