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

Xiaobo Guo, Lu-an Dong, Yanbo Wang, Peng Zhang, Cai Zhi, Youru Li

arXiv:2608.26473v1q-fin.RMstat.ML

TL;DR

Traditional methods struggle to model temporal dependencies and extract task-relevant information for loan default risk prediction. DTD-VAE integrates temporal dependency learning with element-wise gating to disentangle latent features, outperforming state-of-the-art baselines across six public datasets.

  • Problem

    Traditional machine-learning methods struggle with complex data, temporal dependencies, and task-relevant feature extraction for loan default risk prediction.

  • Method

    DTD-VAE integrates an ATD module for temporal dependencies among latent variables with an EG mechanism for fine-grained latent disentanglement.

  • Results

    Across six public datasets, DTD-VAE outperforms state-of-the-art baselines on ROC-AUC and AR metrics.

  • Takeaways & Limitations

    The framework improves loan default risk prediction while enhancing latent-feature disentanglement and model interpretability and controllability.

Abstract

from arXiv · show

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.

1. Introduction

The paper frames credit-risk prediction as a temporal, high-dimensional problem and proposes DTD-VAE to disentangle temporal dependencies from task-specific customer preferences. Its ATD and EG mechanisms are evaluated across six real-world datasets.

  • 1. Introduction: Traditional methods struggle with high-dimensional nonlinear features and temporal dependencies in loan-default prediction.These limitations motivate deep latent-variable approaches for customer risk assessment.
  • 1. Introduction: Credit data are non-stationary, so risk models must adapt to changing distributions and abrupt default-rate shifts.The Lending Club illustration associates recession-driven repayment declines with increased defaults.
  • 1. Introduction: DTD-VAE combines autoregressive temporal-dependency learning with element-wise gating for finer-grained disentanglement of latent variables.ATD models dependencies and evolving trends among latent variables, while EG assigns independent weights to expert dimensions.
  • 1. Introduction: The framework is designed to disentangle temporal dependencies and task-specific customer preferences in credit-risk prediction.The paper presents this as a probabilistic generative framework for more robust and generalizable risk prediction.
  • 1. Introduction: Experiments on six real-world datasets validate DTD-VAE's consistent superiority over state-of-the-art financial-risk prediction methods.The paper also reports ablation studies, efficiency evaluations, and module migration tests.

2 RELATED WORK

Related work covers sequential probabilistic models, VAEs, and Mixture-of-Experts methods for high-dimensional representation learning. The paper positions its EG-integrated VAE as an approach for financial risk prediction.

  • 2 RELATED WORK: Financial risk prediction supports lending-portfolio management, decision-making, and financial stability.Existing approaches include credit scoring models, financial ratios, and statistical techniques.
  • 2 RELATED WORK: Sequential probabilistic models use memory units and stochastic latent variables to capture complex dynamics in high-dimensional data.Prior work also integrated VAEs with LSTMs and stochastic recurrent units.
  • 2 RELATED WORK: Mixture-of-Experts methods scale model capacity, improve parameter sharing, and support applications in healthcare, finance, and pattern recognition.Prior VAE research used products of experts and MoEs for multimodal generation and factorized variational posteriors.
  • 2 RELATED WORK: This work integrates an element-wise gating mechanism with a VAE for generative representation learning in financial risk prediction.The stated aims include improving performance, scalability, and user experience.

3. Preliminaries

The preliminaries formulate binary loan-default prediction and review the VAE's encoder-decoder architecture and objective. The model learns default probabilities while balancing reconstruction and latent-distribution regularization.

  • 3.1. Problem Statement: The dataset contains N historical loan records, each pairing an m-dimensional application feature vector with a binary default label.The prediction function maps each feature vector to a default probability and is typically evaluated using ROC-AUC.
  • 3.2. Variational Autoencoder: A VAE uses an encoder to map input data x to latent variables z and a decoder to reconstruct x from z.Latent variables are sampled from a standard normal prior, and generated data follow the decoder's conditional distribution.
  • 3.2. Variational Autoencoder: The VAE objective combines expected reconstruction negative log-likelihood with KL divergence between qϕ(z|x) and the prior p(z).This balances reconstruction accuracy against regularization of the learned latent distribution.

4. Methodology

DTD-VAE combines autoregressive temporal dependency learning with element-wise expert gating in an end-to-end variational framework for credit risk prediction.

  • Overview: DTD-VAE contains ATD, EG, and integration-and-joint-prediction components optimized through unified variational inference.ATD models temporal dependencies, EG disentangles task-relevant latent variables, and joint prediction generates risk predictions.
  • Temporal Dependencies Learning: The temporal prior models each latent variable from the preceding latent state and Gaussian noise, explicitly representing autoregressive dependencies.A linear transition uses a state transition matrix and bias; this contrasts with hierarchical conditioning that does not model temporal dependencies.
  • Temporal Dependencies Learning: A learnable adjacency matrix transforms latent states into a graph representation that encodes evolving interdependencies.The graph structure is optimized during training rather than predefined, and the transformed sequence is processed with a GRU for long-term dependencies.
  • Temporal Dependencies Learning: The generative model factorizes observed-data and latent-variable distributions conditionally across time, while GRUs preserve local patterns and temporal dependencies.The latent distribution conditions on prior observations and the previous latent state; the observation distribution conditions on current and previous latent variables and observations.
  • Task-Relevant Information Learning: EG assigns independent weights to each expert dimension, enabling finer-grained disentanglement than uniform within-expert weighting.Element-wise multiplication combines weighted expert outputs, dynamically adjusting each expert’s contribution to the final representation.
  • Integration and Joint Prediction: The objective jointly minimizes reconstruction error, KL divergence, and classification binary cross-entropy.This combines generative reconstruction, latent regularization, and credit-risk prediction objectives.

5. Experiments

The experiments evaluate DTD-VAE on six public benchmark datasets through comparisons, module analyses, challenge-focused tests, and hyperparameter studies.

  • Experimental Design: Six publicly available benchmark datasets with diverse characteristics were used to evaluate the proposed solution and its modules.The experiments also examine operational mechanisms through several analyses organized around four research questions.
  • Research Questions: RQ1 compares DTD-VAE with state-of-the-art methods for financial-risk prediction.The evaluation also asks about module contributions, performance across datasets, and hyperparameter effects.
  • Dataset Preparation: Dataset statistics are reported after preprocessing that removes meaningless or highly missing features, imputes missing values, encodes Boolean variables, and normalizes continuous features.Table 1 summarizes the resulting datasets.
  • Evaluation Metrics: ROC-AUC and Accuracy Ratio are used because credit-scoring models rank customers by default risk probabilities.ROC-AUC reflects ranking ability, while Accuracy Ratio measures discriminatory power.

5.1 Experimental Settings

The study uses standardized data splits, neural baselines, and Table 2 comparisons to assess DTD-VAE’s predictive performance.

  • Evaluation Settings: Probability-based ranking metrics are emphasized because credit-scoring outputs can represent the likelihood of customer default.The protocol uses ROC-AUC and Accuracy Ratio to assess ranking and discriminatory power.
  • Baselines: The comparison includes traditional classifiers and deep models designed for temporal or disentangled dependency learning.The listed traditional methods include Logistic Regression, Random Forest, LightGBM, and XGBoost.
  • Implementation: Each dataset is split randomly into 80% training, 10% validation, and 10% testing subsets.MLPs encode inputs, while element-wise gating experts decode them; Adam is used with batch size 128.
  • Performance Comparisons: Table 2 reports ROC-AUC(%) and AR(%) comparisons against state-of-the-art baselines, marking improvements and statistical significance against runner-up results.An asterisk denotes p < 0.05 by paired t-test.
  • Performance Comparisons: 3.2%-4.86% ROC-AUC and 6.41%-9.71% AR improvements are reported across the evaluated benchmark datasets.The paper describes DTD-VAE as significantly exceeding the other methods and showing competitive performance against the second-best method.
  • Performance Comparisons: Gating-based methods improve prediction precision by combining inference and generative models to capture latent user factors.The paper attributes further gains to incorporating ATD and EG to address temporal-dependency disentanglement and task-relevant information extraction.

5.2 Performance Comparisons (RQ1)

DTD-VAE outperforms its ablated variants and demonstrates gains across datasets, while ATD and EG contribute complementary improvements to temporal representation and generation.

  • 3.7% ROC-AUC and 7.39% AR gains were achieved on the small-scale BLS dataset.
  • DTD-VAE’s complete structure outperformed all other DTD-VAE variants in the ablation comparison.
  • 1.13% average ROC-AUC and 2.26% average AR decreases occurred when ATD was removed.The results support ATD’s contribution to capturing temporal dependencies among latent variables.
  • 1.61% average ROC-AUC and 3.22% average AR decreases occurred when EG was removed.EG contributes to the model’s generative capabilities through element-wise gating experts.
  • 7.62% average ROC-AUC and 15.24% average AR decreases occurred when both ATD and EG were removed.The combined ablation indicates complementary effects from temporal dependency learning and element-wise gating.
  • DTD-VAE-generated insights improved traditional models across all reported metrics.

5.4. Discussion of In-Depth Studies (RQ3)

The in-depth studies examine DTD-VAE’s module contributions, showing that ATD models temporal dynamics while EG provides dimension-level control over latent representations and supports competitive prediction performance.

  • DTD-VAE-enhanced traditional models consistently outperformed their non-enhanced counterparts across all metrics.The integration expands the applicability of DTD-VAE-generated insights within existing methods.
  • ATD mechanism: ATD addresses temporal dependencies and complex data structures that traditional methods often neglect in loan-default prediction.
  • ATD mechanism: ATD integration yielded superior credit risk prediction performance against Ladder VAE, VRNN, VAneu, Crocodile, GIB, GPGVAE, GaVaMoE, and MulVAEK.
  • EG mechanism: EG assigns independent weights to each latent dimension rather than uniform global weights, enabling finer-grained feature-representation control.

5.5 Hyperparameter Analysis (RQ4)

Hyperparameter analysis evaluates how latent-space size and expert configuration affect performance across datasets, with EG-based models showing competitive results and dataset-dependent settings.

  • EG-based DTD-VAE achieves competitive performance compared with existing models across the evaluated datasets.
  • Performance sensitivity was evaluated for key hyperparameters across datasets.
  • The analysis uses parameter-sensitivity experiments to examine latent-space size and expert-count effects on prediction performance.
  • Latent space size: Optimal latent dimensions vary by dataset, ranging from 4 for LC, BLS, BFL, and GMSC to 32 for PL and 16 for STC.

5.5 Hyperparameter Analysis (RQ4)

The remaining sensitivity analyses show that expert count and learning-rate settings are dataset-specific, while DTD-VAE remains stable across a broad range of hyperparameter values.

  • Number of experts: Calibrating the number of experts to each dataset’s characteristics is important for achieving peak performance.
  • Learning rate: Learning-rate analysis identifies dataset-specific settings associated with peak performance.
  • DTD-VAE consistently outperforms baselines even with suboptimal hyperparameters, indicating performance stability across a wide range of settings.

5.6. Discussion

DTD-VAE combines ATD and EG to improve temporal modeling, latent disentanglement, interpretability, and controllability. Discussion emphasizes predictive accuracy, adaptability, parameter stability, and potential use beyond credit risk.

  • Theoretical Significance: ATD learns dynamic temporal patterns, while EG assigns independent dimension-wise weights for finer-grained latent disentanglement.Together, these mechanisms improve generated-sample quality and diversity, interpretability, and controllability.
  • Practical Implications: Learning-rate sensitivity was evaluated across six datasets.
  • Practical Implications: Across six public datasets, DTD-VAE surpasses state-of-the-art baselines on ROC-AUC and Accuracy Ratio for loan default prediction.The discussion identifies this enhanced accuracy as relevant to financial institutions’ decision-making.
  • Practical Implications: The modular DTD-VAE architecture supports integration into existing models and customization across data scales, types, and financial contexts.This flexibility is presented as a source of practical value across diverse financial scenarios.
  • Practical Implications: Parameter sensitivity analysis identifies configurations for latent-space dimensions and network depth that support stable performance across hyperparameter settings.The paper connects this optimization to efficiency and stability in real-world applications.
  • Practical Implications: DTD-VAE may also apply to healthcare analytics, customer behavior prediction, and market trend forecasting where temporal dependencies and informative latent structures matter.These areas are presented as promising directions beyond credit risk assessment.

6. Conclusion and Future Work

The study presents DTD-VAE as an end-to-end credit-risk framework that captures temporal dependencies and disentangles task-relevant features. Experiments, ablations, efficiency evaluations, and migration tests support its predictive performance and component adaptability, while future work targets heterogeneous financial data, generative diversity, and interpretability.

  • DTD-VAE captures temporal dependencies and disentangles task-relevant features in an end-to-end framework for credit risk prediction.
  • Experiments, ablation studies, efficiency evaluations, and module migration tests validate the framework’s performance and component adaptability.
  • Future work will address heterogeneous financial data, generative diversity, and interpretability for real-world applications.
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