Hybrid DNN–Transformer–AE Framework

• The hybrid deep learning framework is a composite system that integrates DNN, Transformer, and Autoencoder modules to jointly model static, temporal, and anomaly characteristics.
• The framework utilizes fully connected layers to fuse distinct representations into risk scores with proven high accuracy (e.g., 0.91) and robust F1-scores (e.g., 0.88) on corporate tax data.
• Its modular design enhances interpretability and adaptability for diverse applications such as tax supervision, fraud detection, and regulatory risk assessment.

A Hybrid Deep Learning Framework combining Deep Neural Networks (DNN), Transformer architectures, and Autoencoders (AE)—hereafter abbreviated as DNN–Transformer–AE (Editor's term)—is a composite neural system designed to leverage the complementary strengths of each constituent module for complex decision-making, representation learning, temporal modeling, and anomaly detection tasks. Such frameworks have demonstrated strong empirical performance and enhanced interpretability in structured prediction, risk supervision, sequence modeling, and other regulatory or high-stakes contexts by fusing static, temporal, and reconstruction-based features into joint risk scores or classification outputs.

1. Architectural Composition and Integration

The canonical DNN–Transformer–AE architecture comprises three subsystems, each operating on distinct but complementary modalities of input data:

1. Deep Neural Network (DNN):
   • Models static, cross-sectional, or non-sequential enterprise features (e.g., industry type, registered capital, firm size).
   • Inputs are typically normalized or embedded, passed through multiple nonlinear layers:

   $$h^{(i)} = \sigma\left( W^{(i)} h^{(i-1)} + b^{(i)} \right), \quad h^{(0)} = X_s$$

   where $X_s$ is the static feature vector and $\sigma(\cdot)$ is a nonlinearity such as ReLU.

2. Transformer:
   • Responsible for modeling temporal dynamics in sequential financial or behavioral time series (e.g., quarterly turnover, tax payments).
   • Employs an embedding of the input sequence with positional encodings, followed by multi-head self-attention layers:

   $$\text{Attention}(Q,K,V) = \operatorname{softmax}\left( \frac{Q K^\top}{\sqrt{d_k}} \right) V$$

   where $Q, K, V$ are linear projections of elements in the input sequence.

3. Autoencoder (AE):
   • Operates in an unsupervised mode to highlight deviations from "normal" feature configurations.
   • Takes as input either the fused static and temporal representations, or the raw feature set.
   • Encodes to a compact latent embedding $z = f_{enc}(Z)$ and reconstructs the original vector, with reconstruction error

   $L_{AE} = \| Z - Z' \|^2, \qquad Z' = f_{dec}(z)$

   High reconstruction error is indicative of anomalous or high-risk patterns.

The outputs from each subsystem—$h_s$ (DNN), $h_t$ (Transformer), $z$ (AE)—are concatenated to yield a joint feature vector, processed by subsequent fully-connected layers and normalized (via softmax) into a final risk score or categorical decision.

Module	Input Modality	Output
DNN	Static cross-sectional features	$h_s$
Transformer	Financial/tax time series	$h_t$
Autoencoder	Fused static + temporal, or all features	Latent embedding $z$

2. Functional Roles of Submodules

Static DNN Subnetwork

The DNN operates on features that do not vary with time but are instrumental in long-term risk stratification (e.g., firm registration metadata). These variables are first embedded or normalized, then modeled via standard feedforward architectures to encapsulate nonlinear relations that are typically absent in rule-based tax supervision schemes.

Temporal Transformer Architecture

The Transformer subnetwork ingests sequential data and, through self-attention mechanisms, learns inter-period dependencies (trend, seasonality, spikes, etc.) central for risk estimation in domains where behaviors and irregularities manifest over time. Positional encodings enable retention of event order, while the multi-head structure captures diverse dependencies across the sequence. This approach has been evidenced particularly effective in regulatory settings where temporal patterns are nontrivial.

Autoencoder-Based Anomaly Detection

The AE module is explicitly tasked with modeling "normal" behavior, such that large reconstruction errors serve as statistical outliers or flags for anomalous tax activities. Its placement after feature fusion allows it to act as an additional unsupervised signal within the risk assessment pipeline, rather than as a standalone detector.

3. Feature Fusion and Risk Scoring

After individual embeddings have been extracted via DNN ($h_s$), Transformer ($h_t$), and AE ($z$), feature fusion is performed:

$H = [h_s, h_t, z]$

This concatenated vector is fed through fully connected layers:

$\hat{y} = \text{softmax}(W_H H + b)$

where $\hat{y}$ is interpreted as a probability distribution over discrete risk levels ("high", "medium", "low") or as a continuous risk score.

This fusion step is critical for synthesizing complementary evidence:

• Static and time-dependent risk indicators are combined with latent anomaly signals.
• The softmax score supports probabilistic decision-making and threshold-based stratification, enabling targeted regulatory actions.

4. Empirical Results and Comparative Evaluation

On a real-world corporate tax dataset (circa 12,000 enterprises; multi-industry), the DNN–Transformer–AE framework achieved:

• Accuracy: 0.91
• Macro F1-score: 0.88

Comparisons against baseline classifiers (logistic regression, random forest, XGBoost) and strong deep learning models (DNN-LSTM) revealed superior recall and F1-score, especially in high-risk detection. This indicates the model's efficacy in accurately identifying both typical and anomalous cases, supporting fine-grained regulatory scrutiny.

5. Risk Level Discretization and Decision Interpretation

The final output is mapped into risk levels via softmax probabilities and thresholding criteria. For a given enterprise:

• If the "high risk" probability exceeds a specified threshold (or is maximal), it is stratified into that category; similar logic applies for "medium" and "low".
• This allows regulatory authorities direct, interpretable mapping between model output and actionable supervision steps, supporting prioritization and resource allocation.

The inclusion of AE-driven anomaly scores further supports interpretability by highlighting which input vectors or temporal events deviate from established patterns, making post hoc investigation tractable.

6. Interpretability and Practical Applicability

By spatially separating the learning of static structural patterns (DNN), temporal trends (Transformer), and anomaly detection (AE), the DNN–Transformer–AE system enhances interpretability in the following ways:

• Attribution: Each submodule provides a candidate explanation for escalated risk scores (e.g., anomalous sequence vs. static attribute irregularity).
• Forensic auditability: Large AE reconstruction errors become direct pointers to unusual feature combinations or time periods.

In practical deployment (corporate tax risk supervision), such modularity allows for incremental model updates as new data becomes available, extension to new static or sequential features, and integration with existing business rules.

7. Broader Implications and Methodological Innovations

The DNN–Transformer–AE architecture demonstrates that hybridization—across feedforward, attention-based, and reconstruction-driven paradigms—can capture complex, multi-factorial risk patterns characteristic of high-stakes domains. Testing on operational datasets with real regulatory implications confirms that such models are not only accurate but also suitable for settings requiring interpretability, explainability, and adaptability—attributes often missing from monolithic deep learning architectures.

A plausible implication is that similar frameworks could be generalized beyond tax supervision, to any application where static, sequential, and anomaly signals offer nonredundant risk indications (e.g., supply chain fraud, anti-money laundering, health risk stratification).

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For further methodological details and empirical findings, see "A Hybrid DNN Transformer AE Framework for Corporate Tax Risk Supervision and Risk Level Assessment" (Song et al., 28 Sep 2025).