Hybrid intelligent modeling for calibrated business and financial risk classification: a decision-support perspective

Purpose This study aims to develop a calibration-aware hybrid framework for large-scale business and financial risk classification, addressing heterogeneous features, nonlinear relationships, temporal distribution shifts, class imbalance and unreliable probability estimates. Design/methodology/approach The hybrid multi-agent quantum neural transformer (H–MAQNT) combines a tabular transformer, domain-specialized variational quantum neural network (VQNN) agents and adaptive attention-based fusion. It uses leakage-controlled preprocessing, domain-informed features, imbalance-aware training, validation-only model selection and calibration and quantum-aware multi-fidelity Bayesian optimization (QMFBO) for cost-aware tuning. Findings On temporally held-out LendingClub and IEEE–CIS future test periods, H–MAQNT outperforms strong gradient-boosted, neural and transformer-based baselines in discrimination and probability quality. It improves the receiver operating characteristic area under the curve (ROC–AUC) by approximately 1.1–1.3 percentage points over the strongest Transformer-based baselines, lowers expected calibration error (ECE) on both data sets and reduces high-fidelity optimization evaluations by 35% relative to single-fidelity Bayesian optimization. Research limitations/implications The evidence is based on two public data sets and classically simulated quantum circuits. Future work should test additional institutions, rolling time windows, physical hardware and prospective drift and recalibration policies. Practical implications Calibrated risk probabilities may support fraud-alert prioritization, risk ranking, threshold setting, and secondary review, helping institutions weigh predictive gains against computational cost. For LendingClub, the inclusion of loan grade and interest rate makes the framework more appropriate for post-pricing monitoring than for independent pre-pricing approval. Operational use would require institution-specific validation, monitoring for drift and subgroup performance, documented decision thresholds, and human oversight. Social implications The study integrates tabular attention, domain-specialized simulated quantum representations, calibration analysis and cost-aware optimization in one decision-support framework, while evaluating incremental value without claiming inherent quantum advantage. Originality/value The contribution is the theoretically motivated integration of tabular attention, domain-specialized simulated quantum representations, calibration analysis and cost-aware optimization in a unified financial decision-support framework. The study does not claim inherent quantum advantage; instead, it tests the incremental value of simulated quantum modules through temporal evaluation, strong nonhybrid comparisons and component-level ablations.