Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries

Abstract The rapid adoption of artificial intelligence (AI) in the automobile banking industry has transformed business processes, yet how AI techniques influence financial performance is not yet fully understood. The difficulty here lies in scattered AI disclosures and disparate metrics that hinder effective forecasts of financial results in car-related banks. In order to bridge this gap, the research introduces a model called "Residual Self-Guided Quantum Vision Adversarial Attention Network for Predicting AI-Driven Financial Performance in Automobile Industries." The analysis relies on a dataset called "AI Techniques on Financial Performance of Automobile Industries," which contains 131 annual report records for sixteen Indian-listed banks from 2015 to 2023. This dataset captures keyword-based AI disclosures, as well as institutional and financial indicators. Data pre-processing is done through the use of the Correlation Coefficient with Min–Max Weighted (CCMMW) method to normalize and fine-tune the input parameters, while the Masked Attention Mask Transformer (Mask2Former) is utilized for feature extraction for isolating the disclosure patterns that have the greatest influence. Following these steps, a Self-Guided Quantum Generative Adversarial Network (SG-QGAN) is constructed and then advanced into the new Residual Self-Guided Quantum Single-Head Generative Elk Herd Vision Adversarial Attention Network (RSG-QSAGE-HV2AN). This network combines SG-QGAN with a Residual Attention Single-Head Vision Transformer Network (RA-SHViT-Net) and fine-tunes its parameters with the Elk Herd Optimizer (EHO). Experimental outcomes show that RSG-QSAGE-HV2AN is 99.9% accurate in calculating how AI methods affect financial performance and also improves the financial analytics' interpretability and greatly lessens computational overhead over conventional deep learning approaches.