Cognitive AI-Governance-Empowered FinTech Ecosystem for autonomous ethical risk intelligence and sustainable decision optimization in smart financial systems

The rapid expansion of AI-driven FinTech has improved forecasting and operational efficiency, yet existing studies remain fragmented by focusing either on financial performance or ESG compliance separately, suffer from limited datasets, lack governance transparency, and fail to jointly optimize profit, ethics, and sustainability. However, smart financial systems still face several critical challenges, including the detection of highly imbalanced financial risk patterns, rapidly evolving cyber-fraud behaviors, and the complexity of processing large-scale heterogeneous financial and ESG data. For example, traditional financial prediction and risk assessment models often struggle with extreme class imbalance, where high-risk or fraudulent transactions represent only a small fraction of overall financial activities, leading to reduced predictive reliability and delayed risk mitigation . To overcome these limitations, this study proposes an ambitious Cognitive AI-Governance-Empowered FinTech Ecosystem designed to generate autonomous, fair, and sustainability-oriented financial decisions. The core objective is to integrate Temporal Fusion Transformer (TFT) for deep temporal reasoning, a Governance & Ethical Risk Intelligence Layer for bias detection, fairness correction, and SHAP-based explainability, and a multi-objective NSGA-II optimization engine to derive Pareto-optimal strategies balancing profitability, ESG compliance, and long-term sustainability. The framework is implemented using Python 3.11, PyTorch 2.1, and GPU-accelerated experimentation on the ESG & Financial Performance Dataset (Kaggle). Experimental evaluation demonstrates that the proposed framework significantly improves predictive and governance performance across multiple evaluation metrics, enabling more reliable financial forecasting and ethical decision support in complex FinTech environments . Results demonstrate significant predictive improvements, achieving R 2 = 0.97 and RMSE = 2.84, outperforming GRU (R 2 = 0.9642) and GA-LSTM (R 2 = 0.87). ESG compliance accuracy reaches 94.5%, fairness deviation reduces to 0.031, and NSGA-II optimization yields balanced strategies improving profit (+8–12%), ESG alignment (+10%), and sustainability stability.