The ethical governance of Artificial Intelligence (AI) is becoming the new institutional sustainability in the modern digital banking environment.
On the Kaggle Credit Card Fraud Dataset, the research group structures a collaborative system in which Machine Learning models generate sub-second anomaly detection, and an Ethical Human Override (EHO) system adjudicates ambiguous, high-value transactions. The approach includes an empirical study of Principal Component Analysis (PCA)-transposed features to single out grey-zone clusters in which algorithmic rigidity tends to cause false positives and unjust rejection. The framework adopts a more socially sustainable framework by moving towards a defensive security posture through the application of a Human-in-the-Loop protocol to prevent the risk of algorithmic redlining.
The novelty of the writing is in the fact that the Cost of False Positives became a part of the more comprehensive sustainability discussion, and human control was seen as an essential measure of financial equity.
This research proposes a bimodal fraud detection framework that resolves the inherent tension between algorithmic efficiency and socio-economic inclusion.
In practice, the research offers a transformational map of applying human contextual judgment to FinTech ecosystems, balancing predictive accuracy of AI with moral responsibility of human intelligence to create a resilient, inclusive and sustainable financial future.
