Strengthening financial system stability through Artificial Intelligence Enabled Risk Surveillance, Crisis Detection and Strategic Resilience
Elizabeth OpeYejide R. AlliIfeoluwa A. OjoOyindamola AdejumobiAniedi OjoVictoria Enoc-Ahiamadu
Aug 2026· International Journal of Science and Research Archive· 0 citations
Abstract
The world financial environment is characterised by increasing interdependence, rapid technological change and cyclic economic crises such that current and legacy EWS is no longer useful in regard to macroeconomic governance. Conventional models, based on lagging indicators and historical data, do not take into account contagion non-linearities, velocity and systemic shocks in the digital world, such as algorithmic “flash” crashes or “bank” runs. The paper suggests the use of AI to overcome these issues, through real-time monitoring and early warning of risks. The suggested Multi-layered structure to combine Big Data, Machine Learning and Artificial Intelligence in the macroeconomic direction is based on the Financial Instability Hypothesis (FIH) of Minsky and the theory of Complex Adaptive Systems (CAS). The architecture comprises data ingestion, core analytics (unsupervised anomaly detection, network contagion mapping and supervised crisis forecasting) and decisions support operational dashboards. Moreover, it discusses a dynamic stress test using Generative Adversarial Networks (GANs), answers key questions in the context of Explainable AI (XAI) and data privacy, and offers a recommended course of action in implementing and standardised RegTech/SupTech systems to transition regulatory control to an active and proactive science.
It is argued that both AI and bullshitters are untrustworthy informants, and for similar reasons, it is natural to describe AI’s informational outputs as bullshit, as it signals their distinctive kind of epistemic deficiencies, which they share with bullshit.
In light of the pervasive methodological limitations identified, including high analytic risk of bias, absence of external validation, and lack of model interpretability, claims of ML superiority over CHA2DS2-VASc must be interpreted with caution.
Md. Mohaimenul Islam, Arinzechukwu Nkemdirim Okere· Int. J. Medical Informatics· 0 citations
The widespread use of pesticides in modern agriculture has substantially improved food production while raising serious concerns regarding contamination and food safety. Considerable scientific effort has focused on improving methods for monitoring and detecting pesticide residues in food to enhance safety assurance. Although chromatography coupled with mass spectrometry remains the gold standard, its routine application is constrained by high costs, labor-intensive sample preparation, and prolonged analysis times. Recent advances in spectroscopic techniques, including surface-enhanced Raman spectroscopy (SERS), Raman spectroscopy, hyperspectral imaging (HSI), and near-infrared (NIR) spectroscopy, offer promising non-destructive, rapid, and sensitive alternatives for pesticide residue detection across diverse food matrices. When integrated with machine learning (ML), these approaches further improve predictive accuracy and analytical robustness. This review synthesizes recent advances in ML-assisted spectroscopic approaches for pesticide residue detection across diverse food matrices, with emphasis on analytical performance, preprocessing strategies, feature engineering, and model selection. Convolutional neural networks (CNNs), support vector machines (SVMs), random forests (RFs), and ensemble learning methods are increasingly used to improve classification and quantitative prediction. Across the reviewed studies, analytical performance was generally strong, with high classification accuracies, while the lowest reported detection limit was achieved using a SERS-CNN platform. Despite these advances, key limitations remain, including reliance on laboratory-spiked samples, small dataset sizes, matrix interference, inconsistent validation strategies, high computational demands associated with high-dimensional spectral data, and limited field validation. Future directions should focus on hybrid AI-driven sensors, IoT integration, advanced data augmentation, QuEChERS-assisted preprocessing, and explainable AI to improve real-world applicability and interpretability.
B. C. Ezenwanne, C. Okoye, Stanley Ebhohimhen Abhadiomhen et al.· Food Research International· 0 citations
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
