Strengthening financial system stability through Artificial Intelligence Enabled Risk Surveillance, Crisis Detection and Strategic Resilience
TL;DR
The paper 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.
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.
