#CommunityAMA Flash crashes in the forex market—sudden, sharp price drops followed by rapid recoveries—can cause severe disruption, especially when liquidity is thin or algorithmic reactions intensify volatility. Detecting the triggers behind such events in real time is a complex task due to the speed and interdependence of market reactions. AI is now being used to identify early-warning signs and causal patterns that precede flash crashes, offering a crucial layer of protection for traders and institutions. By processing vast volumes of high-frequency data, AI systems can detect anomalies in order book behavior, trade flow, and price momentum that often foreshadow a crash. Machine learning models trained on historical flash crash events learn to recognize subtle conditions—such as thinning liquidity across correlated pairs, abrupt withdrawal of large limit orders, or synchronized algorithmic activity—that may not be visible to human analysts. Natural language processing adds another layer by monitoring news feeds, central bank statements, and social media for sudden sentiment shifts or geopolitical developments that can spark panic or mispricing. When integrated, these systems provide a holistic view of both technical and psychological catalysts. Importantly, AI-based detection systems operate in real time, flagging elevated crash risk before price collapse fully unfolds. This allows trading systems or human operators to hedge, halt trading, or adjust algorithms defensively. As flash crashes grow more frequent and complex, AI offers a proactive solution—helping market participants distinguish between transient turbulence and system-level failures. In a market where milliseconds count, detecting flash crash triggers with AI could mean the difference between preserving capital and suffering outsized losses.