IoT ‐Integrated Risk Management Systems Using Edge AI and Deep Reinforcement Learning

The accelerating pace of the financial markets and the spread of real‐time economic indicators require smart portfolio optimization systems that can make risk‐sensitive decisions in a very short time. The new capabilities of the decentralized, low‐latency financial intelligence are presented by emerging Edge AI and IoT technologies. The current methods of portfolio optimization are still mostly centralized and reactive, which leads to the slowness of risk evaluation, ineffective rebalancing of assets, and low responsiveness to unexpected market shocks as recorded by distributed financial data sources. In this paper, EDGE‐FORCE (Edge‐driven Financial Optimization with Reinforcement‐based Cognitive Evaluation) is presented. This new architecture is based on IoT‐enabled market sensors, edge‐based risk cognition, and a hierarchical deep reinforcement learning agent (HDRL). Edge nodes are used to do real‐time risk scoring and filtering of anomalies. Simultaneously, a DRL policy network uses a risk‐sensitive assignment of assets with a risk‐sensitive reward function with volatility, liquidity, and tail‐risk penalties. The experimental results obtained using multi‐asset market data indicate that EDGE‐FORCE outperforms centralized DRL and classical optimization models by providing higher cumulative returns, a better Sharpe ratio, and quicker conversion in volatile settings. EDGE‐FORCE shows the presence of an efficient, scalable, and resilient solution to real‐time financial portfolio optimization with intelligent risk management.