Due to the ever-increasing economic uncertainty, managing financial risks is becoming increasingly important in the financial services sector and across industries. The issues of knowledge imbalances, mode latency, credit risks, insecurity, and high operational vulnerability between banks and organizations in the practice of finance business have been a major concern of risks in bank management and regulators. Data analytics encompasses a wide variety of tools, technologies, and methods used to identify patterns in data and provide solutions to issues. Financial sectors use data analytics to focus on the correct objects, to improve the company operations and the downsides of their financial risks. The issues emphasize the need to employ big data analytics to construct more accurate risk prediction models for foreseeing default behaviors at scale. The study recommends commercial banks using IoT implement a big data analytics system based on the Rapid Ant Colony Optimization algorithm with Hybrid Blockchain Technology (RACO-HBT) for Financial Risk Management (FRM) which constructs a linear concurrent optimization approach for on-balance and off-balance items is built using the Apache Hadoop framework for FRM in commercial banks with IoT deployment. The hybrid blockchain technology in the proposed paradigm combines the beneficial features of public and private blockchains. Results imply that banks must preserve a healthy equilibrium between financial risk mitigation methods and economic health by implementing tough financial, credit, and stability risk management strategies to generate profits. As a result, the approach used in this study is contrasted with more conventional methodologies and used to forecast the outcome of the financial crisis.