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@article{199058,
        author = {Dev Varad Chhaya and Shreya Patel},
        title = {Engineering In Financial Markets},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {11002-11010},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199058},
        abstract = {Artificial intelligence has become a transformative force in financial markets, fundamentally altering how institutional traders, banks, and derivatives market participants operate. This paper examines the integration of AI technologies—including machine learning, deep learning, and natural language processing—across institutional trading strategies, banking operations, and futures and options markets [4]. Institutional traders deploy sophisticated AI algorithms for high-frequency trading, portfolio optimization, and market-making that process millions of data points in real time [2, 11]. Banks leverage AI for credit risk assessment, fraud detection, algorithmic lending, and regulatory compliance [1]. In derivatives markets, AI systems price complex options structures, manage Greeks exposure, forecast volatility surfaces, and execute delta-hedging strategies with unprecedented precision [16]. However, these advances introduce systemic risks including model opacity, data dependencies, procyclical behavior, flash crashes, and potential market manipulation through adversarial machine learning [5]. Analysis of recent market events reveals that concentrated AI adoption by institutional players may amplify volatility during stress periods. This study synthesizes current applications across these three critical domains, evaluates associated risks with particular attention to market microstructure impacts, and examines emerging regulatory frameworks from securities commissions, banking supervisors, and derivatives exchanges. While AI enhances pricing efficiency, liquidity provision, and risk management, it also introduces systemic risks related to model opacity, data dependency, and procyclical behavior. Addressing these challenges requires robust governance frameworks, improved transparency, and coordinated regulatory oversight.},
        keywords = {Artificial intelligence; Institutional trading; Banking operations; Futures and Options; Algorithmic trading; Derivatives pricing; Financial risk management; Market microstructure},
        month = {April},
        }