Purpose This paper includes monetary and non-monetary data to acquire a dependable image of organizations' presentations, and the yearly report is one of the principal hotspots for the dynamic course of financial backers in the monetary market. Assess how AI is enhancing or transforming trading strategies through automation and high-frequency trading. Examine how AI-driven predictive models are being used to forecast stock prices, market trends, and economic indicators more accurately than traditional methods. Explore how AI helps financial institutions manage risks, such as credit risk, market risk, and operational risk, by identifying patterns and anomalies. Design/Methodology/Approach The study employed used a Primary data, using a structured questionnaire administered through an online platform targeting a selection of forensic accounting investigators and forensic accountants. The reliability and validity of the instrument were confirmed with the use of Cronbach Alpha and descriptive statistics. The sample size is 300 from 10 various corporate financial companies Findings Enhanced Liquidity is a AI-driven trading strategies contribute to increased market liquidity by executing large volumes of trades rapidly, leading to smaller bid-ask spreads and greater market depth and Improved Market Efficiency is a AI’s ability to process vast amounts of data has made market predictions more accurate, resulting in better price discovery and reduced market inefficiencies. Originality This research paper explores the pervasive influence of Artificial Intelligence (AI) in the realm of finance, investigating its multifaceted impact on the industry. The study encompasses a comprehensive analysis of AI applications, focusing on its transformative effect on operational processes, decision-making paradigms, and the overall trajectory of the financial sector. The primary objective is to dissect the varied applications of AI within finance, ranging from risk management and fraud detection to algorithmic trading and customer service. Research Limitations/Implications Personal finance encompasses the management of your money, including budgeting, saving, investing, and planning for your financial goals. It involves making informed decisions about how to allocate your financial resources to achieve your short- and long-term objectives. Traditionally, this process relied on individual knowledge and manual calculations Practical Implications Improved Market Liquidity is use of AI in trading increases the volume of trades and can make markets more liquid, which lowers bid-ask spreads and helps price discovery. Optimized Investment Portfolios is AI systems can assess market conditions and adjust portfolios automatically, improving returns for institutional investors, hedge funds, and even retail investors using robo-advisory services. Reduced Human Error is a AI can reduce errors caused by human bias or emotional decision-making, leading to more rational and data-driven trading strategies. Social Implications The social implications of the impact of Artificial Intelligence (AI) on financial markets in India are significant and multifaceted, affecting a broad spectrum of individuals, communities, and society at large. These implications span across economic inequality, employment, financial inclusion, privacy, and education. Below are the key social implications