Machine learning is changing the way we do current finance research by letting us look at huge, high-dimensional datasets like transaction records, social media sentiment, and images to find insights that classic econometric models could not find. It finds complicated, nonlinear patterns in asset pricing, portfolio optimization, and macroeconomic forecasting, which goes beyond what financial theory has said thus far. Reinforcement learning for adaptive trading techniques and unsupervised learning to find hidden risk variables in markets are two examples of new uses. But in order to use ML in academic finance, we need to make progress in interpretable AI and hybrid models that combine statistical rigor with data-driven insights to make sure they are strong and consistent with theory. Because of this, ML is not simply a tool, it is also a force that is changing the way finance questions are asked and answered.

ML model predict market behaviour using previous data also assess risk, fraud detection, sentiment analysis, financial forecasting and actuarial analytics.

@Ayodeji Bamidele Owoeye

Financial applications aids Organizations in vast areas of the today's economy in the nation. Almost, in every industry Financial Planning and Budgeting process plays vital role to keep the business on track to achieve financial goals of the company. For instance, If we consider Banking and Financial markets including Investment involves to deal with transactions of the Organization on daily operations including Product expenses, Product purchases, Service offerings, Customer payments, Organizational revenue, Investment transactions(like retirement income debit/credits, loan offerings, cash payments/recievables etc...). All of these financial planning processes needs to be entered into Online Transactional processing systems (OLTP) to track the individual transactions with the help of relational and webbased applications to pertain related data for the industry. However, the significance of Financial Applications research in Machine learning plays only after considering the Financial planning and budgeting process of an Organization. The real business view of any Organization can only be visited through the Profit and Loss (P&L) statements and Taxes paid to the Government in the Fiscal year to the respective governments in the region of the nation. If the Organization witholds pressure from markets it can survive the Business through positive feedback to continue further. The P&L, Balance sheet and other aspects of Planning includes corporate planning, financial planning, Managing Human resources (HR) Departments also called as workforce, capital management of an organization across regions/departments, Projects and Allocations. However, ML models are used in predicting(Extrapolation of) the future for an Organization to budget for the entire Fiscal Year(Upcoming FY26, FY27 etc...) and to plan for current Fiscal year market financial metrics including product, meassure, customer sales, revenue, employee taxes, salaries, retirement funds, stocks, ETF and other investment products, software purchases, hardware purchases, human resource capital, financial budgeting on employees for the current Fiscal year (FY25) are major implications of an Financial applications in ML Research area.

The major shift is moving towards using Generative and Artificial Intilligence models including LLMs, Llama, NLP, GAN, VAN, CNN, RNN, Bayesian and Regression techniques along with SVM, Clustering principles, Autoregressive Integrated Moving Average(ARIMA) could leverage advanced ML capabilities to train and optimize these models to offer best computation techniques with latest GPU/TPU processing units.

In addition we have marketing capabilities with ML models required to help the business to improve the sales/servies of an Organization in the given industry could potentially benifit Financials of the Organization.

Thank you Ramakoti Reddy Thamma, Vinay Kumar Singh and Shefayet Hossain Monna, for your detailed answers.