Developing End-to-End Intelligent Finance Solutions Through AI and Cloud Integration

Murali Malempati

Abstract: In 2020, artificial intelligence (AI) will be one of the most popular topics across all industries, which either leads to changes of industrial trees and business strategies or accelerates their developments. Apart from consumers who care about their products and corresponding prices, financial intermediaries, who serve as a bridge between financial institutions and multi-capitalists, hedge funds or individual investors, extract fees from this information asymmetry. In the past few decades, however, machine learning techniques have gradually gotten rid of most of the hurdles that existed in analyzing high dimensional features and thereby observed billions of data transactions. As a result, great potential is presented for financial agents to beat their opponents. AI, modeling the decision-making process in a general context of automation, is one approach of intelligence powered by computational processes. It is in turn usually categorized based on knowledge types, computational paradigms, or input data types. Originally leveraging econometric theories and assumptions regarding human behaviors, rational expectations and market equilibrium were traditionally employed to describe asset price movements and forecast their trends. All kinds of methodologies from simple regression models to complex stochastic differential equations are applied on price trajectories or order states to extract timing or directional signals. In the current dynamic market, however, growingly complex data limiting traditional econometric approaches prompt the rise of prediction-based methods involving nonlinear and nontraditional answers, which have never been considered before in the financial context. Quantifying the exogeneity of data sources, recent empirical studies on supply-demand imbalance point out rich sectors? power in driving asset price movements. As such, projects regarding order outbreak clusters? trend or intention forecasting are developed on representing the rise-of-knife stylized fact and trying to conquer methodological hurdles like label noise and nonstationarity. For the vertical aspects, revealing how much a supply-demand imbalance shock affects asset vectors is a recently extending but rather challenging topic. Different from handling snapshots of the system, event-based and causal inference approaches have been proposed to quantitatively handle endogenous price movements and their corresponding feedback. Furthermore, a faster and more accurate model exploiting temporal convolutions has been designed to facilitate the advancement of multi-agent models and incorporate recent methodological improvements.

Keywords: AI in Finance, Cloud Computing, Financial Automation, End-to-End Solutions, Machine Learning Models, Data-Driven Insights, Predictive Analytics, Financial Forecasting, Real-Time Data Processing, Cloud Integration, Smart Finance Platforms, Risk Management Automation, Intelligent Financial Systems, AI-Powered Decision Making, Digital Transformation in Finance

How to Cite?: Murali Malempati, "Developing End-to-End Intelligent Finance Solutions Through AI and Cloud Integration", Volume 10 Issue 12, December 2021, International Journal of Science and Research (IJSR), Pages: 1602-1615, https://www.ijsr.net/getabstract.php?paperid=SR211216161830, DOI: https://dx.doi.org/10.21275/SR211216161830