A Comparative Analysis of Business Machine Learning in Making Effective Financial Decisions Using Structural Equation Model (SEM)

Abstract

Globally, organisations are focused on deriving more value from the data which has been collected from various sources. The purpose of this research is to examine the key components of machine learning in making efficient financial decisions. The business leaders are now faced with huge volume of data, which needs to be stored, analysed, and retrieved so as to make effective decisions for achieving competitive advantage. Machine learning is considered to be the subset of artificial intelligence which is mainly focused on optimizing the business process with lesser or no human interventions. The ML techniques enable analysing the pattern and recognizing from large data set and provide the necessary information to the management for effective decision making in different areas covering finance, marketing, supply chain, human resources, etc. Machine learning enables extracting the quality patterns and forecasting the data from the data base and fosters growth; the machine learning enables transition from the physical data to electronically stored data, enables enhancing the memory, and supports with financial decision making and other aspects. This study is focused on addressing the application of machine learning in making the effective financial decision making among the companies; the application of ML has emerged as a critical technology which is being applied in the current competitive market, and it has offered more opportunities to the business leaders in leveraging the large volume of data. The study is intended to collect the data from employees, managers, and business leaders in various industries to understand the influence of machine learning in financial decision making .

Continued Intention to Use of M-Banking in Jordan by Integrating UTAUT, TPB, TAM and Service Quality with ML

“…The analysis of data for this study included: firstly, a descriptive analysis to measure respondent's attitudes; secondly, a structural equation model (SEM) (which included a confirmatory factor analysis (CFA) and then structural equation modeling (SEM) using Amos 20, performed to test the study hypotheses); thirdly, the moderating effects; and finally, validation of this research using machine learning (ML). SEM and CFA verified the hypotheses and analyzed the results whilst ML validated and predicted mean square error and correlation coefficient (R 2 ), similar to the work of [90][91][92][93][94][95][96], since other researchers suggested the use of triangulation of mixed methods [97], which is an effective tool to understand and explore in depth the findings of the study at hand. This research employed triangulation by using multiple data collection and analysis.…”

“…Also, this study is the only one that used SEM, CFA, and machine learning (ML) methods to confirm the results to predict CIU. The use of such methods is dupped from the idea of triangulation of mixed methods [97], similar to the work of [90][91][92][93][94][95][96]. Furthermore, the ML validated the findings that WoM can predict CIU, and BI can predict WoM as shown previously.…”

Importance of Machine Learning and Data Science in Modern Business

“…Avln Sujith and his team investigated how machine learning could impact effective financial decision-making within businesses. Their study highlighted how ML techniques help in analyzing vast amounts of data to extract patterns and forecast outcomes, thereby facilitating better decision-making across various business functions including finance and marketing [10]. Leonidas G. Barbopoulos, Rui Dai, Tālis J.…”

The Artificial Intelligence Revolution in Digital Finance in Saudi Arabia: A Comprehensive Review and Proposed Framework

“…The ML algorithms in the studies conducted in [39][40][41][42][43][44] are among the best AI solutions for the financial industry. Prominent machine learning methods are connected to financial risk and a taxonomy of financial risk management responsibilities, creating a framework for effective risk management strategies.…”