Mohammad Daffa' Almadani Sujarwo, Anak Agung Gde Satia Utama

AI-Driven Financial Planning: The Future of Investment Advice Through Advanced Analytics

Introduction

Ai-driven financial planning: the future of investment advice through advanced analytics. Discover how AI revolutionizes financial planning and investment advice through advanced analytics. Improve investment decisions, reduce uncertainty, and boost efficiency with AI algorithms.

Abstract

The development of technology today has experienced a fairly rapid development, especially in the field of Finance. There are also various technologies that experience these developments, one of which is artificial intelligence (artificial intelligence). According to the literature study, the role of AI can help investors in making investment decisions and financial planning efficiently and effectively. The purpose of writing is to analyze how AI Analysis helps investors in investment decision and financial planning. The research methods used in this writing adopt qualitative research methods. The results of the study describe the application of The Theory of Planned Behavior has several components including attitude toward the behavior, subjective norms, and perceived behavioral control. AI algorithms can help identify deeper patterns of behavior in credit and investment decision making, thereby improving the accuracy of predictions and recommendations given to investors or lenders. The conclusion is that AI analysis can help investors to make well-planned and confident investment decisions. In addition, through the existence of AI can also improve the effectiveness of financial planning and reduce uncertainty. Based on The Theory of Planned Behavior is able to provide strong social norms in analyzing data, the formation of a positive attitude in managing finances, and increased control of investors in financial results.

Review

This paper, "AI-Driven Financial Planning: The Future of Investment Advice Through Advanced Analytics," tackles a highly relevant and timely subject concerning the integration of Artificial Intelligence (AI) into financial planning and investment decision-making. The abstract posits that AI can significantly enhance the efficiency and effectiveness of financial advice for investors. It aims to analyze how AI analysis assists investors, concluding that AI can lead to more confident and well-planned investment decisions, improve financial planning effectiveness, and reduce uncertainty. The application of the Theory of Planned Behavior (TPB) is highlighted as a framework capable of demonstrating how AI can foster positive attitudes, strong social norms, and increased control for investors in managing their finances. However, the abstract presents significant methodological and conceptual ambiguities that undermine the clarity and perceived rigor of the study. While stating the use of "qualitative research methods" and a "literature study," it is unclear whether this paper presents original empirical qualitative research, a conceptual framework, or a synthesis of existing literature. The "results" section, which describes the application of TPB components and how AI algorithms "can help identify deeper patterns," reads more like a theoretical discussion or a set of propositions rather than findings derived from the stated qualitative methodology. Specifically, the relationship between "AI algorithms" and the qualitative research is not explicated; it's unclear what data was collected or analyzed qualitatively to yield these insights. Furthermore, the phrasing "Based on The Theory of Planned Behavior is able to provide strong social norms..." implies TPB itself is an outcome or an active agent, rather than a framework used to understand the impact of AI, creating confusion regarding the paper's analytical lens and its actual findings. Despite these concerns, the paper addresses an important area at the intersection of finance and technology, and the potential for AI to empower investors is undeniable. To strengthen its contribution, the full manuscript should clearly delineate the specific research methodology employed, detailing the data sources, analytical approach, and how the "qualitative research methods" led to the presented "results." Clarifying whether the study is primarily a literature review, a conceptual paper, or an empirical qualitative investigation would significantly enhance its academic standing. A more precise articulation of how AI specifically influences each component of the Theory of Planned Behavior, grounded in concrete examples or data if available, would also enrich the analysis. Addressing these points would transform the current abstract from a suggestive overview into a more robust and methodologically transparent contribution to the field.

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