Loso Judijanto, Farid Wahyudi, Meiske Wenno, Hasmia Melati Arifin

Integration of Machine Learning in Financial Planning of Young Entrepreneurs in the Era of Digital Economy

Introduction

Integration of machine learning in financial planning of young entrepreneurs in the era of digital economy. Explore how machine learning transforms financial planning for young entrepreneurs in the digital economy. Discover benefits, challenges, and solutions for ML adoption in Indonesia, enhancing decision-making and efficiency.

Abstract

The integration of machine learning (ML) in financial planning offers transformative potential for young entrepreneurs navigating the dynamic digital economy in Indonesia. This study employs a qualitative approach, analyzing data from interviews with five informants, including entrepreneurs, financial advisors, and technology experts, to explore the benefits, challenges, and contextual factors influencing ML adoption. The findings highlight that ML enhances decision-making, improves efficiency, and provides personalized recommendations for financial planning. However, challenges such as limited technical expertise, high costs, cultural resistance, and the digital divide hinder widespread adoption. The study concludes that a multi-stakeholder approach involving government support, education, and infrastructure development is essential to bridge gaps and enable young entrepreneurs to leverage ML effectively. These insights contribute to understanding the interplay between technology and entrepreneurship, offering actionable recommendations for policymakers and practitioners.

Review

This paper addresses a highly pertinent and timely topic: the integration of machine learning (ML) into financial planning for young entrepreneurs within the dynamic digital economy of Indonesia. Employing a qualitative research design, the study gathered data through interviews with five diverse informants, including entrepreneurs, financial advisors, and technology experts. The core findings highlight that ML significantly enhances financial decision-making, improves efficiency, and offers personalized recommendations. However, it also clearly articulates the substantial challenges to adoption, such as limited technical expertise, high costs, cultural resistance, and the prevailing digital divide. A notable strength of this work lies in its focused qualitative exploration, which provides a rich, contextual understanding of the practical benefits and barriers to ML adoption in a specific geographical and demographic setting. The identification of both the advantages ML offers and the multifaceted challenges preventing its widespread use is crucial for a balanced perspective. Furthermore, the study's concluding emphasis on a multi-stakeholder approach—involving government support, education, and infrastructure development—offers actionable and pragmatic recommendations for policymakers and practitioners, directly contributing to the discourse on technology and entrepreneurship. While the qualitative approach offers valuable depth, a potential limitation, as suggested by the abstract, is the relatively small sample size of five informants. While sufficient for initial exploratory insights, this could impact the generalizability of the findings across the broader population of young entrepreneurs in Indonesia. Future research could consider expanding the sample size or employing a mixed-methods approach to validate and quantify these findings. Additionally, while the recommendations are clear, a deeper dive into specific policy mechanisms or successful case studies of intervention could further enrich the practical utility and provide more granular guidance for implementation.

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