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The Es Accounting and Finance
Published by Eastasouth Institute
ISSN : 29857139     EISSN : 29642752     DOI : https://doi.org/10.58812/esaf
Core Subject : Economy,
Economics, Econometrics & Finance
ESAF - The Es Accounting and Finance is a peer-reviewed journal and open access three times a year (March, July and November) published by Eastasouth Institute. ESAF aims to publish articles in the field of Financial Accounting, Managerial Accounting, Public Sector Accounting, Auditing and Forensic Accounting, Accounting Education, Tax Accounting, Capital Markets and Investments, Accounting Information Systems, and Environmental Accounting. ESAF accepts manuscripts of both quantitative and qualitative research based on its originality, relevance, and contribution to the development of accounting practice and profession in Indonesia. ESAF publishes papers: 1) review papers, 2) basic research papers, and 3) case study papers. ESAF has been indexed in, Crossref, and others indexing. All submissions should be formatted in accordance with ESAF template and through Open Journal System (OJS) only.
Arjuna Subject : Bisnis, Menejemen, dan Akutansi (semua kategori) - Bisnis, Menejemen, dan Akutansi (Lain-Lain)
Articles 72 Documents
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Machine Learning in Financial Risk Management: Techniques for Predicting Early Payment and Default Risks Nayak, Saugat
The Es Accounting And Finance Vol. 3 No. 03 (2025): The Es Accounting And Finance (ESAF)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Artificial intelligence and, commonly, its subfield of machine learning (ML) has dramatically impacted financial risk management by improving the elicitation and flexibility of risk forecasts, especially concerning early payment and default risk. That is why it has become possible to speak about the existing traditional risk assessment models that no longer apply in a modern financial context, as they are oriented on historical data and are to be implemented with the help of relatively rigid frameworks. On the other hand, ML provides real-time prediction services, which leverage big datasets and learning algorithms like the logistic regression models, the random forest, and neural nets to develop proper risk profiling. The significant uses of the JHL method are for early payment prediction, default risk identification and credit scoring, which is flexible. There are benefits accrued to its use, such as increased predictive accuracy and real-time risk assessment, where it adopts a cheaper model to arrive at the results. However, its disadvantages include data privacy, security, and interpretability drawbacks. The future of ML in financial risk management trends will include the eventual use of technologies such as blockchain and AI to enhance decentralized, efficient, and secure risk management systems. As ML progresses, it is predicted that this technology will increase the efficiency, effectiveness, and individuality of risk management processes in the financial industry.
Analysis of Raw Material Rice Inventory Control Using the Economic Order Quantity and Reorder Point Methods Octavianty, Ellyn; Ilmiyono, Agung Fajar; Adinugraha, Muhammad Rafli
The Es Accounting And Finance Vol. 3 No. 03 (2025): The Es Accounting And Finance (ESAF)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esaf.v3i03.723

Abstract

This research aims to analyze the Economic Order Quantity (EOQ) and Reorder Point (ROP) methods for controlling rice raw material inventory at Kedai Bubur Ayam Tuangyu. The study employs a descriptive exploratory approach with a case study methodology, utilizing both primary and secondary quantitative data. Data analysis was conducted using non-statistical research techniques with the EOQ method. Through EOQ, a more economical order quantity of 570 kg per order was determined, with a purchasing frequency of 14 times per year. Additionally, a safety stock of 170 kg was established, and the reorder point was set at 192 kg. The implementation of EOQ in rice inventory control is highly feasible, as this method optimizes inventory management and reduces costs by IDR 1,658,144.

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