Corporate Financial Risk Trend Prediction Using ARIMA-Based Machine Learning: A Semantic Literature Review

Deni Sunaryo; Hamdan; Alfina Anggriani; Cecilia Winata; Dian Denta Alumi

Authors

Deni Sunaryo Universitas Serang Raya
Hamdan Universitas Serang Raya
Alfina Anggriani Universitas Serang Raya
Cecilia Winata Universitas Serang Raya
Dian Denta Alumi Universitas Serang Raya

Keywords:

Machine Learning, ARIMA, Financial Risk Prediction, Financial Model, Artificial Intelligence, Time Series

Abstract

This research discusses the role of artificial intelligence (AI) technology in improving company financial risk predictions through the application of the Autoregressive Integrated Moving Average (ARIMA) model supported by machine learning. Using systematic literature analysis, this research explores how ARIMA, with the help of AI, can be applied in financial risk management to identify short-term and long-term trends, detect financial anomalies, and improve the quality of decision making. ARIMA models based on machine learning enable faster and more accurate identification of abnormal financial patterns, which is very important in helping companies respond to risks that have the potential to disrupt financial stability. Key findings show that the combination of ARIMA and AI not only makes it easier to analyze large and complex financial data, but also expands the potential of data-driven financial risk management by providing deeper insights. By integrating machine learning, ARIMA becomes more effective in handling dynamic and fluctuating financial data, so companies can anticipate risks more proactively. This study underscores the enormous potential of AI and ARIMA in helping companies build adaptive, high-precision risk prediction systems, offering new opportunities to strengthen financial resilience in an ever-changing business environment.

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