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All Journal JURNAL Al-AZHAR INDONESIA SERI SAINS DAN TEKNOLOGI Berdikari : Jurnal Ekonomi dan Statistik Indonesia JIM: Jurnal Ilmiah Mahasiswa Pendidikan Sejarah Jurnal Inovasi Hasil Pengabdian JUSTICE: Journal of Social and Political Science
Yuniar, Miandhani Denniz
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Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Journal : Berdikari : Jurnal Ekonomi dan Statistik Indonesia

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Analysis of Insecurity in Gen-Z Influenced by TikTok Social Media Use Based on the Theory of Uses and Gratifications Yuniar, Miandhani Denniz; Hanipah , Aninda Hawa; Amrullah, Ezar
Jurnal Ekonomi Dan Statistik Indonesia Vol 5 No 2 (2025): Berdikari: Jurnal Ekonomi dan Statistik Indonesia (JESI)
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/jesi.05.02.15

Abstract

This study examines the integration of financial mathematics and artificial intelligence (AI), focusing on credit risk analysis within the financial industry. Traditionally, financial mathematics has played a central role in modeling and assessing credit risk. However, recent technological advancements have enabled a more synergistic relationship between financial mathematics and AI, particularly through the use of machine learning. This integration enhances risk assessment by leveraging classification algorithms to uncover complex patterns in historical data patterns that are often difficult to detect using conventional methods. Through a case study in the financial sector, this research explores the application of classification algorithms to better understand customer behavior, evaluate credit history, and predict the likelihood of future loan repayment. In the realm of machine learning, classification techniques are essential for processing large volumes of data and generating accurate risk predictions. By combining the analytical rigor of financial mathematics with the predictive power of machine learning, this study aims to develop a robust and adaptive model for credit risk analysis one capable of responding to dynamic market and economic conditions, and improving the overall decision-making process in credit assessment.
Co-Authors Amrullah, Ezar Bintang Amartiya Putri Fiddina, Qori' Afiata Hanipah , Aninda Hawa Kharismatul Ula, Elok Laila Syaidatul Karima Utami Mj Rizqon Hasani SITTA SARAYA Ula, Elok Kharismatul Yuniar, Miandhani Zulaekha, Titik