مجلة إدارة الأعمال والدراسات الاقتصادية
Volume 12, Numéro 1, Pages 195-214
2026-06-02

Thematic Evolution Of Artificial Intelligence In Financial Engineering: A Bibliometric Analysis



Authors : Kesri Saad . Guettala Aissa .

Abstract

This study conducts a bibliometric analysis of research on Artificial Intelligence in financial engineering over the period 2015–2025. Employing performance analysis and science-mapping methodologies, including co-citation, co-authorship, and co-word analysis, this study analyzes 995 peer-reviewed publications sourced from the Scopus database. The findings reveal a swift expansion of the literature, particularly pronounced following 2022, which is attributable to developments in machine learning and deep learning techniques. The predominant research areas encompass market forecasting, risk management, and bankruptcy prediction, with models such as LSTM, Random Forest, and XGBoost exhibiting considerable predictive accuracy. China, India, and the United States are identified as the principal contributors to global collaborative networks. Furthermore, recent research increasingly highlights robo-advisory services and the necessity of explainable AI to improve transparency within financial decision-making processes. Consequently, these findings offer a structured overview of significant trends, influential sources, and prospective research avenues within the domain of AI-based financial engineering. تتناول هذه الدراسة تحليلاً ببليومترياً للأبحاث المرتبطة بالذكاء الاصطناعي في الهندسة المالية خلال الفترة 2015–2025، بالاعتماد على تحليل الأداء ورسم الخرائط العلمية لعدد 995 منشوراً مفهرساً في قاعدة سكوبس. وتكشف النتائج عن نمو متسارع في الإنتاج العلمي، خاصة بعد عام 2022، نتيجة التطورات في تعلم الآلة والتعلم العميق. وتركزت أبرز الموضوعات البحثية في التنبؤ بالأسواق، وإدارة المخاطر، والتنبؤ بالإفلاس، مع كفاءة عالية لنماذج مثل LSTM وXGBoost. كما تزايد الاهتمام بالاستشارة الآلية والذكاء الاصطناعي القابل للتفسير لتعزيز شفافية القرارات المالية.

Keywords

artificial intelligence ; financial engineering ; decision-making ; bibliometric analysis

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