https://mail.insyma.org/proceedings/files/original/articles/2. Vietnam_Tran Duc Thuc.pdf Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Proceedings of the International Symposium on Management (INSYMA) (Vol 22, 2025) Proceedings meta_title APPLICATION OF AI IN BIG DATA ANALYTICS TO FINANCIAL RISK FORECASTING Author #1 Hanh Dao Thi Abstract/Description Artificial intelligence (AI) is increasingly asserting its important role in many fields, especially healthcare and finance, thanks to its ability to process and analyze large amounts of diverse data, detecting complex relationships that are difficult for traditional methods to recognize. In the medical field, AI has proven to be outstandingly effective in diagnosis and treatment support. Similarly, in finance, AI is being strongly applied to risk forecasting, especially credit risk and market risk. Machine learning and deep learning models help increase the accuracy of predictions, processing both traditional and unstructured data. However, the implementation of AI also comes with challenges such as ethics, privacy, and transparency in the model. This study aims to overview the applications of AI in financial risk forecasting, and analyze the potential and challenges to overcome to optimize risk management efficiency in the context of big data. keywords a;b;c;d Artificial Intelligence (AI); Financial risk forecasting; Credit risk; Market risk publication_date 2025/06/20 2025/06/20 pdf_url https://insyma.org/proceedings/files/articles/2. Vietnam_Tran Duc Thuc.pdf abstract_html_url https://insyma.org/proceedings/items/show/ https://insyma.org/proceedings/items/show/480 firstpage 1050 lastpage 1055 issn 3047-857X 3047-857X conference Proceedings of the International Symposium on Management (INSYMA) Proceedings of the International Symposium on Management (Volume 21, 2024) publisher_name Fakultas Bisnis dan Ekonomika, Universitas Surabaya Fakultas Bisnis dan Ekonomika, Universitas Surabaya no article 168 Volume 22 authors Hanh Dao Thi Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource APPLICATION OF AI IN BIG DATA ANALYTICS TO FINANCIAL RISK FORECASTING Creator An entity primarily responsible for making the resource Hanh Dao Thi