APPLICATION OF AI IN BIG DATA ANALYTICS TO FINANCIAL RISK FORECASTING
Dublin Core
Title
APPLICATION OF AI IN BIG DATA ANALYTICS TO FINANCIAL RISK FORECASTING
            Creator
Hanh Dao Thi
            Proceedings Item Type Metadata
meta_title
APPLICATION OF AI IN BIG DATA ANALYTICS TO FINANCIAL RISK FORECASTING
            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.
            publication_date
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/480
            keywords
Artificial Intelligence (AI); Financial risk forecasting; Credit risk; Market risk
            firstpage
1050
            lastpage
1055
            issn
3047-857X
            conference
Proceedings of the International Symposium on Management (Volume 21, 2024)
            Volume
22
            publisher_name
Fakultas Bisnis dan Ekonomika, Universitas Surabaya
            no article
168
            Citation
Hanh Dao Thi, “APPLICATION OF AI IN BIG DATA ANALYTICS TO FINANCIAL RISK FORECASTING,” Proceedings of the International Symposium on Management (INSYMA), accessed October 31, 2025, https://insyma.org/proceedings/items/show/480.
