Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN)
Abstract This study pursues dual objectives; firstly, to scrutinize the determinants critical to AI’s sustained utilization within banking and secondly, to scrutinize the intermediary role of technological knowledge amidst the factors of technology adaptation and continued usage intention. A survey...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-03-01
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| Series: | Future Business Journal |
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| Online Access: | https://doi.org/10.1186/s43093-025-00464-3 |
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| author | Komal Goyal Megha Garg Shruti Malik |
| author_facet | Komal Goyal Megha Garg Shruti Malik |
| author_sort | Komal Goyal |
| collection | DOAJ |
| description | Abstract This study pursues dual objectives; firstly, to scrutinize the determinants critical to AI’s sustained utilization within banking and secondly, to scrutinize the intermediary role of technological knowledge amidst the factors of technology adaptation and continued usage intention. A survey engaging bank professionals who routinely employ AI for risk and fraud assessment was conducted. The data was analyzed using SmartPLS. 4 in two stages using structural equation modeling (SEM) and artificial neural network (ANN). The study proposes a hierarchical model showing that the perceived ease of use has a significant positive influence on the attitude toward the use of technology, but holds no direct significance on continued usage intention for artificial intelligence. The results are further validated using artificial neural network analysis. In light of these insights, bank policy strategists are better equipped to tailor approaches to navigate the structural and regulatory impediments to the AI adoption process. |
| format | Article |
| id | doaj-art-73c9756eb99f472d91a9bb032c3e9f16 |
| institution | OA Journals |
| issn | 2314-7210 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Future Business Journal |
| spelling | doaj-art-73c9756eb99f472d91a9bb032c3e9f162025-08-20T02:10:19ZengSpringerOpenFuture Business Journal2314-72102025-03-0111112010.1186/s43093-025-00464-3Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN)Komal Goyal0Megha Garg1Shruti Malik2J.C. Bose University of Sciences and Technology, YMCAJ.C. Bose University of Sciences and Technology, YMCAShri Ram College of Commerce, University of DelhiAbstract This study pursues dual objectives; firstly, to scrutinize the determinants critical to AI’s sustained utilization within banking and secondly, to scrutinize the intermediary role of technological knowledge amidst the factors of technology adaptation and continued usage intention. A survey engaging bank professionals who routinely employ AI for risk and fraud assessment was conducted. The data was analyzed using SmartPLS. 4 in two stages using structural equation modeling (SEM) and artificial neural network (ANN). The study proposes a hierarchical model showing that the perceived ease of use has a significant positive influence on the attitude toward the use of technology, but holds no direct significance on continued usage intention for artificial intelligence. The results are further validated using artificial neural network analysis. In light of these insights, bank policy strategists are better equipped to tailor approaches to navigate the structural and regulatory impediments to the AI adoption process.https://doi.org/10.1186/s43093-025-00464-3Artificial intelligenceCredit risk assessmentFraud detectionBanking sectorData privacyEthical considerations |
| spellingShingle | Komal Goyal Megha Garg Shruti Malik Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) Future Business Journal Artificial intelligence Credit risk assessment Fraud detection Banking sector Data privacy Ethical considerations |
| title | Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) |
| title_full | Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) |
| title_fullStr | Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) |
| title_full_unstemmed | Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) |
| title_short | Adoption of artificial intelligence-based credit risk assessment and fraud detection in the banking services: a hybrid approach (SEM-ANN) |
| title_sort | adoption of artificial intelligence based credit risk assessment and fraud detection in the banking services a hybrid approach sem ann |
| topic | Artificial intelligence Credit risk assessment Fraud detection Banking sector Data privacy Ethical considerations |
| url | https://doi.org/10.1186/s43093-025-00464-3 |
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