ARTIFICIAL INTELLIGENCE MODELS AND TOOLS THAT MONITOR AND MONITOR THE SECURITY OF DIGITAL BANKING DATA
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| Natura: | Recurso digital |
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Zenodo
2025
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| _version_ | 1866901773006929920 |
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| author | Shirinov Sherali Ramazon o'g'li |
| author_facet | Shirinov Sherali Ramazon o'g'li |
| contents | <p><em><span lang="EN-US">As digital banking becomes ubiquitous in Uzbekistan, ensuring the security of sensitive financial data is paramount. This article explores how artificial intelligence (AI) models and tools are being leveraged to monitor and safeguard digital banking information. A comprehensive literature review was conducted to identify the most promising AI techniques and their applications in the Uzbek banking sector. The results demonstrate that machine learning algorithms, especially anomaly detection models, are highly effective at identifying fraudulent transactions and unauthorized access attempts in real-time. Furthermore, natural language processing tools enable automated analysis of unstructured data like customer service logs to surface potential security issues. Blockchain technology is also being piloted to create tamper-proof audit trails. </span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17921483 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | ARTIFICIAL INTELLIGENCE MODELS AND TOOLS THAT MONITOR AND MONITOR THE SECURITY OF DIGITAL BANKING DATA Shirinov Sherali Ramazon o'g'li <p><em><span lang="EN-US">As digital banking becomes ubiquitous in Uzbekistan, ensuring the security of sensitive financial data is paramount. This article explores how artificial intelligence (AI) models and tools are being leveraged to monitor and safeguard digital banking information. A comprehensive literature review was conducted to identify the most promising AI techniques and their applications in the Uzbek banking sector. The results demonstrate that machine learning algorithms, especially anomaly detection models, are highly effective at identifying fraudulent transactions and unauthorized access attempts in real-time. Furthermore, natural language processing tools enable automated analysis of unstructured data like customer service logs to surface potential security issues. Blockchain technology is also being piloted to create tamper-proof audit trails. </span></em></p> |
| title | ARTIFICIAL INTELLIGENCE MODELS AND TOOLS THAT MONITOR AND MONITOR THE SECURITY OF DIGITAL BANKING DATA |
| url | https://doi.org/10.5281/zenodo.17921483 |