The External Technological Context of Artificial Intelligence in Financial Services

The purpose of this research is to improve the understanding of the external technological context of Artificial Intelligence applied in financial services, together with other technological solutions such as digitization, automation, cloud computing and licensing. The authors studied the external t...

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Veröffentlicht in:Audit financiar (Bucharest, Romania ) Romania ), 2023-11, Vol.21 (172), p.747-759
Hauptverfasser: DUTESCU, Adriana, MIHAI, Mirela-Simina
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Sprache:eng
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Zusammenfassung:The purpose of this research is to improve the understanding of the external technological context of Artificial Intelligence applied in financial services, together with other technological solutions such as digitization, automation, cloud computing and licensing. The authors studied the external technological context by analyzing the technology providers that are represented by the companies with CAEN code 620 – Service activities in information technology. The purpose of this study was to obtain and analyze answers to six research questions, as follows: what is the role of service providers, what are the other technologies offered by them together with Artificial Intelligence, how can this technology be effectively implemented, what is the involvement users in application development, if there will be specific training and what is the level of implementation of Artificial Intelligence in financial services. The authors found that often the investigated companies use their technology services only for banks, Fintech and large financial companies. Therefore, for small and medium-sized companies, the service providers included in the study are not suitable, and further research should be carried out in this regard. An interesting result emerged from the study of long-term relationships between the technology company and the financial company to improve the solutions and provide the necessary maintenance services until the solutions provide results with close to 100% accuracy.
ISSN:1583-5812
1844-8801
DOI:10.20869/AUDITF/2023/172/025