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<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Artificial Intelligence, Applications and Innovations</JournalTitle>
      <Issn>3060-7124</Issn>
      <Volume>1</Volume>
      <Issue>Journal of Artificial Intelligence, Application and Inovations</Issue>
      <PubDate PubStatus="epublish">
        <Year>2024</Year>
        <Month>10</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Scientific Trend Analysis of Artificial Intelligence Applications in  Banking Models using Text Mining Techniques</ArticleTitle>
    <VernacularTitle>Scientific Trend Analysis of Artificial Intelligence Applications in  Banking Models using Text Mining Techniques</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>13</LastPage>
    <ELocationID EIdType="doi">10.61838/jaiai.1.4.1</ELocationID>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2024</Year>
        <Month>05</Month>
        <Day>24</Day>
      </PubDate>
    </History>
    <Abstract>&lt;p&gt;Reviewing scientific articles and comparing their status can identify scientific gaps and potential opportunities. This study focuses on the field of hybrid models of banking and artificial intelligence (AI). AI applications in banking have grown significantly, ranging from fraud detection and risk assessment to personalized customer services and automated trading systems. These technologies are not only enhancing operational efficiency but also transforming how financial institutions interact with their customers and manage risks. In this paper, after extracting data from the Scopus database, categorization was performed on 4,795 reputable articles over the past 14 years (2010-2023). Clusters were created using text mining techniques to assign subject labels in the interdisciplinary fields of AI and banking. The Box-Jenkins approach was then used to select a model on the data and predict and analyze trends over different periods. The results indicate the primary focus areas for applying AI in banking are: Innovation, Technologies and Digital Banking (58.89%), Commercial and Investment Banking (27.13%), Retail, Personal and Wealth Management Banking (9.49%), and International and Global Operations Banking (4.48%).&lt;/p&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Banking models</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Artificial Intelligence</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Text mining</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Classification</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">K-Nearest Neighbors</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Box-Jenkins</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journalaiai.com/index.php/aiai/article/download/66/23</ArchiveCopySource>
  </Article>
</ArticleSet>
