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  <doi_batch_id>aspg-37-4401-1792223418</doi_batch_id>
  <timestamp>20261017075018</timestamp>
  <depositor>
   <depositor_name>American Scientific Publishing Group</depositor_name>
   <email_address>admin@americaspg.com</email_address>
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  <registrant>American Scientific Publishing Group</registrant>
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  <journal>
   <journal_metadata language="en">
    <full_title>Financial Technology and Innovation</full_title>
    <abbrev_title>FinTechI</abbrev_title>
    <issn media_type="electronic">2836-5372</issn>
   </journal_metadata>
   <journal_issue>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <journal_volume>
     <volume>5</volume>
    </journal_volume>
    <issue>1</issue>
   </journal_issue>
   <journal_article publication_type="full_text">
    <titles>
     <title>Explainable Artificial Intelligence for Real-Time Financial Fraud Detection: A Systematic Literature Review</title>
    </titles>
    <contributors>
     <person_name sequence="first" contributor_role="author">
      <given_name>Ulugbek</given_name>
      <surname>Inoyatov</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, Central Asian University, Tashkent, Uzbekistan</institution_name>
       </institution>
      </affiliations>
     </person_name>
     <person_name sequence="additional" contributor_role="author">
      <given_name>Eugene Q.</given_name>
      <surname>Castro</surname>
      <affiliations>
       <institution>
        <institution_name>Department of Computer Science, Central Asian University, Tashkent, Uzbekistan</institution_name>
       </institution>
      </affiliations>
     </person_name>
    </contributors>
    <jats:abstract>
     <jats:p>Financial fraud detection systems increasingly rely on machine learning to identify suspicious transactions at scale. However, the opacity of many high-performing models raises significant concerns regarding trust, regulatory compliance, and practical deployment in real-time financial environments. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance transparency and accountability, yet its feasibility under real-time constraints remains unclear. This systematic literature review examines empirical studies on explainable AI approaches for financial fraud detection, with explicit focus on real-time applicability. Following PRISMA guidelines, nineteen peer-reviewed empirical studies were selected and analyzed based on fraud domain, model type, explainability technique, evaluation metrics, and evidence of real-time performance. Results show that posthoc explanation methods, particularly SHAP and LIME, dominate the literature, while intrinsic explainability and deployment-level latency reporting remain limited. Despite frequent claims of real-time applicability, only one study provides quantitative runtime evidence. The findings highlight critical gaps: absence of explanation latency evaluation, lack of deployment-oriented validation, and insufficient regulatory compliance integration. This review reveals a systematic disconnect between real-time claims and empirical evidence, establishing the need for standardized latency benchmarking in explainable fraud detection research.</jats:p>
    </jats:abstract>
    <publication_date media_type="online">
     <year>2025</year>
    </publication_date>
    <pages>
     <first_page>01</first_page>
     <last_page>09</last_page>
    </pages>
    <publisher_item>
     <item_number item_number_type="article-number">4401</item_number>
    </publisher_item>
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     <ai:license_ref applies_to="vor">https://creativecommons.org/licenses/by/4.0/</ai:license_ref>
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     <doi>10.54216/FinTech-I.050101</doi>
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