ARTIFICIAL INTELLIGENCE IN BANKING: A BIBLIOMETRIC REVIEW

Abstract

This study examines the existing scientific foundations on the connection between artificial intelligence (AI) and the banking sector. It reviews 218 peer-reviewed articles published between 2010 and2024 and indexed in the Web of Science Core Collection. The literature is discussed in three dimensions, namely, intellectual structure, thematic structure and geographical distribution. With VOSviewer, the co-occurrence networks of key words, authors collaboration patterns, citation structures, and development of thematics are mapped to help to see the pre-existing research areas and the emergent trends. The results reveal that discipline has a solid foundation on technological base, especially machine learning, big data, and blockchain. Nonetheless, some more recent research has started to explore behavioral and more ethical concerns of AI, such as trust, explainability, and user acceptance. Although the thematic scope of the field has expanded, international and institutional collaboration remains limited. There are distinct research gaps that can be identified, especially in explainable AI in banking, green finance and cross-national comparative research. These results demonstrate the relevance of interdisciplinary cooperation in facilitating the responsible application of AI in the banking industry. In order to add to the co-occurrence analysis, Latent Dirichlet Allocation (LDA) topic modeling was applied on 216 article abstracts (k = 5) to reduce potential bias in keyword-based analyses. The findings indicate that there are five recurring latent themes, namely credit risk and fraud detection; Know Your Customer (KYC) and Anti-Money Laundering (AML); trust and explainability in AI systems; AI applications in digital banking services; and Environmental, Social, and Governance (ESG) considerations. An important contribution of the research is the fact that the findings have been related to the theory of technology adoption, especially the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). The results also have a policy implication in that they go in line with developments in AI governance systems today. Altogether, this paper summarizes the existing research on AI in the banking sector and outlines directions for future research.

Keywords

• Artificial Intelligence
• Banking
• Bibliometric Analysis
• Machine Learning
• Fintech.

BANKACILIKTA YAPAY ZEKA: BİBLİYOMETRİK BİR İNCELEME

Abstract

Bu çalışmada, yapay zeka (AI) ile bankacılık sektörü arasındaki bağlantıya ilişkin mevcut bilimsel temelleri ele alınmıştır. 2010-2024 yılları arasında yayınlanan ve Web of Science Core Collection'da indekslenen 218 adet hakemli makaleyi incelenmiştir. Literatür, entelektüel yapı, tematik yapı ve coğrafi dağılım olmak üzere üç boyut dikkate alınmaktadır. VOSviewer ile anahtar kelimelerin eşzamanlılık ağları, yazarların iş birliği modelleri, atıf yapıları ve tematik gelişmeler haritalandırılarak, önceden var olan araştırma alanları ve ortaya çıkan yönelimler daha net bir şekilde ortaya çıkmıştır. Sonuçlar, disiplinin özellikle makine öğrenimi, büyük veri ve blok zinciri gibi teknolojik temellere dayandığını ortaya koymaktadır. Bununla birlikte, bazı daha yeni araştırmalar, güven, açıklanabilirlik ve kullanıcı kabulü gibi AI'nın davranışsal ve daha etik kaygılarını göstermektedir. Alanın tematik kapsamı genişlemiş olsa da, uluslararası ve kurumsal işbirliği sınırlı kalmaktadır. Özellikle bankacılık, yeşil finans ve ülkeler arası karşılaştırmalı araştırmalarda açıklanabilir AI konusunda belirgin araştırma boşlukları tespit edilmiştir. Bu sonuçlar, bankacılık sektöründe AI'nın sorumlu bir şekilde uygulanmasını kolaylaştırmada disiplinler arası işbirliğinin önemini göstermektedir. Eşzamanlılık analizine ek olarak, anahtar kelimelerin önyargılı görüşlerini azaltmak için 216 makale özetine (k = 5) Latent Dirichlet Allocation (LDA) konu modellemesi uygulanmıştır. Bulgular, beş tekrar eden gizil tema olduğunu göstermektedir: kredi riski ve dolandırıcılık tespiti; Müşterini Tanı (KYC) ve Kara Para Aklamayla Mücadele (AML); AI sistemlerinde güven ve açıklanabilirlik; dijital bankacılık hizmetlerinde AI uygulamaları; ve Çevresel, Sosyal ve Yönetişim (ESG) hususları. Araştırmanın önemli bir katkısı, bulguların teknoloji benimseme teorisiyle, özellikle Teknoloji Kabul Modeli (TAM) ve Teknoloji Kabul ve Kullanımının Birleşik Teorisi (UTAUT) ile ilişkili olmasıdır. Sonuçlar, günümüzün AI yönetişim sistemlerindeki gelişmelerle uyumlu olması bakımından politika açısından da önemlidir. Bu makale, bankacılık sektöründe AI ile ilgili mevcut araştırmaları özetlemekte ve gelecekteki araştırmalar için yönlendirmeler sunmaktadır.

Keywords

• Yapay Zeka
• Bankacılık
• Bibliyometrik Analiz
• Makine Öğrenmesi
• Fintek

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