Arcane Channels of Knowledge Generation in Artificial Intelligence for Fraud Detection in Financial Operations: A Bibliometric Overview

Maryam Tanveer; Farah Naz

doi:10.32350/jarms.71.05

Authors

Maryam Tanveer Accounting and Finance Department, Kinnaird College for Women, Lahore, Pakistan
Farah Naz Accounting and Finance Department, Kinnaird College for Women, Lahore, Pakistan

DOI:

https://doi.org/10.32350/jarms.71.05

Keywords:

Artificial Intelligence (AI), Fraud Detection, Risk Analytics, Bibliometric Analysis

Abstract

In this research, we survey and sketch the bibliometric landscape of intellectual evolution, topic distributions, and growing paths of the multidisciplinary topic of artificial intelligence (AI) used for fraud detection within the finance sector. With 243 entries indexed by Scopus, 2020-2025, this investigation produces a picture to explore, illustrate, and vary in intellectual research across the key actors, institutions, nations, conceptual co-occurrence, by utilizing keywords strategy in Scopus, science-mapping, performance metrics tools in VOSviewer. The main topic discovered is machine learning, anomaly detection, and risk analytics as the methodological base, with the growing area of frontier being explainable AI, blockchain and FinTech, with India being the largest country, but low collaborations. Instead of just reporting the data, in this study we propose a theoretical framework regarding evolution of detection field including rule-based, graph-based and explainable, and benchmarks with previous bibliometric researches. The finding of this paper contributes in several ways, unifying inconsistent knowledge dispersed information, indicating avenues of research in areas with research vacuum, providing ideas for academicians toward publications, and guiding practice regarding fraud-risk governance.

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References

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