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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
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| Paper Title: | Literature survey on AI-driven Fraud Detection across Multiple Domains |
| Authors Name: | Kumudha Shree H , Deepika V , Pavithra S , A Ithihas Reddy , Vijay Kumar S |
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IJRTI_208956
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| Published Paper Id: | IJRTI2601004 |
| Published In: | Volume 11 Issue 1, January-2026 |
| DOI: | |
| Abstract: | The growth of digital finance is creating a new opportunity for criminals to commit fraud and has impacted companies leading to organizational risks. Based on the results of fifteen recent research studies which include financial fraud detection, deep learning applications, blockchain governance, information security, and criminology on fraud behavior are combined in this review of the literature paper. According to the studies, Machine Learning (ML) and Deep Learning (DL) techniques such as LSTM, CNN, Transformer models, and optimization-driven techniques are used to replace traditional statistical models in the detection of fraud. These fraud detection systems increase the precision and flexibility of fraud detection in retail, healthcare, and banking sectors. In addition to this research, it is highlighting the requirement of ethical data governance, organizational policy compliance, and multi- cooperative frameworks for successful fraud prevention. In order to overcome the challenges like class imbalance, transparency, and behavioural factors which are influencing fraud, the literature review is highlighting the trends integrating data resampling, explainable AI (XAI), and sentiment analysis. By considering all of these things, the research is showing that detecting fraud has become a complex problem which requires a multi approach involving technology, laws, and human conduct. In order to promote transparency and trust in digital finance, this literature survey paper is ending with recommendations for the future research directions that will prioritize explainability, cross-domain applications, real-time analytics, and the convergence of criminological theories with AI-driven models.j |
| Keywords: | AI/ML, federated learning, deep learning, blockchain, detection of financial fraud, graph neural networks, and healthcare fraud. |
| Cite Article: | "Literature survey on AI-driven Fraud Detection across Multiple Domains", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.11, Issue 1, page no.a16-a21, January-2026, Available :http://www.ijrti.org/papers/IJRTI2601004.pdf |
| Downloads: | 000181 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2601004
Registration ID:208956
Published In: Volume 11 Issue 1, January-2026
DOI (Digital Object Identifier):
Page No: a16-a21 Country: Bengaluru , Karnataka, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2601004 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2601004 |
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ISSN: 2456-3315
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