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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 3
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21686
Article Published : 8549
Total Authors : 22487
Total Reviewer : 811
Total Countries : 159
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| Paper Title: | Bank Fraud Detection Using ML |
| Authors Name: | Ritik Singh , Rana Poddar |
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| Author Reg. ID: |
IJRTI_204086
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| Published Paper Id: | IJRTI2505224 |
| Published In: | Volume 10 Issue 5, May-2025 |
| DOI: | |
| Abstract: | Financial institutions are increasingly vulnerable to fraudulent activities due to weaknesses in banking systems, which not only tarnish their reputation but also result in considerable financial losses for both the institutions and their clients. Each year, a significant sum is lost to financial fraud, underscoring the pressing need for effective strategies to mitigate such risks. This research introduces a machine learning-based methodology aimed at enhancing fraud detection and facilitating the recovery of losses. Our focus is on utilizing artificial intelligence (AI) to optimize the check verification process, thereby addressing counterfeit activities. To establish correlations between various factors and fraudulent behavior, we conducted an analysis of a comprehensive dataset and applied several sophisticated algorithms. To enhance accuracy, the dataset was resampled to rectify the class imbalance issue. The algorithms employed in this study include XGBoost, Random Forest, and KNN classifiers, which are designed to more effectively identify credit card fraud and fraudulent transactions. Keywords: blockchain, artificial intelligence, XGBoost, Random Forest, KNN classifier, credit card fraud, fraudulent transactions. |
| Keywords: | Machine learning Artificial intelligence (AI), Credit card fraud, Fraudulent transactions ,Class imbalance, Check verification, Counterfeit detection, Financial security |
| Cite Article: | "Bank Fraud Detection Using ML ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c232-c239, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505224.pdf |
| Downloads: | 000446 |
| ISSN: |
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: IJRTI2505224
Registration ID:204086
Published In: Volume 10 Issue 5, May-2025
DOI (Digital Object Identifier):
Page No: c232-c239 Country: Gautam Buddh Nagar, Uttar Pradesh, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2505224 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2505224 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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