A ROBUST DETECTION FRAUDULENT TRANSACTIONS IN BANKING USING MACHINE LEARNING
Main Article Content
Abstract
Vulnerability in banking systems has exposed us to fraudulent acts, which cause severe damage to both customers and the bank in terms of loss of money and reputation. Financial fraud in banks is estimated to result in a significant amount of financial loss annually. Early detection of this helps to mitigate the fraud, by developing a counter strategy and recovering from such losses. A machine learning-based approach is proposed in this paper to contribute to fraud detection successfully. The artificial intelligence (AI) based model will speed up the check verification to counteract the counterfeits and lower the damage. In this paper, we analyzed numerous intelligent algorithms trained on a public dataset to find the correlation of certain factors with fraudulence. The dataset utilized for this research is resampled to minimize the high class of imbalance in it and analyzed the data using the proposed algorithm for better accuracy.
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Licensing
TURCOMAT publishes articles under the Creative Commons Attribution 4.0 International License (CC BY 4.0). This licensing allows for any use of the work, provided the original author(s) and source are credited, thereby facilitating the free exchange and use of research for the advancement of knowledge.
Detailed Licensing Terms
Attribution (BY): Users must give appropriate credit, provide a link to the license, and indicate if changes were made. Users may do so in any reasonable manner, but not in any way that suggests the licensor endorses them or their use.
No Additional Restrictions: Users may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
References
R. Rambola, P. Varshney and P. Vishwakarma, “Data Mining Techniques for Fraud Detection in Banking Sector,”
4th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India,
, pp. 1-5, doi: 10.1109/CCAA.2018.8777535.
N. Malini and M. Pushpa, “Analysis on credit card fraud identification techniques based on KNN and outlier
detection,” 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication
and Bio-Informatics (AEEICB), Chennai, 2017, pp. 255-258, doi: 10.1109/AEEICB.2017.7972424.
Ishan Sohony, Rameshwar Pratap, and Ullas Nambiar. 2018. Ensemble learningfor credit card fraud detection. In
Proceedings of the ACM India Joint InternationalConference on Data Science and Management of Data (CoDSCOMAD ’18). Association for Computing Machinery, New York, NY, USA, 289–294.
DOI:https://doi.org/10.1145/3152494.3156815
C. Wang, Y. Wang, Z. Ye, L. Yan, W. Cai, and S. Pan, “Credit Card Fraud Detection Based on Whale Algorithm
Optimized BP Neural Network,” 2018 13th International Conference on Computer Science Education (ICCSE),
Colombo, 2018, pp. 1-4, doi: 10.1109/ICCSE.2018.8468855
I. Benchaji, S. Douzi and B. ElOuahidi, ”Using Genetic Algorithm to Improve Classification of Imbalanced
Datasets for Credit Card Fraud Detection,” 2018 2nd Cyber Security in Networking Conference (CSNet), Paris, 2018,
pp. 1-5, doi: 10.1109/CSNET.2018.8602972.
John O. Awoyemi, Adebayo Olusola Adetunmbi, and Samuel Adebayo Oluwadare. Credit card fraud detection
using machine learning techniques: Acomparative analysis. 2017 International Conference on Computing Networking
and Informatics (ICCNI), pages 1–9, 2017.
Fabrizio Carcillo, Andrea Dal Pozzolo, Yann-A¨el Le Borgne, Olivier Caelen, Yannis Mazzer, and Gianluca
Bontempi. Scarff: a scalable framework for streamingcredit card fraud detection with spark. Information Fusion, 41:182–194, 2018. [8] Galina Baader and Helmut Krcmar. Reducing false positives in fraud detection: Combining the
red flag approach with process mining. International Journal of Accounting Information Systems, 2018. [9]
Ravisankar P, Ravi V, Raghava Rao G, and Bose, Detection of financial statement fraud and feature selection using
data mining techniques, Elsevier, Decision Support Systems Volume 50, Issue 2, p491-500 (2011) SVM
K. Seeja, and M. Zareapoor, “FraudMiner: A Novel Credit Card Fraud Detection Model Based on Frequent
Itemset Mining,” The Scientific World Journal,2014, pp. 1-10. KNN, SVM
C. Tyagi, P. Parwekar, P. Singh, and K. Natla, “Analysis of Credit Card FraudDetection Techniques,” Solid State
Technology, vol. 63, no. 6, 2020, pp. 18057- 18069. Credit card faud
C. Chee, J. Jaafar, I. Aziz, M. Hassan, and W. Yeoh, “Algorithms for frequentitemset mining: a literature review,”
Artificial Intelligence Review, vol. 52, 2019, pp. 2603–2621. Litrature review AI
S. Kiran, J. Guru, R. Kumar, N. Kumar, D. Katariya, and M. Sharma, “Credit card fraud detection using Naïve
Bayes model based and KNN classifier,” International Jounral of Advance Research, Ideas and Innovations in
Technology, vol. 4, 2018, pp. 44-47. KNN Naïve Byers