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Fusion: Practice and Applications
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Title

AI-based model for fraud detection in bank systems

  Ahmed Al-Fatlawi 1 * ,   Ahmed A. Talib Al-Khazaali 2 ,   Sajjad H. Hasan 3

1  Department of Computer Techniques Engineering University of AlKafeel Al-Najaf, Iraq
    (ahmed.fatlawi@alkafeel.edu.iq)

2  Department of Computer Techniques Engineering University of AlKafeel Al-Najaf, Iraq
    (ahmed.ali@alkafeel.edu.iq)

3  Department of Computer Techniques Engineering University of AlKafeel Al-Najaf, Iraq
    (sajad.hadi@alkafeel.edu.iq)

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Doi   :   https://doi.org/10.54216/FPA.140102

Received: May 09, 2023 Revised: August 18, 2023 Accepted: November 03, 2023

Abstract :

Due to the very high direct or indirect costs of fraud, banks and financial institutions seek to accelerate the recognition of the activities of fraudsters. The reason for this is its direct effect on serving the customers of these institutions, reducing operating costs and remaining as a reliable and valid financial service provider. On the other hand, in recent years, with the development of information and communication technology, electronic banking has become very popular. In the meantime, it is inevitable to use fraud detection techniques to prevent fraudulent actions in banking systems, especially electronic banking systems. In this paper, a method has been developed that leads to the improvement of fraud detection in information security and cyber defense systems. The main purpose of fraud detection systems is to predict and detect false financial transactions and improve the intrusion detection system using information classification. In this regard, the genetic algorithm, which is known as one of the stochastic optimization methods, is used. At the end, the results of the genetic algorithm have been compared with the results of the decision tree classification and the regression tree. The simulation results show the effectiveness and superiority of the proposed method.

 

Keywords :

Artificial intelligence; Intrusion detection system; Genetic algorithm; Banking system; Information security; Cyber defense; Fraud detection

References :

[1]     Mohammed G. Fathi Al-Obaidi,  Intelligent Classification for Credit Scoring Based on a Data Mining algorithm,  Journal of Intelligent Systems and Internet of Things,  Vol. 9 ,  No. 2 ,  (2023) : 149-161 (Doi   :  https://doi.org/10.54216/JISIoT.090211)

[2]     Darwish, Saad M. "A bio-inspired credit card fraud detection model based on user behavior analysis suitable for business management in electronic banking." Journal of Ambient Intelligence and Humanized Computing 11, no. 11 (2020): 4873-4887.

[3]     Bagga, Siddhant, Anish Goyal, Namita Gupta, and Arvind Goyal. "Credit card fraud detection using pipeling and ensemble learning." Procedia Computer Science 173 (2020): 104-112.

[4]     Rai, Arun Kumar, and Rajendra Kumar Dwivedi. "Fraud detection in credit card data using unsupervised machine learning based scheme." In 2020 international conference on electronics and sustainable communication systems (ICESC), pp. 421-426. IEEE, 2020.

[5]     Darwish, Saad M. "An intelligent credit card fraud detection approach based on semantic fusion of two classifiers." Soft Computing 24, no. 2 (2020): 1243-1253.

[6]     Sadgali, Imane, S. A. E. L. Nawal, and Fouzia Benabbou. "Fraud detection in credit card transaction using machine learning techniques." In 2019 1st International Conference on Smart Systems and Data Science (ICSSD), pp. 1-4. IEEE, 2019.

[7]     Noor Hanoon Haroon, Hanan Burhan Saadon, Ansam Mohammed Abed, Ahmed Taha, Maryam Ghassan Majeed, Marwan Qaid Mohammed, Salem Saleh Bafjaish,  Developing a Smart Economy Using Statistical Framework-Based Business Models in Smart Cities,  Journal of Intelligent Systems and Internet of Things,  Vol. 9 ,  No. 2 ,  (2023) : 194-205 (Doi   :  https://doi.org/10.54216/JISIoT.090214

[8]     Reem Atassi, Fuad Alhosban,  Predictive Maintenance in IoT: Early Fault Detection and Failure Prediction in Industrial Equipment,  Journal of Intelligent Systems and Internet of Things,  Vol. 9 ,  No. 2 ,  (2023) : 231-238 (Doi   :  https://doi.org/10.54216/JISIoT.090217)

[9]     Al-Hashedi, Khaled Gubran, and Pritheega Magalingam. "Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019." Computer Science Review 40 (2021): 100402.

[10]   Bagga, Siddhant, Anish Goyal, Namita Gupta, and Arvind Goyal. "Credit card fraud detection using pipeling and ensemble learning." Procedia Computer Science 173 (2020): 104-112.

[11]   Sahu, Aanchal, G. M. Harshvardhan, and Mahendra Kumar Gourisaria. "A dual approach for credit card fraud detection using neural network and data mining techniques." In 2020 IEEE 17th India council international conference (INDICON), pp. 1-7. IEEE, 2020.

[12]   Darwish, Saad M. "A bio-inspired credit card fraud detection model based on user behavior analysis suitable for business management in electronic banking." Journal of Ambient Intelligence and Humanized Computing 11, no. 11 (2020): 4873-4887.

[13]   Wang, Chunzhi, Yichao Wang, Zhiwei Ye, Lingyu Yan, Wencheng Cai, and Shang Pan. "Credit card fraud detection based on whale algorithm optimized BP neural network." In 2018 13th international conference on computer science & education (ICCSE), pp. 1-4. IEEE, 2018.

[14]   Hussein, Ameer Saleh, Rihab Salah Khairy, Shaima Miqdad Mohamed Najeeb, and Haider Th ALRikabi. "Credit Card Fraud Detection Using Fuzzy Rough Nearest Neighbor and Sequential Minimal Optimization with Logistic Regression." International Journal of Interactive Mobile Technologies 15, no. 5 (2021).

[15]   Aldo Tenis,Santhosh R., Modelling of an Adaptive Network Model for Phishing Website Detection Using Learning Approaches, Fusion: Practice and Applications, Vol. 12 , No. 2 , (2023) : 159-171 (Doi   :  https://doi.org/10.54216/FPA.120213)

[16]   Katoch, Sourabh, Sumit Singh Chauhan, and Vijay Kumar. "A review on genetic algorithm: past, present, and future." Multimedia Tools and Applications 80, no. 5 (2021): 8091-8126.

[17]   Nadweh, R., " On The Fusion of Neural Networks and Fuzzy Logic, Membership Functions and Weights", Galoitica Journal Of Mathematical Structures and Applications, Vol 7, 2023.

[18]   Charchekhandra, B., " The Reading and Analyzing Of The Brain Electrical Signals To Execute a    Control Command and Move an Automatic Arm", Pure Mathematics for Theoretical Computer Science, Vol 1, 2023.

Cite this Article as :
Style #
MLA Ahmed Al-Fatlawi, Ahmed A. Talib Al-Khazaali, Sajjad H. Hasan. "AI-based model for fraud detection in bank systems." Fusion: Practice and Applications, Vol. 14, No. 1, 2024 ,PP. 19-27 (Doi   :  https://doi.org/10.54216/FPA.140102)
APA Ahmed Al-Fatlawi, Ahmed A. Talib Al-Khazaali, Sajjad H. Hasan. (2024). AI-based model for fraud detection in bank systems. Journal of Fusion: Practice and Applications, 14 ( 1 ), 19-27 (Doi   :  https://doi.org/10.54216/FPA.140102)
Chicago Ahmed Al-Fatlawi, Ahmed A. Talib Al-Khazaali, Sajjad H. Hasan. "AI-based model for fraud detection in bank systems." Journal of Fusion: Practice and Applications, 14 no. 1 (2024): 19-27 (Doi   :  https://doi.org/10.54216/FPA.140102)
Harvard Ahmed Al-Fatlawi, Ahmed A. Talib Al-Khazaali, Sajjad H. Hasan. (2024). AI-based model for fraud detection in bank systems. Journal of Fusion: Practice and Applications, 14 ( 1 ), 19-27 (Doi   :  https://doi.org/10.54216/FPA.140102)
Vancouver Ahmed Al-Fatlawi, Ahmed A. Talib Al-Khazaali, Sajjad H. Hasan. AI-based model for fraud detection in bank systems. Journal of Fusion: Practice and Applications, (2024); 14 ( 1 ): 19-27 (Doi   :  https://doi.org/10.54216/FPA.140102)
IEEE Ahmed Al-Fatlawi, Ahmed A. Talib Al-Khazaali, Sajjad H. Hasan, AI-based model for fraud detection in bank systems, Journal of Fusion: Practice and Applications, Vol. 14 , No. 1 , (2024) : 19-27 (Doi   :  https://doi.org/10.54216/FPA.140102)
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