Application of Artificial Intelligence for Fraudulent Banking Operations Recognition

: This study considers the task of applying artificial intelligence to recognize bank fraud. In recent years, due to the COVID-19 pandemic, bank fraud has become even more common due to the massive transition of many operations to online platforms and the creation of many charitable funds that criminals can use to deceive users. The present work focuses on machine learning algorithms as a tool well suited for analyzing and recognizing online banking transactions. The study’s scientific novelty is the development of machine learning models for identifying fraudulent banking transactions and techniques for preprocessing bank data for further comparison and selection of the best results. This paper also details various methods for improving detection accuracy, i.e., handling highly imbalanced datasets, feature transformation, and feature engineering. The proposed model, which is based on an artificial neural network, effectively improves the accuracy of fraudulent transaction detection. The results of the different algorithms are visualized, and the logistic regression algorithm performs the best, with an output AUC value of approximately 0.946. The stacked generalization shows a better AUC of 0.954. The recognition of banking fraud using artificial intelligence algorithms is a topical issue in our digital society.

[1]  Salwa Zolkaflil,et al.  A systematic literature review of money mule: its roles, recruitment and awareness , 2023, Journal of Financial Crime.

[2]  Atta ur Rehman Khan,et al.  Memory Forensics-Based Malware Detection Using Computer Vision and Machine Learning , 2022, Electronics.

[3]  D. U. Ozsahin,et al.  Impact of feature scaling on machine learning models for the diagnosis of diabetes , 2022, 2022 International Conference on Artificial Intelligence in Everything (AIE).

[4]  N. Shakhovska,et al.  Stacking Machine Learning Model for the Assessment of R&D Product’s Readiness and Method for Its Cost Estimation , 2022, Mathematics.

[5]  J. Rojo-álvarez,et al.  On the Black-Box Challenge for Fraud Detection Using Machine Learning (I): Linear Models and Informative Feature Selection , 2022, Applied Sciences.

[6]  Zenghui Wang,et al.  A machine learning based credit card fraud detection using the GA algorithm for feature selection , 2022, Journal of Big Data.

[7]  Waqas Mahmood,et al.  Technology Adoption in Pakistani Banking Industry using UTAUT , 2022, International Journal of Information Technology and Computer Science.

[8]  Monika Arora,et al.  Artificial Intelligence in Collaborative Information System , 2022, International Journal of Modern Education and Computer Science.

[9]  Andi Nurkholis,et al.  Application of Support Vector Machine (SVM) Algorithm in Classification of Low-Cape Communities in Lampung Timur , 2021, Building of Informatics, Technology and Science (BITS).

[10]  Boniface Kabaso,et al.  Hybrid Machine Learning: A Tool to Detect Phishing Attacks in Communication Networks , 2021, International Journal of Advanced Computer Science and Applications.

[11]  Meyliana,et al.  Extending the Design of Smart Mobile Application to Detect Fraud Theft of E-Banking Access Using Big Data Analytic and SOA , 2021, 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE).

[12]  Giorgio Terracina,et al.  Enhanced air quality prediction by edge-based spatiotemporal data preprocessing , 2021, Comput. Electr. Eng..

[13]  Abolfazl Mehbodniya,et al.  Financial Fraud Detection in Healthcare Using Machine Learning and Deep Learning Techniques , 2021, Secur. Commun. Networks.

[14]  A. Yousif,et al.  Assessment of Knowledge Management Application in Banking Sector of Sudan: Case Study Farmer’s Commercial Bank , 2021, International Journal of Information Engineering and Electronic Business.

[15]  Sherali Zeadally,et al.  Blockchain-Based Solution for Detecting and Preventing Fake Check Scams , 2021, IEEE Transactions on Engineering Management.

[16]  Auliya Rahman Isnain,et al.  Implementation of K-Nearest Neighbor (K-NN) Algorithm For Public Sentiment Analysis of Online Learning , 2021, IJCCS (Indonesian Journal of Computing and Cybernetics Systems).

[17]  Bahzad Charbuty,et al.  Classification Based on Decision Tree Algorithm for Machine Learning , 2021, Journal of Applied Science and Technology Trends.

[18]  Justin D. Weisz,et al.  Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks , 2021, ACM Trans. Comput. Hum. Interact..

[19]  Aaron Zimba.  A Bayesian Attack-Network Modeling Approach to Mitigating Malware-Based Banking Cyberattacks , 2021, International Journal of Computer Network and Information Security.

[20]  Asif Ali Laghari,et al.  Quality of Experience Assessment of Banking Service , 2020, International Journal of Information Engineering and Electronic Business.

[21]  R. Barker.  The use of proactive communication through knowledge management to create awareness and educate clients on e-banking fraud prevention , 2020 .

[22]  Pin-Chang Su,et al.  The application of multi-server authentication scheme in internet banking transaction environments , 2020, Information Systems and e-Business Management.

[23]  Umi Kalsom Yusof,et al.  Combining oversampling and undersampling techniques for imbalanced classification: A comparative study using credit card fraudulent transaction dataset , 2020, 2020 IEEE 16th International Conference on Control & Automation (ICCA).

[24]  Shaik Mazhar Hussain,et al.  Design and development for detection and prevention of ATM skimming frauds , 2020, Indonesian Journal of Electrical Engineering and Computer Science.

[25]  Juliana Freire,et al.  A Large-Scale Study About Quality and Reproducibility of Jupyter Notebooks , 2019, 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR).

[26]  Goldie Gabrani,et al.  Python for Data Analytics, Scientific and Technical Applications , 2019, 2019 Amity International Conference on Artificial Intelligence (AICAI).

[27]  V. Vasanthi,et al.  Machine Learning Algorithms with ROC Curve for Predicting and Diagnosing the Heart Disease , 2018, Soft Computing and Medical Bioinformatics.

[28]  Ariel Rokem,et al.  Confidence Intervals for Random Forests in Python , 2017, J. Open Source Softw..

[29]  Helmut Krcmar,et al.  Big Data , 2014, Wirtschaftsinf..

[30]  C. Mood.  Logistic Regression: Why We Cannot Do What We Think We Can Do, and What We Can Do About It , 2010 .

[31]  Mohammed Shuaib,et al.  Unbalanced Credit Card Fraud Detection Data: A Machine Learning-Oriented Comparative Study of Balancing Techniques , 2023, Procedia Computer Science.

[32]  N. Nguyen,et al.  A Proposed Model for Card Fraud Detection Based on CatBoost and Deep Neural Network , 2022, IEEE Access.

[33]  Ibomoiye Domor Mienye,et al.  A Neural Network Ensemble With Feature Engineering for Improved Credit Card Fraud Detection , 2022, IEEE Access.

[34]  Thanh-Nghi Do,et al.  ImageNet Challenging Classification with the Raspberry Pis: A Federated Learning Algorithm of Local Stochastic Gradient Descent Models , 2022, FDSE.

[35]  Bouabid El Ouahidi,et al.  Credit Card Fraud Detection Model Based on LSTM Recurrent Neural Networks , 2021, Journal of Advances in Information Technology.

[36]  Jagdish Chandra Patni,et al.  Machine learning model for credit card fraud detection- a comparative analysis , 2021, Int. Arab J. Inf. Technol..

[37]  Giorgio Terracina,et al.  Generalizing identity-based string comparison metrics: Framework and techniques , 2020, Knowl. Based Syst..

[38]  G. Moruzzi.  Plotting with Matplotlib , 2020 .

[39]  Josh Cutler,et al.  Introduction to Machine Learning with Python , 2020, Textbooks on Political Analysis.

[40]  Ishu Trivedi,et al.  Credit Card Fraud Detection , 2016 .