6th DOCTORAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE
(DoSCI-2025)
28-29 March 2025.
Analysing Machine Learning Algorithms for Identifying Fraudulent Financial Transactions
Ritam Maity*, Tegil J John, Romit Shah
*PG Scholar,
Department Of Computer Science,
Christ University, Bengaluru 560029, India
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INDEX:
Abstract
Introduction
Literature Review
Research Gaps
Proposed Methodology
Results & Discussions
Comparative Analysis
Conclusion & Future Work
References
2. ABSTRACT:
As we witness the era of digital payments and trans- actions, which has increased exponentially because of the ever- emerging advanced technology,
recognizing fraudulent and legitimate transactions has become a volatile challenge that banking institutions face these days. The resulting by-product
of digital transformation is a significant rise in fraudulent activities that affect banking institutions, merchants, and banks. Traditional methods of
identifying such statements, including manual auditing and inspection, are costly, imprecise, and time-consuming. Machine learning algorithms and
other latest technologies are being applied in the financial sector to support trading activities, mobile banking, payments, and making customer credit
decisions. In this comprehensive study, I have reviewed many methods of machine learning that detect financial fraud. Since Ensemble Learning
techniques were employed more than unsupervised and supervised methods like clustering and random forest. The next research and analysis should
intensify the focus on unsupervised semi-supervised for fraud detection in new emerging problems in the field of digital financial fraud.
3. INTRODUCTION:
The latest progress made in electronic and internet-based Payment systems have changed. The financial landscape offers a seamless experience in
digital transactions across the globe. Still, this digital transformation in the financial sector has also brought new challenges and threats along with it,
including growing fraudulent activities in the spheres of finance, health, insurance, etc. The ever-growing technology for online payments has
emerged as a significant worry for the masses and banking sector. Conventional methods of fraud detection are a method that is comprised of manual
auditing. Supervisions and evaluations are mostly insignificant systems that finally lead to higher costs and more human involvement Resources and
time consuming in nature. Machine learning Algorithms offer a structured and efficient form of achieving the reduction in the risk of fraudulent
financial operations with growing accuracy and significantly improved fraud-detecting efficiency. Machine learning has become a critical tool within
the financial sector. Can look at huge volumes of data and notice identify patterns that may otherwise evade detection by human auditors and
traditional tools methods. The following comparative analysis explores how fraud detection is accomplished using various machine learning
algorithms that identify fraudulent transactions, emphasizing their effectiveness and limitations. This study specifically explores ensemble learning
styles, which have become the trend over other unsupervised approaches include clustering. Another critical This is a huge reason to consider, as
ensemble learning strategies that integrate a mix of different frameworks to Deliver superior performance and usage in fraudulent actions. Detection
has been somewhat promising. But the dynamic characteristics of this industry make this sector continuously blended. The subsequent sections will
provide a comparative outline, including a complete and detailed review of prior literature available for ML approaches to identify fraudulent
transactions. So, to find the best algorithm, a variety of ML algorithms and models, including ensemble methods and supervised and hybrid
approaches, have been analysed to determine the optimal technique to reduce financial fraud and point out research gaps for future venture.
4. LITERATURE REVIEW :
5. RESEARCH GAPS :
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Limited Research on Unsupervised & Semi-Supervised Learning – More studies are needed on these
methods for fraud detection instead of relying mainly on ensemble learning.
Challenges in Handling Class Imbalance – While SMOTE and ADASYN are used, generative models (e.g.,
VAEs) could improve synthetic fraud sample generation.
Adaptability to Evolving Fraud Tactics – Fraud patterns change rapidly; reinforcement learning and federated
learning could enhance model adaptability.
Integration of Financial Risk Assessment – Combining machine learning with financial risk metrics (e.g.,
Value-at-Risk, Expected Shortfall) could improve fraud detection strategies.
Real-Time Fraud Detection & Explainability – Research should focus on real-time fraud detection with
Explainable AI (XAI) to enhance transparency and decision-making.
6. PROPOSED METHODOLOGY :
7. RESULTS & DISCUSSION:
8. COMPARATIVE ANALYSIS:
9. CONCLUSION & FUTURE WORK :
📌 Conclusion
✅ Ensemble learning models (XGBoost, Random Forest, AdaBoost) outperform traditional methods in fraud
detection.
✅ Deep learning (CNNs, ResNeXt-GRU) achieves high accuracy (~99.9%) but requires large datasets.
✅ Addressing class imbalance (SMOTE, ADASYN, VAEs) enhances model training, reducing false negatives.
✅ Hybrid & bio-inspired models (Firefly Optimization + SVM) improve fraud detection efficiency.
🚀 Future Scope
🔹 Unsupervised & Semi-Supervised Learning – Exploring new fraud detection techniques beyond supervised models.
🔹 Explainable AI (XAI) for Fraud Detection – Making fraud detection models transparent for financial analysts.
🔹 Real-Time Fraud Detection – Developing low-latency fraud detection for instant alerts.
🔹 Federated Learning for Privacy-Preserving AI – Enhancing security in financial fraud detection.
🔹 Risk-Based Fraud Prevention – Integrating financial risk assessment models for better fraud detection.
10. REFERENCES :
[1] S. K. Hashemi, S. L. Mirtaheri and S. Greco,” Fraud Detection in Banking Data by Machine Learning Techniques,” in IEEE Access, vol. 11, pp. 3034-3043, 2023, doi:10.1109/ACCESS.2022.3232287.
[2] J. Jemai, A. Zarrad and A. Daud, ”Identifying Fraudulent Credit Card Transactions Using Ensemble Learning,” in IEEE Access, vol. 12, pp. 54893-54900, 10.1109/ACCESS.2024.3380823. 2024
[3] E. Ileberi, Y. Sun and Z. Wang, ”Performance Evaluation of Machine Learning Methods for Credit Card Fraud Detection Using SMOTE and AdaBoost,” in IEEE Access, vol. 9, pp. 165286-165294, 2021, doi:
10.1109/ACCESS.2021.3134330.
[4] A. A. Taha and S. J. Malebary, ”An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine,” in IEEE Access, vol. 8, pp. 25579-25587, 2020, doi: 10.1109/AC- CESS.2020.2971354.
[5] A. Thennakoon, C. Bhagyani, S. Premadasa, S. Mihiranga and N. Ku- ruwitaarachchi, ”Real-time Credit Card Fraud Detection Using Machine Learning,” 2019 9th International Conference on Cloud Computing, Data Science and Engineering
(Confluence), Noida, India, 2019, pp. 488-493, doi: 10.1109/CONFLUENCE.2019.8776942.
[6] F. K. Alarfaj, I. Malik, H. U. Khan, N. Almusallam, M. Ramzan & M. Ahmed, ”Credit Card Fraud Detection Using State-of-the Art Machine Learning and Deep Learning Algorithms,” in IEEE Access, vol. 10, pp. 39700-39715, 2022, doi:
10.1109/ACCESS.2022.3166891.
[7] Hajek, P., Abedin, M.Z. & Sivarajah, U. Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework. Inf Syst Front 25, 1985–2003 (2023).
[8] K. Randhawa, C. K. Loo, M. Seera, C. P. Lim and A. K. Nandi, ”Credit Card Fraud Detection Using AdaBoost and Majority Voting,” in IEEE Access, vol. 6, pp. 14277-14284, 2018, doi: 10.1109/AC- CESS.2018.2806420.
[9] A. A. Almazroi and N. Ayub, ”Online Payment Fraud Detection Model Using Machine Learning Techniques,” in IEEE Access, vol. 11, pp. 137188-137203, 2023, doi: 10.1109/ACCESS.2023.3339226.
[10] S. Ray, ”A Quick Review of Machine Learning Algorithms,” 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India, 2019, pp.35-39, doi:
10.1109/COMITCon.2019.8862451.
[11] T. K. Dang, T. C. Tran, L. M. Tuan, and M. V. Tiep, ”Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems,” Applied Sciences, vol. 11, no. 21, p. 10004, Oct.
2021. doi: 10.3390/app112110004.
[12] N. S. Alfaiz and S. M. Fati, "Enhanced Credit Card Fraud Detection Model Using Machine Learning," Electronics, vol. 11, no. 4, p. 662, Feb. 2022. doi: 10.3390/electronics11040662.
[13] A. U. Usman, S. B. Abdullahi, Y. Liping, B. Alghofaily, A. S. Almasoud and A. Rehman, "Financial Fraud Detection Using Value-at-Risk With Machine Learning in Skewed Data," in IEEE Access, vol. 12, pp. 64285-64299, 2024, doi:
10.1109/ACCESS.2024.3393154.
[14] H. Tingfei, C. Guangquan and H. Kuihua, "Using Variational Auto Encoding in Credit Card Fraud Detection," in IEEE Access, vol. 8, pp. 149841-149853, 2020, doi: 10.1109/ACCESS.2020.3015600.
[15] T. R. Noviandy, G. M. Idroes, A. Maulana, I. Hardi, E. S. Ringga, and R. Idroes, "Credit card fraud detection for contemporary financial management using XGBoost-driven machine learning and data augmentation techniques," Indatu J.
Manag. Account., vol. 1, no. 1, pp. 29-35, 2023.
[16] A. Alwadain, R. F. Ali, and A. Muneer, "Estimating Financial Fraud through Transaction-Level Features and Machine Learning," Mathematics, vol. 11, no. 5, p. 1184, Feb. 2023, doi: 10.3390/math11051184.
[17] T. Ashfaq, R. Khalid, A. S. Yahaya, S. Aslam, A. T. Azar, S. Alsafari, and I. A. Hameed, "A machine learning and blockchain based efficient fraud detection mechanism," Sensors, vol. 22, no. 21, p. 7162, Sep. 2022, doi:
10.3390/s22197162.
[18] A. Singh, A. Jain, and S. E. Biable, "Financial fraud detection approach based on firefly optimization algorithm and support vector machine," Appl. Comput. Intell. Soft Comput., vol. 2022, Art. no. 1468015, pp. 1-10, Jun. 2022, doi:
10.1155/2022/1468015.
[19] E. Rawashdeh, N. Al-Ramahi, H. Ahmad, and R. Zaghloul, "Efficient credit card fraud detection using evolutionary hybrid feature selection and random weight networks," Chronicle, vol. 2024, pp. 1-12, Sept. 2023. doi:
10.5267/j.ijdns.2023.9.009.
[20] M. Seera, C. P. Lim, A. Kumar, L. Dhamotharan, and K. H. Tan, "An intelligent payment card fraud detection system," Ann. Oper. Res., vol. 334, pp. 445–467, 2024, doi: 10.1007/s10479-021 04149-2.