Machine Learning for Fraud Detection Leveraging SAP Data: A Case Study for ML Application

References

[1] Ashtiani, M. N., & Raahemi, B. (2021). Intelligent fraud detection in financial statements using machine learning and data mining: a systematic literature review. Ieee Access, 10, 72504-72525. https://doi.org/10.1109/ACCESS.2021.3096799

[2] Tiwari, P., Mehta, S., Sakhuja, N., Kumar, J., & Singh, A. K. (2021). Credit card fraud detection using machine learning: a study. arXiv preprint arXiv:2108.10005. https://doi.org/10.48550/arXiv.2108.10005

[3] Priya, G. J., & Saradha, S. (2021, February). Fraud detection and prevention using machine learning algorithms: a review. In 2021 7th International Conference on Electrical Energy Systems (ICEES) (pp. 564-568). IEEE. https://doi.org/10.1109/ICEES51510.2021.9383631

[4] Sailusha, R., Gnaneswar, V., Ramesh, R., & Rao, G. R. (2020, May). Credit card fraud detection using machine learning. In 2020 4th international conference on intelligent computing and control systems (ICICCS) (pp. 1264-1270). IEEE. https://doi.org/10.1109/ICICCS48265.2020.9121114

[5] Khatri, S., Arora, A., & Agrawal, A. P. (2020, January). Supervised machine learning algorithms for credit card fraud detection: a comparison. In 2020 10th international conference on cloud computing, data science & engineering (confluence) (pp. 680-683). IEEE. https://doi.org/10.1109/Confluence47617.2020.9057851

[6] Kalbande, D., Prabhu, P., Gharat, A., & Rajabally, T. (2021, July). A fraud detection system using machine learning. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. https://doi.org/10.1109/ICCCNT51525.2021.9580102

[7] Lucas, Y., & Jurgovsky, J. (2020). Credit card fraud detection using machine learning: A survey. arXiv preprint arXiv:2010.06479. https://doi.org/10.48550/arXiv.2010.06479

[8] Itoo, F., Meenakshi, & Singh, S. (2021). Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. International Journal of Information Technology, 13(4), 1503-1511. https://doi.org/10.1007/s41870-020-00430-y

[9] Ileberi, E., Sun, Y., & Wang, Z. (2021). Performance evaluation of machine learning methods for credit card fraud detection using SMOTE and AdaBoost. Ieee Access, 9, 165286-165294. https://doi.org/10.1109/ACCESS.2021.3134330

[10] Boutaher, N., Elomri, A., Abghour, N., Moussaid, K., & Rida, M. (2020, November). A review of credit card fraud detection using machine learning techniques. In 2020 5th International Conference on cloud computing and artificial intelligence: technologies and applications (CloudTech) (pp. 1-5). IEEE. https://doi.org/10.1109/CloudTech49835.2020.9365916

[11] Siddiqui, T., Jindal, A., Qiao, S., Patel, H., & Le, W. (2020, June). Cost models for big data query processing: Learning, retrofitting, and our findings. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 99-113). https://doi.org/10.1145/3318464.3380584

[12] Ghane, K. (2020, March). Big data pipeline with ML-based and crowd sourced dynamically created and maintained columnar data warehouse for structured and unstructured big data. In 2020 3rd International Conference on Information and Computer Technologies (ICICT) (pp. 60-67). IEEE. https://doi.org/10.1109/ICICT50521.2020.00018

[13] Mucchetti, M., & Mucchetti, M. (2020). Bigquery ml. BigQuery for Data Warehousing: Managed Data Analysis in the Google Cloud, 419-468. https://doi.org/10.1007/978-1-4842-6186-6_19

[14] Wang, Y., Yang, Y., Zhu, W., Wu, Y., Yan, X., Liu, Y., ... & Tang, Y. (2020). Sqlflow: A bridge between SQL and machine learning. arXiv preprint arXiv:2001.06846. https://doi.org/10.48550/arXiv.2001.06846

[15] Savva, F., Anagnostopoulos, C., & Triantafillou, P. (2020). Ml-aqp: Query-driven approximate query processing based on machine learning. arXiv preprint arXiv:2003.06613. https://doi.org/10.48550/arXiv.2003.06613

[16] Savva, F. (2021). Query-driven learning for automating exploratory analytics in large-scale data management systems (Doctoral dissertation, University of Glasgow). 10.5525/gla.thesis.81907

[17] Makrynioti, N., Ley-Wild, R., & Vassalos, V. (2021, June). Machine learning in SQL by translation to TensorFlow. In Proceedings of the Fifth Workshop on Data Management for End-To-End Machine Learning (pp. 1-11). https://doi.org/10.1145/3462462.3468879

[18] Fard, A., Le, A., Larionov, G., Dhillon, W., & Bear, C. (2020, June). Vertica-ml: Distributed machine learning in vertica database. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (pp. 755-768). https://doi.org/10.1145/3318464.3386137

[19] Nararya, S., Saputra, M., & Puspitasari, W. (2021, August). Automation in Financial Reporting by using Predictive Analytics in SAP Analytics Cloud for Gold Mining Industry: a Case Study. In 2021 International Conference on ICT for Smart Society (ICISS) (pp. 1-6). IEEE. https://doi.org/10.1109/ICISS53185.2021.9532504

[20] Danzer, S., & Hacker, B. (2021). Digitization in Finance and Accounting: Accounting treatment of ERP and RPA systems using the example of SAP S4/HANA at credit institutions (No. 3/2021). Rosenheim Papers in Applied Economics and Business Sciences. https://hdl.handle.net/10419/266205

[21] Utomo, S. B., Yuliani, E. W., & Wulandari, N. (2021, April). Business process improvement (BPI) with enterprise resource planning (ERP) financial & control (FICO) and procurement modules using SAP S/4 HANA to handle non-banking processes in case study: PT bank mantap. In Journal of Physics: Conference Series (Vol. 1807, No. 1, p. 012004). IOP Publishing. https://doi.org/10.1088/1742-6596/1807/1/012004

[22] Gole, V., & Shiralkar, S. (2020). Empower decision makers with SAP analytics cloud. Kaliforniya: Apress. https://doi.org/10.1007/978-1-4842-6097-5

[23] Nazarov, D. M., Nazarov, A. D., & Kovtun, D. B. (2020, June). Building technology and predictive analytics models in the SAP analytic cloud digital service. In 2020 IEEE 22nd conference on business informatics (CBI) (Vol. 2, pp. 106-110). IEEE. https://doi.org/10.1109/CBI49978.2020.10067

[24] Raschig, S., & Schulze, M. (2020). Weiterentwicklung des Finanz-Forecasts im Rahmen der digitalen Transformation am Beispiel der SAP SE. Die Digitalisierung der Controlling-Funktion: Anwendungsbeispiele aus Theorie und Praxis, 25-41. https://doi.org/10.1007/978-3-658-29196-9_3

[25] Surden, H. (2021). Machine learning and law: An overview. Research handbook on big data law, 171-184. https://doi.org/10.4337/9781788972826.00014

[26] Graves, L., Nagisetty, V., & Ganesh, V. (2021, May). Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 35, No. 13, pp. 11516-11524). https://doi.org/10.1609/aaai.v35i13.17371

[27] Janiesch, C., Zschech, P., & Heinrich, K. (2021). Machine learning and deep learning. Electronic markets, 31(3), 685-695. https://doi.org/10.1007/s12525-021-00475-2

[28] Brunton, S. L., Noack, B. R., & Koumoutsakos, P. (2020). Machine learning for fluid mechanics. Annual review of fluid mechanics, 52(1), 477-508. https://doi.org/10.1146/annurev-fluid-010719-060214

[29] Jiang, T., Gradus, J. L., & Rosellini, A. J. (2020). Supervised machine learning: a brief primer. Behavior therapy, 51(5), 675-687. https://doi.org/10.1016/j.beth.2020.05.002

[30] Li, G., Zhou, X., & Cao, L. (2021, October). Machine learning for databases. In Proceedings of the First International Conference on AI-ML Systems (pp. 1-2). https://doi.org/10.1145/3486001.3486248

[31] Jo, T. (2021). Machine learning foundations. Supervised, Unsupervised, and Advanced Learning. Cham: Springer International Publishing, 6(3), 8-44. https://doi.org/10.1007/978-3-030-65900-4

[32] Volk, M. J., Lourentzou, I., Mishra, S., Vo, L. T., Zhai, C., & Zhao, H. (2020). Biosystems design by machine learning. ACS synthetic biology, 9(7), 1514-1533. https://doi.org/10.1021/acssynbio.0c00129

[33] Fradkov, A. L. (2020). Early history of machine learning. IFAC-PapersOnLine, 53(2), 1385-1390. https://doi.org/10.1016/j.ifacol.2020.12.1888

[34] Burkart, N., & Huber, M. F. (2021). A survey on the explainability of supervised machine learning. Journal of Artificial Intelligence Research, 70, 245-317. https://doi.org/10.1613/jair.1.12228

[35] Badillo, S., Banfai, B., Birzele, F., Davydov, I. I., Hutchinson, L., Kam‐Thong, T., ... & Zhang, J. D. (2020). An introduction to machine learning. Clinical pharmacology & therapeutics, 107(4), 871-885. https://doi.org/10.1002/cpt.1796

[36] Bertomeu, J., Cheynel, E., Floyd, E., & Pan, W. (2021). Using machine learning to detect misstatements. Review of Accounting Studies, 26, 468-519. https://doi.org/10.1007/s11142-020-09563-8

[37] Morgan, D., & Jacobs, R. (2020). Opportunities and challenges for machine learning in materials science. Annual Review of Materials Research, 50(1), 71-103. https://doi.org/10.1146/annurev-matsci-070218-010015

[38] Vartiainen, H., Tedre, M., & Valtonen, T. (2020). Learning machine learning with very young children: Who is teaching whom?. International journal of child-computer interaction, 25, 100182. https://doi.org/10.1016/j.ijcci.2020.100182

[39] Hilbert, S., Coors, S., Kraus, E., Bischl, B., Lindl, A., Frei, M., ... & Stachl, C. (2021). Machine learning for the educational sciences. Review of Education, 9(3), e3310. https://doi.org/10.1002/rev3.3310

[40] Artrith, N., Butler, K. T., Coudert, F. X., Han, S., Isayev, O., Jain, A., & Walsh, A. (2021). Best practices in machine learning for chemistry. Nature chemistry, 13(6), 505-508. https://doi.org/10.1038/s41557-021-00716-z

[41] Kumar, P., & Huang, H. H. (2020). Graphone: A data store for real-time analytics on evolving graphs. ACM Transactions on Storage (TOS), 15(4), 1-40. https://doi.org/10.1145/3364180

[42] Harfoush, F. A. (2021). Real-time analytics. In From Big Data to Intelligent Data: An Applied Perspective (pp. 57-62). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-76990-1_4

[43] Indrakumari, R., Poongodi, T., Suresh, P., & Balamurugan, B. (2020). The growing role of integrated and insightful big and real-time data analytics platforms. In Advances in Computers (Vol. 117, No. 1, pp. 165-186). Elsevier. https://doi.org/10.1016/bs.adcom.2019.09.009

[44] Naseer, A., Naseer, H., Ahmad, A., Maynard, S. B., & Siddiqui, A. M. (2021). Real-time analytics, incident response process agility and enterprise cybersecurity performance: A contingent resource-based analysis. International Journal of Information Management, 59, 102334. https://doi.org/10.1016/j.ijinfomgt.2021.102334

[45] Dubuc, T., Stahl, F., & Roesch, E. B. (2020). Mapping the big data landscape: technologies, platforms and paradigms for real-time analytics of data streams. IEEE Access, 9, 15351-15374. https://doi.org/10.1109/ACCESS.2020.3046132

[46] Ting, D., Malkin, J., & Rhodes, L. (2020, August). Data sketching for real time analytics: Theory and practice. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 3567-3568). https://doi.org/10.1145/3394486.3406480

[47] Guo, X., Grushka-Cockayne, Y., & De Reyck, B. (2020). London heathrow airport uses real-time analytics for improving operations. INFORMS Journal on Applied Analytics, 50(5), 325-339. https://doi.org/10.1287/inte.2020.1044

[48] Pendse, S., Krishnaswamy, V., Kulkarni, K., Li, Y., Lahiri, T., Raja, V., ... & Kulkarni, A. (2020, April). Oracle database in-memory on active data guard: Real-time analytics on a standby database. In 2020 IEEE 36th International Conference on Data Engineering (ICDE) (pp. 1570-1578). IEEE. https://doi.org/10.1109/ICDE48307.2020.00139

[49] Verma, P., & Fatima, S. (2020). Smart healthcare applications and real-time analytics through edge computing. Internet of things use cases for the healthcare industry, 241-270. https://doi.org/10.1007/978-3-030-37526-3_11

[50] Li, W., Koo, C., Hong, T., Oh, J., Cha, S. H., & Wang, S. (2020). A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics. Renewable and Sustainable Energy Reviews, 127, 109885. https://doi.org/10.1016/j.rser.2020.109885

@article{1707493,
      author = {Ashitosh Chitnis},
      title = {Machine Learning for Fraud Detection Leveraging SAP Data: A Case Study for ML Application},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {5},
      number = {8},
      pages = {399-411},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707493.pdf},
      abstract = {Businesses encounter serious financial fraud issues which produce major money loss and reputation destruction together with regulatory penalties. Businesses currently use traditional fraud detection tools which are based on rules but they struggle to detect modern complex fraudulent actions. Financial institutions apply Machine Learning as a strong detection tool to process vast financial data which enables them to detect suspicious operational patterns linked to fraud. The research evaluates the implementation of ML-based fraud detection on BigQuery ML which is Google Cloud's machine learning platform through SAP Finance Data. The study executes a process that includes cleans up raw data while applying new fields before choosing an ideal model for training purposes and conducting performance assessments using authentic transaction records. Numerous ML algorithms and logistic regression along with decision trees and deep learning techniques get implemented while comparing their performance using accuracy and other metrics such as precision and recall and F1-score. BigQuery ML shows its capability to detect fraud through efficient data handling capabilities allowing easy processing of large datasets in real time while minimizing system resource consumption. The documented evidence shows ML-based fraud detection outperforms conventional rule-based solutions in terms of precision, speed of detection along with flexibility in application scenarios. BigQuery ML provides enterprise-level tools for model analysis alongside deployment functionalities which enable financial institutions to implement its solutions effectively when handling their large-scale data. Even so the acceptance of these solutions requires solving issues with model interpretability problems and privacy complications along with resource limitations. The research advances understanding of AI-driven financial security through evaluation of cloud-based ML solutions which boost detector systems for financial crimes. Research results reveal the necessity of improving artificial intelligence models with new capabilities that counter fraud schemes as they develop. Studies should focus on developing fraud detection mechanisms through combining explainable AI (XAI) methods with real-time anomaly systems.},
      keywords = {Fraud Detection, BigQuery ML, SAP Finance Data, Machine Learning, Real-Time Analytics},
      month = {February},
  }