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Mobile money fraud detection using data analysis and visualization techniques

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Abstract

Financial investigations in the realm of fraud detection demand rigorous data analysis to identify anomalies and inform decision-making. This paper demonstrates the importance of data visualization as a means of conducting initial assessments of testable datasets to validate their suitability and promptly detect unexpected patterns before delving deeper into investigations. Using the publicly available PAYSIM dataset as a case study, we analyzed 6,362,620 records, of which 8213 were fraudulent and the remainder were legitimate. The dataset comprised 9 features and a single target class. Our analysis reveals the powerful role of visualization in identifying early indications of incompatibility with the dataset and guiding analysts to question its fitness for the context at hand. In particular, we show how visualization can highlight key findings and provide an added emphasis to the results. Through visual and numerical analysis, we demonstrate the importance of identifying potential outliers and other anomalies before proceeding with data preprocessing and modeling. Our results suggest that visual analysis of data is an essential step in detecting fraudulent activities in mobile money transactions. This approach can help to improve the accuracy and efficiency of fraud detection systems, thereby protecting users from financial losses. We conclude that data visualization should be an integral part of any data analysis project, especially in the field of fraud detection, to ensure the validity and suitability of the data before proceeding with further investigations.

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Data availability

The datasets generated during and/or analyzed during the current study are available in [25].

References

  1. Alazzam H, Sharieh A, Sabri KE (2020) A feature selection algorithm for intrusion detection system based on pigeon inspired optimizer. Expert Syst Appl 148:113249

    Article  Google Scholar 

  2. Albrecht C, Duffin KM, Hawkins S, Morales Rocha VM (2019) The use of cryptocurrencies in the money laundering process. Journal of Money Laundering Control 22(2):210–216

  3. Al-Hashedi KG, Magalingam P (2021) Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019. Comput Sci Rev 40:100402

    Article  Google Scholar 

  4. Al-Qudah AA, Al-Okaily M, Alqudah G, Ghazlat A (2022) Mobile payment adoption in the time of the covid-19 pandemic. Electron Commer Res, pages 1–25

  5. Aslam N, Kolekar MH (2022) Unsupervised anomalous event detection in videos using spatio-temporal inter-fused autoencoder. Multimed Tools Appl 81:42457–42482

    Article  Google Scholar 

  6. Aslam N, Rai PK, Kolekar MH (2022) A3n: Attention-based adversarial autoencoder network for detecting anomalies in video sequence. J Vis Commun Image Represent 87:103598

    Article  Google Scholar 

  7. Besenbruch J (2018) Fraud detection using machine learning techniques. Research Paper Business Analytics,  [online] Available: https://beta.vu.nl/nl/Images/werkstukbesenbruch_tcm235-910176.pdf

  8. Bhowmik R (2008) Data mining techniques in fraud detection. J Digit Forensic Secur Law 3(2):3

    Google Scholar 

  9. Botchey FE, Qin Z, Hughes-Lartey K (2020) Mobile money fraud prediction—a cross-case analysis on the efficiency of support vector machines, gradient boosted decision trees, and nave bayes algorithms. Information 11(8):383

    Article  Google Scholar 

  10. Breve B, Caruccio L, Cirillo S, Deufemia V, Polese G (2021) Dependency visualization in data stream profiling. Big Data Research 25:100240

    Article  Google Scholar 

  11. Carcillo F, Le Borgne Y-A, Caelen O, Bontempi G (2018) Streaming active learning strategies for real-life credit card fraud detection: assessment and visualization. Int J Data Sci Anal 5(4):285–300

    Article  Google Scholar 

  12. Caruccio L, Cirillo S, Deufemia V, Polese G (2021) Efficient validation of functional dependencies during incremental discovery. In: SEBD, pages 1–12

  13. Chang R, Lee A, Ghoniem M, Kosara R, Ribarsky W, Yang J, Suma E, Ziemkiewicz C, Kern D, Sudjianto A (2008) Scalable and interactive visual analysis of financial wire transactions for fraud detection. Inf Vis 7(1):63–76

    Article  Google Scholar 

  14. Chen Z, Van Khoa LD, Teoh EN, Nazir A, Karuppiah EK, Lam KS (2018) Machine learning techniques for anti-money laundering (aml) solutions in suspicious transaction detection: a review. Knowl Inf Syst 57(2):245–285

    Article  Google Scholar 

  15. Cochrane N, Gomez T, Warmerdam J, Flores M, Mccullough P, Weinberger V, Pirouz M (2021) Pattern analysis for transaction fraud detection. In: 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), IEEE, pages 0283–0289

  16. Dilla WN, Raschke RL (2015) Data visualization for fraud detection: Practice implications and a call for future research. Int J Account Inf Syst 16:1–22

    Article  Google Scholar 

  17. Botchey FE, Qin Z, Hughes-Lartey K, Ampomah EK (2022) Predicting fraud in mobile money transactions using machine learning: the effects of sampling techniques on the imbalanced dataset. Informatica 45(7)

  18. Gao S, Dongming X (2009) Conceptual modeling and development of an intelligent agent-assisted decision support system for anti-money laundering. Expert Syst Appl 36(2):1493–1504

    Article  Google Scholar 

  19. Gardner C, Lo DC-T (2021) Pca embedded random forest. In: SoutheastCon 2021, IEEE, pages 1–6

  20. Horcas J-M, Galindo JA, Benavides D (2022) Variability in data visualization: a software product line approach. In: Proceedings of the 26th ACM International Systems and Software Product Line Conference-Volume A, pages 55–66

  21. Kokina J, Blanchette S (2019) Early evidence of digital labor in accounting: Innovation with robotic process automation. Int J Account Inf Syst 35:100431

    Article  Google Scholar 

  22. Sarker M (2020) Forensic accounting and fraud examination: Evidence from bangladesh. International Journal of Science and Business 4(9):138–144

  23. Leite RA, Gschwandtner T, Miksch S, Gstrein E, Kuntner J (2018) Visual analytics for event detection: Focusing on fraud. Vis Inform 2(4):198–212

    Article  Google Scholar 

  24. Lopez-Rojas E (2020) Financial Synthetic Data is the New Oil for FinCrime Analytics, [online] Available: https://www.ieee-security.org/TC/SP2020/downloads/st/sp20-shorttalk-7.pdf

  25. Lopez-Rojas E, Elmir A, Axelsson S (2016) Paysim: A financial mobile money simulator for fraud detection. In: 28th European Modeling and Simulation Symposium, EMSS, Larnaca, Dime University of Genoa, pages 249–255

  26. Lurie NH, Mason CH (2007) Visual representation: Implications for decision making. J Mark 71(1):160–177

    Article  Google Scholar 

  27. Lv L-T, Ji N, Zhang J-L (2008) A rbf neural network model for anti-money laundering. In: 2008 International Conference on Wavelet Analysis and Pattern Recognition, IEEE, volume 1, pages 209–215

  28. Maurer B (2012) Mobile money: Communication, consumption and change in the payments space. J Dev Stud 48(5):589–604

    Article  Google Scholar 

  29. Mohamed Amin M, Zainal A, Mohd Azmi NF, Ali NA (2020) Detecting telecommunication fraud with visual analytics: A review. In: IOP Conference Series: Materials Science and Engineering, volume 884, page 012059. IOP Publishing

  30. Ngai EWT, Yong H, Wong YH, Chen Y, Sun X (2011) The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decis Support Syst 50(3):559–569

    Article  Google Scholar 

  31. North C, Shneiderman B (2009) Snap-together visualization: a user interface for coordinating visualizations via relational schemata. In: Proceedings of the working conference on Advanced visual interfaces, pages 128–135

  32. Novikova E, Kotenko I (2019) Visualization-driven approach to fraud detection in the mobile money transfer services. In: Algorithms, Methods, and Applications in Mobile Computing and Communications. IGI Global, pp 205–236

    Chapter  Google Scholar 

  33. Olszewski D (2014) Fraud detection using self-organizing map visualizing the user profiles. Knowl-Based Syst 70:324–334

    Article  Google Scholar 

  34. Pal SC, Abu Reza M, Islam T, Rabin Chakrabortty M, Islam S, Saha A, Shit M (2022) Application of data-mining technique and hydro-chemical data for evaluating vulnerability of groundwater in indo-gangetic plain. J Environ Manag 318:115582

    Article  Google Scholar 

  35. Powers DMW (2020) Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061

  36. Rich ML (2016) Machine learning, automated suspicion algorithms, and the fourth amendment. Univ Pa Law Rev 164:871–929 

  37. Rouhollahi Z (2021) Towards artificial intelligence enabled financial crime detection. arXiv preprint arXiv:2105.10866

  38. Sa’adah S, Pratiwi MS (2020) Classification of customer actions on digital money transactions on paysim mobile money simulator using probabilistic neural network (pnn) algorithm. In: 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), IEEE, pages 677–681

  39. Salehi A, Ghazanfari M, Fathian M (2017) Data mining techniques for anti money laundering. Int J Appl Eng Res 12(20):10084–10094

    Google Scholar 

  40. Sánchez-Aguayo M, Urquiza-Aguiar L, Estrada-Jiménez J (2021) Fraud detection using the fraud triangle theory and data mining techniques: A literature review. Computers 10(10):121

    Article  Google Scholar 

  41. Segovia-Vargas M-J et al (2021) Money laundering and terrorism financing detection using neural networks and an abnormality indicator. Expert Syst Appl 169:114470

    Article  Google Scholar 

  42.  Shaikh ZA, Qayoom A, Rehman SU, Khan AA, Ousmane B, Makar SV, Shkodinsky SV, Dianova TV, Alekseev PV, Chupin, AL, Mtvis (2022) A framework for visual analysis and exploration of mobile money transactions. Journal of Tianjin University Science and Technology 55(4):324–337

  43. Shao C, Yang Y, Juneja S, Tamizharasi GSeetharam. (2022) Iot data visualization for business intelligence in corporate finance. Inf Process Manag 59(1):102736

    Article  Google Scholar 

  44. Sharma A, Panigrahi PK (2013) A review of financial accounting fraud detection based on data mining techniques. arXiv preprint arXiv:1309.3944

  45. Shukla AK, Singh P, Vardhan M (2018) A hybrid gene selection method for microarray recognition. Biocybern Biomed Eng 38(4):975–991

    Article  Google Scholar 

  46. Singh K, Best P (2019) Anti-money laundering: Using data visualization to identify suspicious activity. Int J Account Inf Syst 34:100418

    Article  Google Scholar 

  47. Sun J, Zhu Q, Liu Z, Liu X, Lee J, Zhigang S, Shi L, Huang L, Wei X (2018) Fraudvis: understanding unsupervised fraud detection algorithms. In: 2018 IEEE Pacific Visualization Symposium (PacificVis), IEEE, pages 170–174

  48. Yan C, Siddik AB, Akter N, Dong Q (2021) Factors influencing the adoption intention of using mobile financial service during the covid-19 pandemic: the role of fintech. Environ Sci Pollut Res 30:1–19

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, The University of Jordan, Amman, Jordan

    Rizik Al-Sayyed

  2. Department of Software Engineering, Al-Ahliyya Amman University, Amman, Jordan

    Esra’a Alhenawi

  3. Department of Intelligent Systems, Al-Balqa Applied University, Al-Salt, Jordan

    Hadeel Alazzam

  4. Department of Computer Science, The University of Jordan, Amman, Jordan

    Ala’a Wrikat & Dima Suleiman

Authors
  1. Rizik Al-Sayyed
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  2. Esra’a Alhenawi
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  3. Hadeel Alazzam
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  4. Ala’a Wrikat
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  5. Dima Suleiman
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Correspondence to Hadeel Alazzam.

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Al-Sayyed, R., Alhenawi, E., Alazzam, H. et al. Mobile money fraud detection using data analysis and visualization techniques. Multimed Tools Appl 83, 17093–17108 (2024). https://doi.org/10.1007/s11042-023-16068-4

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