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Article Details

Vol. 8 No. 2 (2026): September

A Decision Support System (DSS) for Fraud Detection Using Genetic Support Vector Machine (GSVM)

https://doi.org/10.35912/ijfam.v8.n2.p289-302.2026

Abstract

Purpose: This study developed a Decision Support System (DSS) for fraud prediction using a Genetic Support Vector Machine (GSVM), a hybrid model that employs a Genetic Algorithm (GA) to optimize Support Vector Machine (SVM) hyperparameters (C and γ) on financial ratios extracted from MachameRatios®.

Research Methodology: A quantitative ex post facto design using data from 75 purposively sampled quoted manufacturing firms across six sectors over an 11-year panel (2011–2021) was assessed. Class imbalances were handled via Synthetic Minority Oversampling Technique (SMOTE), data were labelled via a performance-based threshold, and the hybrid framework was benchmarked against Decision Tree, Bayesian Networks, and Naïve Bayes classifiers.

Results: The GSVM with an Radial Basis Function (RBF) kernel achieved an optimal 10-fold cross-validation accuracy of 87.91%, 89.4% sensitivity, and an Area Under the Receiver Operating Characteristic Curve (AUC-ROC) of 0.92, significantly outperforming the standard baseline models.

Conclusions: The study concludes that the wrapper-based GSVM provides a highly accurate and parsimonious nonparametric pipeline for financial screening, establishing a mathematically robust foundation for automating corporate surveillance in regional markets.

Limitations: This study relied solely on the financial ratio architectures of the sampled manufacturing firms.

Contributions: The system addresses data imbalance via SMOTE and demonstrates that GA-optimized feature selection outperforms other methods.

Keywords

Artificial Intelligence Decision Support System Genetic Support Vector Machine Machine Learning

How to Cite

Egbunike, C., Onyali, C., & Okafor, K. (2026). A Decision Support System (DSS) for Fraud Detection Using Genetic Support Vector Machine (GSVM). International Journal of Financial, Accounting, and Management, 8(2), 289–302. https://doi.org/10.35912/ijfam.v8.n2.p289-302.2026

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References

  1. Abualrob, L. A. R., & Maswadeh, S. N. (2020). The effect of financial ratios derived from operating cash flows on Jordanian commercial banks’ earnings per share. International Journal of Financial Research, 11(1), 394-404. https://doi.org/10.5430/ijfr.v11n1p394
  2. Adhikari, A., Bhatta, S., Jangwan, H. S., Mishra, A., Nisa, K. U., Zamani, A. T., & Parveen, N. (2025). A two-stage ensemble feature selection and particle swarm optimization approach for microarray data classification in distributed computing environments. ArXiv Preprint, 1-22. https://doi.org/10.48550/arXiv.2507.04251
  3. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609. https://doi.org/10.1111/j.1540-6261.1968.tb0 0843.x
  4. Balcerzak, A. P., & Valaskova, K. (2024). Artificial intelligence: Financial management under pressure of transformative technology. Equilibrium. Quarterly Journal of Economics and Economic Policy, 19(4), 1127-1137. https://doi.org/10.24136/eq.3394
  5. Bayraktar, E., Tatoglu, E., Aydiner, A. S., & Delen, D. (2024). Business analytics adoption and technological intensity: An efficiency analysis. Information Systems Frontiers, 26(4), 1509-1526. https://doi.org/10.1007/s10796-023-10424-3
  6. BBR. (1930). A Test Analysis of Unsuccessful Industrial Companies. Urbana: University of Illinois Press.
  7. Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171
  8. Budha, S., Ho, N., & Brown, M. (2025). Hybrid genetic algorithm and support vector machine for prediction of student dropout and success. 2025 IEEE Integrated STEM Education Conference (ISEC). https://doi.org/10.1109/ISEC64801.2025.11147283
  9. Che, W., Wang, Z., Jiang, C., & Abedin, M. Z. (2024). Predicting financial distress using multimodal data: An attentive and regularized deep learning method. Information Processing & Management, 61(4), 103703. https://doi.org/10.1016/j.ipm.2024.103703
  10. Chen, Y., & Wu, Z. (2022). Financial fraud detection of listed companies in China: A machine learning approach. Sustainability, 15(1), 1-15. https://doi.org/10.3390/su15010105
  11. Chen, Y.-S., Lin, C.-K., Lo, C.-M., Chen, S.-F., & Liao, Q.-J. (2021). Comparable studies of financial bankruptcy prediction using advanced hybrid intelligent classification models to provide early warning in the electronics industry. Mathematics, 9(20), 1-26. https://doi.org/10.3390/math9202622
  12. Dahiyat, A. A., Weshah, S. R., & Aldahiyat, M. (2021). Liquidity and solvency management and its impact on financial performance: Empirical evidence from Jordan. The Journal of Asian Finance, Economics and Business, 8(5), 135-141. https://doi.org/10.13106/jafeb.2021.vol8.no5.0135
  13. Dainelli, F., Bet, G., & Fabrizi, E. (2024). The financial health of a company and the risk of its default: Back to the future. International Review of Financial Analysis, 95, 1-19. https://doi.org/10.1016/j.irfa.2024.103449
  14. Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1), 167-179. https://doi.org/10.2307/2490225
  15. Egbunike, F. C., Anachedo, C. K., Echekoba, F. N., & Ubesie, C. M. (2022). A comparative study of genetic algorithm and neural network model in bankruptcy prediction of manufacturing firms in Nigeria. Journal of Contemporary Issues in Accounting, 3(1), 231-256.
  16. Fahreza, M. A., Zaharuddin, Z., & Safariningsih, R. T. H. (2026). The effect of external pressure and financial targets on financial statement fraud. Advances in Management and Business Studies, 1(3), 199-207. https://doi.org/10.35912/ambus.v1i3.4170
  17. Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., Ra, I. H., & Alazab, M. (2020). Early detection of diabetic retinopathy using PCA-firefly based deep learning model. Electronics, 9(2), 1-16. https://doi.org/10.3390/electronics9020274
  18. Gajdosikova, D., Michulek, J., & Tulyakova, I. (2025). AI-Based bankruptcy prediction for agricultural firms in Central and Eastern Europe. International Journal of Financial Studies, 13(3), 1-35. https://doi.org/10.3390/ijfs13030133
  19. Gholampoor, H., & Asadi, M. (2024). Risk analysis of bankruptcy in the US healthcare industries based on financial ratios: A machine learning analysis. Journal of Theoretical and Applied Electronic Commerce Research, 19(2), 1303-1320. https://doi.org/10.3390/jtaer19020066
  20. Gregova, E., Valaskova, K., Adamko, P., Tumpach, M., & Jaros, J. (2020). Predicting financial distress of slovak enterprises: Comparison of selected traditional and learning algorithms methods. Sustainability, 12(10), 1-17. https://doi.org/10.3390/su12103954
  21. Holland, J. H. (1992). Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. Cambridge: The MIT Press.
  22. Kumar, S. (2024). Is depreciation fraud detectable using ADTFA and DAAT financial models? A case study. International Journal of Financial, Accounting, and Management, 5(4), 459-474. https://doi.org/10.34104/ijfam.024.04590474
  23. Li, X., Da, X., Shi, W., & Liu, W. (2023). Manufacturing companies financial fraud detection based on interpretable machine learning. Proceedings of the 2nd International Conference on Public Management, Digital Economy and Internet Technology (ICPDI 2023), 114–128, 1-11. https://doi.org/10.4108/eai.1-9-2023.2338768
  24. Liu, W., Suzuki, Y., & Du, S. (2025). Ensemble learning algorithms based on easyensemble sampling for financial distress prediction. Annals of Operations Research, 346(3), 2141-2172. https://doi.org/10.1007/s10479-025-06494-y
  25. Luaha, L., Fahmi, F., & Zarlis, M. (2024). Performance analysis of Support Vector Machine (SVM) model through parameter optimization with Genetic Algorithm (GA) in chronic kidney disease classification. 2024 8th International Conference on Electrical, Telecommunication and Computer Engineering (ELTICOM). https://doi.org/10.1109/ELTICOM64085.2024.10864385
  26. Makolo, A., & Adeboye, T. (2021). Credit card fraud detection system using machine learning. International Journal of Information Technology and Computer Science, 13(4), 24-37. https://doi.org/10.5815/ijitcs.2021.04.03
  27. Martín, G. C. J., & Herrero, B. (2025). Employee satisfaction, firm performance and financial distress. The Journal of Risk Finance, 26(3), 393-409. https://doi.org/10.1108/JRF-11-2024-0358
  28. Medina-Olivares, V., Calabrese, R., Dong, Y., & Shi, B. (2022). Spatial dependence in microfinance credit default. International Journal of Forecasting, 38(3), 1071-1085. https://doi.org/10.1016/j.ijforecast.2021.05.009
  29. Naeem, A., Khan, M. F., Rezaei, S., Iqbal, A., Sohail, M., Jatoi, M., & Shakeel, A. (2025). Comparing genetic algorithms and principal component analysis in reducing feature dimensionality. Proceedings of Machine Learning Research, 45–58.
  30. Nazareth, N., & Ramana Reddy, Y. V. (2023). Financial applications of machine learning: A literature
  31. review. Expert Systems with Applications, 219, 1-33. https://doi.org/10.1016/j.eswa.2023.119640
  32. Nemati, Z., Mohammadi, A., Bayat, A., & Mirzaei, A. (2024). Financial ratios and efficient classification algorithms for fraud risk detection in financial statements. International Journal of Industrial Mathematics, 16(1), 46-63. https://doi.org/10.30495/ijim.2023.72769.1663
  33. Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. https://doi.org/10.2307/2490395
  34. Ramser, J., & Foster, L. (1931). A demonstration of ratio analysis (Bulletin No 40). Urbana: University of Illinois.
  35. Saheed, Y. K., Usman, A. A., Sukat, F. D., & Abdulrahman, M. (2023). A novel hybrid autoencoder and modified particle swarm optimization feature selection for intrusion detection in the Internet of Things network. Frontiers in Computer Science, 5, 1-13. https://doi.org/10.3389/fcomp.2023.99 7159
  36. Sahu, P. K., & Fatma, T. (2025). Optimized breast cancer classification using PCA-LASSO feature selection and ensemble learning strategies with Optuna optimization. IEEE Access, 13, 11024– 11039. https://doi.org/10.1109/ACCESS.2025.3534821
  37. Salah, H. A., Ahmed, A. S., & Layeb, S. B. (2025). Towards an integrated decision support system for the evaluation of data mining tools in an economic intelligence system. JOIV: International Journal on Informatics Visualization, 9(6), 2487-2502. https://doi.org/10.62527/joiv.9.6.3635
  38. Santoro, G., Jabeen, F., Kliestik, T., & Bresciani, S. (2025). AI-powered growth hacking: Benefits, challenges and pathways. Management Decision, 63(10), 3389-3407. https://doi.org/10.1108/MD-10-2023-1964
  39. Sarumi, J. A. (2021). Fraud detection in customers' electricity consumption in Nigeria using support vector machine and C4.5 decision tree algorithms. Computing, Information Systems, Development Informatics & Allied Research Journal, 12(1), 43-64. https://doi.org/10.22624/aims/cisdi/v12n1p6
  40. Seretidou, D., Billios, D., & Stavropoulos, A. (2025). Integrative analysis of traditional and cash flow financial ratios: Insights from a systematic comparative review. Risks, 13(4), 1-27. https://doi.org/10.3390/risks13040062
  41. Shahriyar, O., Moghaddam, B. N., Yousefi, D., Mirzaei, A., & Hoseini, F. (2025). An analysis of the combination of feature selection and machine learning methods for an accurate and timely detection of lung cancer. ArXiv Preprint, 1-9. https://doi.org/10.48550/arXiv.2501.10980
  42. Sowah, R. A., Kuuboore, M., Ofoli, A., Kwofie, S., Asiedu, L., Koumadi, K. M., & Apeadu, K. O. (2019). Decision Support System (DSS) for fraud detection in health insurance claims using Genetic Support Vector Machines (GSVMs). Journal of Engineering, 2019, 1-29. https://doi.org/10.1155/2019/5823197
  43. Suwardi, A. D. ., & Riyadi, S. (2024). The effect of financial performance targets and financial stability on financial report fraud practices with independent commissioners as moderating variables (Empirical study on companies’ sector consumer goods industry period 2018 - 2022). Global Academy of Business Studies, 1(1), 71-92. https://doi.org/10.35912/gabs.v1i1.3575
  44. Valaskova, K., Nagy, M., & Juracka, D. (2025). Digital transformation and financial performance: An empirical analysis of strategic alignment in the digital age. Journal of Enterprising Communities: People and Places in the Global Economy, 19(5), 1178-1205. https://doi.org/10.1108/jec-11-2024-0241
  45. Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer.
  46. Wang, G., Chen, G., Zhao, H., Zhang, F., Yang, S., & Lu, T. (2021). Leveraging multisource heterogeneous data for financial risk prediction: A novel hybrid-strategy-based self-adaptive method. MIS Quarterly, 45(4), 1949-1998. https://doi.org/10.25300/MISQ/2021/16118
  47. Wang, G., Ma, J., & Chen, G. (2023). Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention. Decision Support Systems, 167. https://doi.org/10.1016/ j.dss.2022.113913
  48. Wasserbacher, H., & Spindler, M. (2022). Machine learning for financial forecasting, planning and analysis: Recent developments and pitfalls. Digital Finance, 4(1), 63-88. https://doi.org/10.1007/s 42521-021-00046-2
  49. Wei, C., & Qian, X. (2025). Bridging the semantic gap: An ensemble learning framework with textual topic‐raw financial feature fusion to enhance fraud detection in Chinese markets. Journal of Mathematics, 2025(1), 1-16. https://doi.org/10.1155/jom/6643152
  50. Widiastuti, M. S., Umar, H. B., & Hafiziandra, Y. (2025). The influence of the e-catalogue system on fraud prevention in goods and services procurement in the regional government of Mimika Regency. Global Academy of Multidisciplinary Studies, 1(4), 249-264. https://doi.org/10.35912/gams.v1i4.3550
  51. Xiao, L., Wang, C. W., Deng, Y., Yang, Y. J., Lu, J., Yan, J. F., & Peng, Q. H. (2024). HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy. International Journal of Ophthalmology, 17(6), 991-1002. https://doi.org/10.18240/ijo.2024.06.03

Authors

1 Chinedu Francis Egbunike
Nnamdi Azikiwe University, Anambra, Nigeria
https://orcid.org/0000-0002-4159-8630
2 Chidiebele Onyali
Nnamdi Azikiwe University, Anambra, Nigeria
https://orcid.org/0000-0001-7832-0619
3 Kenebechukwu Okafor
Nnamdi Azikiwe University, Anambra, Nigeria

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Vol. 8 No. 2 (2026): September
Section: Articles
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