Predicting Accounting Transaction Outcomes Using Financial Performance Indicators: A Machine Learning Approach
Keywords:
Accounting Analytics; Machine Learning; Financial Performance Indicators; Transaction Outcome Prediction; Explainable Artificial IntelligenceAbstract
As digital accounting systems are becoming more common, there has been a tremendous amount of financial data created, which allows for the use of machine learning techniques to enhance accounting decisions. In this study, the aim is to use supervised machine learning algorithms to predict the outcomes of accounting transactions based on the financial performance indicators and to compare the performance of the multiple machine learning algorithms. A dataset containing 1,000 sets of accounting transaction data consisting of 18 variables were analyzed with Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naïve Bayes, XGBoost, LightGBM and CatBoost. The data was preprocessed, explored, engineered and optimized for hyperparameters before models were developed. The evaluation of model performance was done byAccuracy, Precision, Recall, F1-score, ROC-AUC, and Cross-Validation. The results revealed that XGBoost is the optimum model with accuracy of 95.0% and F1-score of 0.974. The Cash Flow, Profit Margin, Operating Expenses, Revenue and Transaction Volume were identified as the most important features from feature importance analysis. The proposed framework highlights the potential of machine learning in enhancing accounting analytics and aiding in making informed financial decisions.
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Gabrielli G, Melioli A, Bertini F. Corporate financial distress prediction: a machine learning approach in the era of big data. Journal of Accounting & Organizational Change. 2026 Dec 14;22(7):31-65.
Patel D. Integrating Machine Learning into Automated Accounting Transaction Classification: Architecture, Algorithms, and Performance Evaluation. Emerging Frontiers Library for The American Journal of Management and Economics Innovations. 2025 Aug 21;7(8):138-44.
Pechlivanidis E, Ginoglou D, Barmpoutis P. Can intangible assets predict future performance? A deep learning approach. International Journal of Accounting & Information Management. 2022 Feb 1;30(1):61-72.
Chang A, Lee TS, Lee HM. Applying sustainable development goals in financial forecasting using machine learning techniques. Corporate social responsibility and environmental management. 2024 May;31(3):2277-89.
Kotios D, Makridis G, Fatouros G, Kyriazis D. Deep learning enhancing banking services: a hybrid transaction classification and cash flow prediction approach. Journal of big Data. 2022 Oct 2;9(1):100.
Sharipov B, Scientist D. MACHINE LEARNING MODELS FOR CORPORATE REVENUE PREDICTION. International Journal Of Engineering Technology Research & Management (IJETRM). 2024;8(02):189-97.
Lei X, Mohamad UH, Sarlan A, Shutaywi M, Daradkeh YI, Mohammed HO. Development of an intelligent information system for financial analysis depend on supervised machine learning algorithms. Information Processing & Management. 2022 Sep 1;59(5):103036.
Lee CW, Fu MW, Wang CC, Azis MI. Evaluating machine learning algorithms for financial fraud detection: Insights from Indonesia. Mathematics. 2025 Feb 12;13(4):600.
Bakumenko A, Elragal A. Detecting anomalies in financial data using machine learning algorithms. Systems. 2022 Aug 25;10(5):130.
Alonso Robisco, A., & Carbo Martinez, J. M. (2022). Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction. Financial Innovation, 8(1), 70.
Zhao Z, Bai T. Financial fraud detection and prediction in listed companies using SMOTE and machine learning algorithms. Entropy. 2022 Aug 19;24(8):1157.
Balogun ED, Ogunsola KO, Ogunmokun AS. Developing an advanced predictive model for financial planning and analysis using machine learning. IRE journals. 2022 May;5(11):320-8.
Mahmood F, Shahzad U, Nazakat AL, Ahmed Z, Rjoub H, Wong WK. The nexus between cash conversion cycle, working capital finance, and firm performance: Evidence From Novel Machine Learning Approaches. Annals of Financial Economics. 2022 Jun 21;17(02):2250014.
Kureljusic M, Karger E. Forecasting in financial accounting with artificial intelligence–A systematic literature review and future research agenda. Journal of Applied Accounting Research. 2024 Jan 18;25(1):81-104.
Swarnali SH, Uddin HM. Predictive Cash Flow Forecasting Using Deep Learning and ERP Transaction Data in Mid-Market Manufacturing Firms. International Journal of Scientific Interdisciplinary Research. 2022 Jun 30;1(01):316-34.
Ziya07. Accounting Data for Financial Management [Internet]. Kaggle; 2024 [cited 2026 Jun 25]. Available from: https://www.kaggle.com/datasets/ziya07/accounting-data-for-financial-management
Gao H, Kou G, Liang H, Zhang H, Chao X, Li CC, Dong Y. Machine learning in business and finance: a literature review and research opportunities. Financial Innovation. 2024 Sep 19;10(1):86.
Bello OA, Folorunso A, Ejiofor OE, Budale FZ, Adebayo K, Babatunde OA. Machine learning approaches for enhancing fraud prevention in financial transactions. International Journal of Management Technology. 2023;10(1):85-108.
Xiuguo W, Shengyong D. An analysis on financial statement fraud detection for Chinese listed companies using deep learning. Ieee Access. 2022 Feb 22;10:22516-32.
Nielsen S. Management accounting and the concepts of exploratory data analysis and unsupervised machine learning: a literature study and future directions. Journal of Accounting & Organizational Change. 2022 Oct 4;18(5):811-53.
Mushtaq R, Gull AA, Shahab Y, Derouiche I. Do financial performance indicators predict 10-K text sentiments? An application of artificial intelligence. Research in International Business and Finance. 2022 Oct 1;61:101679.
Japinye AO. Integrating machine learning in anti-money laundering through crypto: a comprehensive performance review. European Journal of Accounting, Auditing and Finance Research. 2024;12(4):54-80.
Subedi M. Principles based accounting standards, audit fees and going concern: Evidence using advanced machine learning. International Journal of Accounting & Information Management. 2024 Mar 13;32(2):308-44.
Lokanan ME. Predicting mobile money transaction fraud using machine learning algorithms. Applied AI Letters. 2023 Apr;4(2):e85.
Özlem Ş, Tan OF. Predicting cash holdings using supervised machine learning algorithms. Financial Innovation. 2022 May 18;8(1):44.
Dong H, Liu R, Tham AW. Accuracy comparison between five machine learning algorithms for financial risk evaluation. Journal of Risk and Financial Management. 2024 Jan 29;17(2):50.
Hamdi M, Mestiri S, Arbi A. Artificial intelligence techniques for bankruptcy prediction of tunisian companies: An application of machine learning and deep learning-based models. Journal of Risk and Financial Management. 2024 Mar 22;17(4):132.
Iqbal A, Amin R. Time series forecasting and anomaly detection using deep learning. Computers & Chemical Engineering. 2024 Mar 1;182:108560.
Ayitey Junior M, Appiahene P, Appiah O, Bombie CN. Forex market forecasting using machine learning: Systematic Literature Review and meta-analysis. Journal of Big Data. 2023 Jan 28;10(1):9.
Chen Y, Wu Z. Financial fraud detection of listed companies in China: A machine learning approach. Sustainability. 2022 Dec 21;15(1):105.
Lokanan ME, Maddhesia V. Supply chain fraud prediction with machine learning and artificial intelligence. International Journal of Production Research. 2025 Jan 2;63(1):286-313.
Murad MH. Machine Learning–Based Consumer Behavior Prediction Models for E-Commerce Platforms: Enhancing Digital Financial Inclusion and Market Accessibility. ASRC Procedia: Global Perspectives in Science and Scholarship. 2025 Apr 29;1(01):2078-118.
Routhu K, Velaga V, Chinta PC, Jha KM. Leveraging machine learning techniques for predictive analysis in merger and acquisition (M&A). Available at SSRN 5102672. 2023 Jul 8.
Chang V, Sivakulasingam S, Wang H, Wong ST, Ganatra MA, Luo J. Credit risk prediction using machine learning and deep learning: A study on credit card customers. Risks. 2024 Nov 4;12(11):174.
Xu X, Xiong F, An Z. Using machine learning to predict corporate fraud: evidence based on the gone framework. Journal of Business Ethics. 2023 Aug;186(1):137-58.
Kamuangu P. A review on financial fraud detection using AI and machine learning. Journal of Economics, Finance and Accounting Studies. 2024 Feb 11;6(1):67-77.
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