Optimization of Transaction Processing Workflows in Finance through AI-Powered Decision Engines
Keywords:
AI in finance, transaction optimization, decision engines, financial workflows, automation, fraud detection, real-time processingAbstract
In the rapidly evolving landscape of financial services, transaction processing represents a critical function where efficiency, accuracy, and security are paramount. This paper investigates the integration of AI-powered decision engines to optimize transaction workflows, reduce operational latency, and enhance fraud detection. By synthesizing prior literature and analyzing use cases across retail banking and capital markets, the study reveals measurable improvements in throughput, error reduction, and real-time risk assessment. The research underscores the potential of AI to revolutionize legacy financial systems and provides strategic insights into implementation challenges and governance.
References
Arner, Douglas W., Janos Barberis, and Ross P. Buckley. "Fintech, Regtech, and the Reconceptualization of Financial Regulation." Northwestern Journal of International Law & Business, vol. 37, no. 3, 2017, pp. 371–414.
Biru, S. (2025). Transforming Investment Banking Middle Office: A Framework for Advanced Security and Data Management. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(1), 608–616. https://doi.org/10.32628/CSEIT25111268
Bach, Thomas, and Johannes Waser. "Real-Time Data Analytics for Financial Payments." ACM Transactions on Management Information Systems, vol. 11, no. 1, 2020, pp. 1–22.
Brynjolfsson, Erik, and Andrew McAfee. Machine, Platform, Crowd: Harnessing Our Digital Future. W. W. Norton & Company, 2017.
Biru, S. (2025). Intelligent Automation in Banking Operations: Impact Analysis on Renewable Energy Investment Assessment. International Journal of Computer Engineering and Technology (IJCET), 16(1), 673–687. https://doi.org/10.34218/IJCET_16_01_056
Chen, Yuzhe, Xiaoming Zhang, and Liang Liu. "Reinforcement Learning for Blockchain-Based Transaction Routing." IEEE Transactions on Network and Service Management, vol. 18, no. 3, 2021, pp. 2914–2925.
Kumar, Ashish, and Prerna Malhotra. "Neural Network Approach for Detecting Fraudulent Financial Transactions." Journal of Risk Finance, vol. 20, no. 2, 2019, pp. 162–176.
Zhou, Ying, and Haoyu Gu. "Rule-Based versus AI-Driven Decision Systems in Retail Banking Workflows: A Comparative Study." Journal of Financial Innovation, vol. 9, no. 4, 2023, pp. 201–218.
Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016.
Biru, S. (2025). AI-Powered Deduplication in Investment Banking Middle Office. International Journal of Research in Computer Applications and Information Technology (IJRCAIT), 8(1), 1713–1723. https://doi.org/10.34218/IJRCAIT_08_01_125
Buehler, Kevin, Alexander Grant, and Christian Weber. "The Risk-Based Approach to AI Governance in Financial Services." McKinsey & Company, 2019.
D’Acunto, Francesco, Nagpurnanand Prabhala, and Alberto G. Rossi. "Human Advice and Machine Learning: Evidence from Robo-Financial Planning." Review of Financial Studies, vol. 34, no. 11, 2021, pp. 5314–5351.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep Learning." Nature, vol. 521, no. 7553, 2015, pp. 436–444.
Bazarbash, M. "Fintech in Financial Inclusion: Machine Learning Applications in Credit Risk Modeling." IMF Working Paper WP/19/109, International Monetary Fund, 2019.
Khandani, Amir E., Adlar J. Kim, and Andrew W. Lo. "Consumer Credit-Risk Models via Machine-Learning Algorithms." Journal of Banking & Finance, vol. 34, no. 11, 2010, pp. 2767–2787.
Treleaven, Philip, Michael Galas, and Vito Lalchand. "Algorithmic Trading Review." Communications of the ACM, vol. 56, no. 11, 2013, pp. 76–85.
Yao, Quanming, Mengshuo Wang, Yu Chen, Wei Dai, Qiwei Yi, and Qiang Yang. "Taking Human Out of Learning Applications: A Survey on Automated Machine Learning (AutoML)." arXiv preprint, arXiv:1810.13306, 2018.
