Natural Language Processing-Based Layered Reconciliation System for Financial Transaction Analysis
A new system uses Natural Language Processing to automatically reconcile financial transaction records with bank statements, reducing errors and improving accuracy.
Researchers developed a system that uses Natural Language Processing and rule-based techniques to compare bank transaction records with internal operational logs. The system automatically retrieves and classifies transaction records, identifies linguistic patterns, and assigns accounting codes. This leads to reduced manual inspection, faster error detection, and improved overall data accuracy.
Abstract
With the widespread adoption of digital payment systems, the volume and diversity of financial transaction data have increased significantly. For payment institutions and electronic money companies in particular, the cross-verification of internal transaction logs with bank statements has become a critical requirement for ensuring financial security, accounting accuracy, and auditability. However, in practice, inconsistencies often occur between bank-side and firm-side records due to system interruptions, service errors, or manually entered transactions. This study presents a financial data reconciliation system based on Natural Language Processing (NLP) and rule-based analytical techniques, designed to detect inconsistencies by comparing bank transaction records with internal operational logs. The system, developed by Elekse, automatically retrieves millions of transaction records from multiple banks via the Finekra platform and classifies them by transaction type using key attributes such as description, date, amount, and IBAN. Throughout this process, NLP techniques are used to identify linguistic patterns, extract meaningful expressions, and assign the appropriate accounting codes through predefined rules, enabling the automatic reconciliation of records. As a result, the need for manual inspection is reduced, error detection is accelerated, and overall data accuracy is improved.
References
- 1.S. Gunasekaran, “AI-Powered Transaction Reconciliation: A Reinforcement Learning Approach,” International Research Journal of Engineering and Technology, vol. 11, no. 6, pp. 17-29, 2024-06, 2024.
- 2.M. Lecci, and T. Hanne, “Accounting Support Using Artificial Intelligence for Bank Statement Classification,” Computers, vol. 14, no. 5, pp. 193, 2025.
- 3.P. Bruno, U. Jeenah, A. Gandhi, and I. Gancho, Global payments in 2024: Simpler interfaces, complex reality, McKinsey & Company, 2024.
- 4.A. P. Alessandro, B. Jess, M. Bazzi, K. Kennedy, M. Arderne, D. Rodrigues, and M. Lotz, “Categorising SME Bank Transactions with Machine Learning and Synthetic Data Generation,” arXiv preprint arXiv:2508.05425, 2025.
- 5.J. K. Vemuri, “Cloud-based reconciliation systems in investment banking: A comprehensive analysis of architecture, implementation, and performance metrics,” International Research Journal of Modernization in Engineering, Technology and Science, vol. 7, no. 2, pp. 1-16, 2025-02, 2025.
- 6.S. O. Ikponmwoba, O. K. Chima, O. J. Ezeilo, B. M. Ojonugwa, A. Ochefu, and M. O. Adesuyi, “Conceptual Framework for Improving Bank Reconciliation Accuracy Using Intelligent Audit Controls,” 2020.
- 7.O. Olasoji, E. F. Iziduh, and O. O. Adeyelu, “A Predictive Modeling Approach for Managing Accounts Payable Workflow Efficiency and Ledger Reconciliation Accuracy,” Shodhshauryam, International Scientific Refereed Research Journal, vol. 6, no. 4, pp. 106-120, 2023.
- 8.P. S. R. P. Muntala, “Enhancing Financial Close with ML: Oracle Fusion Cloud Financials Case Study,” International Journal of AI, BigData, Computational and Management Studies, vol. 3, no. 3, pp. 62-69, 2022.
- 9.B. A. Makayasa, M. U. Siregar, B. Sugiantoro, and A. Fatwanto, “Comparison of Classification Algorithm and Language Model in Accounting Financial Transaction Record: A Natural Language Processing Approach,” International Journal on Advanced Science, Engineering & Information Technology, vol. 14, no. 3, 2024.
- 10.E. Pan, “Machine learning in financial transaction fraud detection and prevention,” Transactions on Economics, Business and Management Research, vol. 5, pp. 243-249, 2024.
- 11.R. K. Inampudi, D. Kondaveeti, and T. Pichaimani, “Optimizing Payment Reconciliation Using Machine Learning: Automating Transaction Matching and Dispute Resolution in Financial Systems,” Journal of Artificial Intelligence Research, vol. 3, no. 1, pp. 273-317, 2023.
Hazırlar, D., Avcı, Ö., Tekir, M., Doğan, B. (2025). Natural Language Processing-Based Layered Reconciliation System for Financial Transaction Analysis. *Orclever Proceedings of Research and Development*, 7(1), 30-42. https://doi.org/10.56038/oprd.v7i1.695
Bibliographic Info
Indexing & License
More from Orclever Proceedings of Research and Development
Single-Bath Dyeing of Blends of Cotton Fibers with New Generation Polyacrylonitrile Fibers with Reactive Dye in Line with the Target of Sustainable Production
Yıldıray Fatih Dilsiz, Seda Keskin, Rıza Atav
2025 · Vol 7 · Issue 1
The Green Step Upper: A Novel Sustainable Bonding Method Replacing Solvent-Based Adhesives in Footwear Upper Assembly
Baris Bekiroglu, Mustafa Yener
2025 · Vol 7 · Issue 1
Innovative Technological Strategies to Enhance Bioavailability in Germinated Grains
Ebru Bozkurt Abdik
2025 · Vol 7 · Issue 1
Graph-Based Customer Segmentation with GraphSAGE on a Customer–Vehicle Bipartite Network
Abdullah Sezdi, Metin Bilgin
2025 · Vol 7 · Issue 1
Development of a Secure Structural Component to Mitigate Environmental Contamination at Ports During the Transfer of Granular Materials in Global Maritime Logistics: Ecological Port Loading Bunker
Özge Güler, M.Cemal Çakır
2025 · Vol 7 · Issue 1
The Development of a Platform as a Service for Game Key Distribution
Deniz Tahmaz, Yasin Başer, Esma Güneş
2025 · Vol 7 · Issue 1