With the proliferation of digital financial services and digital transactional documents, data volumes are vastly increasing, including invoices, receipts, bank statements, and balance sheets. The document has garnered massive interest and a keen interest in handling Information extraction from these documents. For such documents, manual data extraction is time-consuming and prone to human error as the documents come in many formats. This paper covers techniques, tools, and technology in the case of extracting tables from financial and transactional documents, specifically in the case of vertical tables and in the presence of mixed-type data representations. Table extraction means extracting tabular data from a readable image schema document and transforming it into a structured format (CSV / JSON). The paper discusses other extraction methods, such as rule-based extraction, optical character recognition (OCR), and machine learning models. The book also covers some use cases from industry banking, e-commerce, or accounting, amongst other industries. The paper then discusses ethical and legal implications such as GDPR, HIPAA, compliance with data privacy laws, and how it should be transparent and fair for AI systems. Last but not least, the future trends of table extraction, including integration of generative AI and large language models (LLMs) and robotic process automation (RPA), as well as real-time data extraction, are discussed. This paper presents the growing demand for advanced extraction technologies to increase financial document processing accuracy, efficiency, and scalability.

Table extraction, financial documents, Machine learning, Optical character recognition (OCR), Automation.

Abdullah, A. H., Abidin, N. L. Z., & Ali, M. (2015). Analysis of students’ errors in solving Higher Order Thinking Skills (HOTS) problems for the topic of fraction. Asian Social Science, 11(21), 133-142.

Appelbaum, D. A., Kogan, A., & Vasarhelyi, M. A. (2018). Analytical procedures in external auditing: A comprehensive literature survey and framework for external audit analytics. Journal of Accounting Literature, 40(1), 83-101.

Berenguel Centeno, A. (2019). Analysis of background textures in banknotes and identity documents for counterfeit detection.

Bettini, L. (2016). Implementing domain-specific languages with Xtext and Xtend. Packt Publishing Ltd.

Bouillon, M., Ingold, R., & Liwicki, M. (2019). Grayification: a meaningful grayscale conversion to improve handwritten historical documents analysis. Pattern Recognition Letters, 121, 46-51.

Carruthers, B. G., & Lamoreaux, N. R. (2016). Regulatory races: the effects of jurisdictional competition on regulatory standards. Journal of Economic Literature, 54(1), 52-97.

Chen, G., Douch, C. I., & Zhang, M. (2016). Accuracy-based learning classifier systems for multistep reinforcement learning: a fuzzy logic approach to handling continuous inputs and learning continuous actions. IEEE Transactions on Evolutionary Computation, 20(6), 953-971.

Chen, Z., Van Khoa, L. D., Teoh, E. N., Nazir, A., Karuppiah, E. K., & Lam, K. S. (2018). Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowledge and Information Systems, 57, 245-285.

Chylek, L. A., Harris, L. A., Faeder, J. R., & Hlavacek, W. S. (2015). Modeling for (physical) biologists: an introduction to the rule-based approach. Physical biology, 12(4), 045007.

Dixit, R., & Ravindranath, K. (2018). Encryption techniques & access control models for data security: A survey. Int. J. Eng. Technol, 7(1.5), 107-110.

Elger, P., & Shanaghy, E. (2020). AI as a Service: Serverless machine learning with AWS. Manning.

ETCHI, P. E., & TARKPAH, S. F. (2019). HOW HAS TECHNOLOGY INFLUENCED FINANCIAL REPORTING PROCESS IN ACCOUNTING FIRMS?: An analysis of two international audit firms in Liberia.

Gatos, B., Pratikakis, I., & Perantonis, S. J. (2006). Adaptive degraded document image binarization. Pattern Recognition, 39(3), 317–327. https://doi.org/10.1016/j.patcog.2005.05.009

Goodrich, M. T., Kornaropoulos, E. M., Mitzenmacher, M., & Tamassia, R. (2017, April). Auditable data structures. In 2017 IEEE European Symposium on Security and Privacy (EuroS&P) (pp. 285-300). IEEE.

Hamad, K., & Kaya, M. (2016). A detailed analysis of optical character recognition technology. International Journal of Applied Mathematics Electronics and Computers, (Special Issue-1), 244-249.

Hastings, R. M. (2017). Planning Cloud-Based Disaster Recovery for Digital Assets.

Islam, R. U., Hossain, M. S., & Andersson, K. (2020). A deep learning inspired belief rule-based expert system. IEEE Access, 8, 190637-190651.

Juneau, J. (2017). Unicode, Internationalization, and Currency Codes. In Java 9 Recipes: A Problem-Solution Approach (pp. 285-304). Berkeley, CA: Apress.

Kluegl, P., Toepfer, M., Beck, P. D., Fette, G., & Puppe, F. (2016). UIMA Ruta: Rapid development of rule-based information extraction applications. Natural Language Engineering, 22(1), 1-40.

Kumar, A. (2019). The convergence of predictive analytics in driving business intelligence and enhancing DevOps efficiency. International Journal of Computational Engineering and Management, 6(6), 118-142. Retrieved from https://ijcem.in/wp-content/uploads/THE-CONVERGENCE-OF-PREDICTIVE-ANALYTICS-IN-DRIVING-BUSINESS-INTELLIGENCE-AND-ENHANCING-DEVOPS-EFFICIENCY.pdf

Majumder, M. R., Mahmud, B. U., Jahan, B., & Alam, M. (2019, December). Offline optical character recognition (OCR) method: An effective method for scanned documents. In 2019 22nd International Conference on Computer and Information Technology (ICCIT) (pp. 1-5). IEEE.

Morsfield, S. G., Yang, S. Y., & Yount, S. (2016). A critical and empirical examination of currently-used financial data collection processes and standards.

Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015). Deep learning applications and challenges in big data analytics. Journal of big data, 2, 1-21.

Nyati, S. (2018). Revolutionizing LTL carrier operations: A comprehensive analysis of an algorithm-driven pickup and delivery dispatching solution. International Journal of Science and Research (IJSR), 7(2), 1659-1666. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203183637

Nyati, S. (2018). Transforming telematics in fleet management: Innovations in asset tracking, efficiency, and communication. International Journal of Science and Research (IJSR), 7(10), 1804-1810. Retrieved from https://www.ijsr.net/getabstract.php?paperid=SR24203184230

Pall, G. K., Bridge, A. J., Gray, J., & Skitmore, M. (2019). Causes of delay in power transmission projects: An empirical study. Energies, 13(1), 17.

Pozza, M., Rao, A., Flinck, H., & Tarkoma, S. (2018). Network-in-a-box: A survey about on-demand flexible networks. IEEE Communications Surveys & Tutorials, 20(3), 2407-2428.

Raju, R. K. (2017). Dynamic memory inference network for natural language inference. International Journal of Science and Research (IJSR), 6(2). https://www.ijsr.net/archive/v6i2/SR24926091431.pdf

Renes, S. (2020). When Debit= Credit, The Balance Constraint in Bookkeeping, Its Causes and Consequences for Accounting. The Balance Constraint in Bookkeeping, Its Causes and Consequences for Accounting (June 11, 2020).

Rule, G. (2015). Understanding the central bank balance sheet.

Salgueiro, R. U. B. (2020). The Impact of Microsoft Power Platform in Streamlining End-to-End Business Solutions: Internship Report at Microsoft Portugal, Specialist Team Unit (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

Scatiggio, V. (2020). Tackling the issue of bias in artificial intelligence to design ai-driven fair and inclusive service systems. How human biases are breaching into ai algorithms, with severe impacts on individuals and societies, and what designers can do to face this phenomenon and change for the better.

Schreiber, S., Agne, S., Wolf, I., Dengel, A., & Ahmed, S. (2017). Deepdesrt: Deep learning for detection and structure recognition of tables in document images. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 1162–1167. https://doi.org/10.1109/ICDAR.2017.191

Singh, V., Murarka, Y., Jaiswal, A., & Kanani, P. (2020). Detection and classification of arrhythmia. International Journal of Grid and Distributed Computing, 13(6). http://sersc.org/journals/index.php/IJGDC/article/view/9128

Singh, V., Oza, M., Vaghela, H., & Kanani, P. (2019, March). Auto-encoding progressive generative adversarial networks for 3D multi-object scenes. In 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT) (pp. 481-485). IEEE. https://arxiv.org/pdf/1903.03477

Smith, J., Benedikt, M., Nikolic, M., & Shaikhha, A. (2020). Scalable querying of nested data. arXiv preprint arXiv:2011.06381.

Somasundaram, P. (2018). Efficient File-Based Data Ingestion for Cloud Analytics: A Framework for Extracting and Converting Non-Traditional Data Sources. International Journal of Science and Research, 13(2), 2223-2227.

Sum, R. M., & Nordin, N. (2018). Decision making biases in insurance purchasing. Journal of advanced research in social and behavioural sciences, 10(2), 165-179.

Tamraparani, V. (2020). Automating Invoice Processing in Fund Management: Insights from RPA and Data Integration Techniques. Available at SSRN 5117121.

Tensmeyer, C., & Martinez, T. (2017). Document image binarization with fully convolutional neural networks. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 99–104. https://doi.org/10.1109/ICDAR.2017.27

Tikkinen-Piri, C., Rohunen, A., & Markkula, J. (2018). EU General Data Protection Regulation: Changes and implications for personal data collecting companies. Computer Law & Security Review, 34(1), 134-153.

Turban, E., Whiteside, J., King, D., Outland, J., Turban, E., Whiteside, J., ... & Outland, J. (2017). Electronic Commerce Payment Systems and Order Fulfillment. Introduction to Electronic Commerce and Social Commerce, 331-380.

Yang, Z., Ce, L., & Lian, L. (2017). Electricity price forecasting by a hybrid model, combining wavelet transform, ARMA and kernel-based extreme learning machine methods. Applied Energy, 190, 291-305.

Yanisky-Ravid, S., & Hallisey, S. (2018). ‘Equality and Privacy by Design’: Ensuring Artificial Intelligence (AI) Is Properly Trained & Fed: A New Model of AI Data Transparency & Certification As Safe Harbor Procedures. Available at SSRN 3278490.

Zainal, R., Md Som, A., & Mohamed, N. (2017). A review on computer technology applications in fraud detection and prevention. Management & Accounting Review (MAR), 16(2), 59-72.

Zhang, S., & Balog, K. (2020). Web table extraction, retrieval, and augmentation: A survey. ACM Transactions on Intelligent Systems and Technology (TIST), 11(2), 1-35.

Zhong, X., Tang, J., & Yepes, A. J. (2019). PubLayNet: Largest Dataset Ever for Document Layout Analysis. Proceedings of the International Conference on Document Analysis and Recognition (ICDAR). https://doi.org/10.1109/ICDAR.2019.00101