Natural Language Processing for Intelligent Automation of Financial Documents and Banking Operations

Authors

  • Sasidhar Reddy Mondeddula

Keywords:

Banking Automation, Compliance Automation, Customer Onboarding, Document Intelligence, Financial Document Processing, Human-in-the-Loop, Intelligent Automation, Know Your Customer (KYC), Machine Learning, Natural Language Processing (NLP), Operational Analytics, Risk Management, Unstructured Data Processing

Abstract

Applications of Natural Language Processing (NLP) in Intelligent Automation of Financial Documents and Operational Activities of Banks Natural Language Processing (NLP) techniques ranging from parsing to translation, when leveraged properly, can introduce the next level of automation to the process flows in Banking and Financial Services (BFS). Such intelligent automation reduces operating costs for banks and other financial services while enhancing the customer experience by providing 24/7 basic services. However, banking operations demand that such automation is precise and failure rates near zero as the consequences of errors and fraud detection are also high. As a result, while NLP capabilities can bring about intelligent automation, the risk of error makes it essential to formulate the right automation strategy that may require human intervention in the loop. Natural Language Processing enables the automation of several operational activities like customer onboarding, KYC (Know Your Customer) workflows, compliance checks, and access provisioning in a bank. These areas demand the handling of several types of structured and unstructured documents associated with the customer. These pose various challenges for automation based on the analytics capabilities, data quality, and risk appetite of the organizations. Customers are also migrating towards online services. However, BFS fails to recognize a high degree of online self-servicing like other industries, like IT CFCs. Growth opportunities will arise from lightweight, 24/7, automated systems based on human-in-the-loop, machine-learning models.

Downloads

Download data is not yet available.

References

Zheng, S., Cao, W., Xu, W., & Bian, J. (2019). Doc2EDAG: An end-to-end document-level framework for Chinese financial event extraction. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, 337–346.

Mashetty, S. (2022). Enhancing Financial Data Security And Business Resiliency In Housing Finance: Implementing AI-Powered Data Analytics, Deep Learning, And Cloud-Based Neural Networks For Cybersecurity And Risk Management. Migration Letters, 19(6), 1302-1818.

Bao, H., Wang, W., Dong, L., Liu, Q., Mohammed, O. K., Aggarwal, K., Som, S., Piao, S., & Wei, F. (2022). VLMo: Unified vision-language pre-training with mixture-of-modality-experts. Advances in Neural Information Processing Systems, 35, 32897–32912.

Chen, Z., Li, S., Smiley, C., Ma, Z., Shah, S., & Wang, W. Y. (2022). ConvFinQA: Exploring the chain of numerical reasoning in conversational finance question answering. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 6279–6292.

Ghosh, S., & Naskar, S. K. (2022). Detecting context-based in-claim numerals in financial earnings conference calls. International Journal of Information Technology, 14(5), 2559–2566.

Huang, Y., Lv, T., Cui, L., Lu, Y., & Wei, F. (2022). LayoutLMv3: Pre-training for document AI with unified text and image masking. Proceedings of the 30th ACM International Conference on Multimedia, 4083–4091.

Kim, G., Hong, T., Yim, M., Nam, J., Park, J., Yim, J., Hwang, W., Yun, S., Han, D., & Park, S. (2022). OCR-free document understanding transformer. Computer Vision – ECCV 2022, 498–517.

Li, J., Xu, Y., Cui, L., & Wei, F. (2022). MarkupLM: Pre-training of text and markup language for visually rich document understanding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 6078–6087.

Inala, R. (2022). Cross-Domain MDM Integration Using AI-Driven Data Governance: A Case Study In Financial Technology Architecture. Migration Letters, 19(2), 280-304.

Appalaraju, S., Jasani, B., Kota, B. U., Xie, Y., & Manmatha, R. (2021). DocFormer: End-to-end transformer for document understanding. Proceedings of the IEEE/CVF International Conference on Computer Vision, 973–983.

Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295-312.

Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.-H., Routledge, B., & Wang, W. Y. (2021). FinQA: A dataset of numerical reasoning over financial data. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 3697–3711.

Garncarek, Ł., Powalski, R., Stanisławek, T., Topolski, B., Halama, P., Turski, M., & Graliński, F. (2021). LAMBERT: Layout-aware language modeling for information extraction. Document Analysis and Recognition – ICDAR 2021, 532–547.

Reddy, V. A. R. (2022). Data-Driven Healthcare Operations: Architecting Unified Member, Provider, and Claims Intelligence Platforms. International Journal of Science, Research and Technology, 5(5), 8511-8521.

Hamdi, A., Carel, E., Joseph, A., Coustaty, M., & Doucet, A. (2021). Information extraction from invoices. Document Analysis and Recognition – ICDAR 2021, 699–714.

Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.

Mathew, M., Karatzas, D., & Jawahar, C. V. (2021). DocVQA: A dataset for VQA on document images. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2199–2208.

Xu, Y., Xu, Y., Lv, T., Cui, L., Wei, F., Wang, G., Lu, Y., Florencio, D., Zhang, C., Che, W., Zhang, M., & Zhou, L. (2021). LayoutLMv2: Multi-modal pre-training for visually-rich document understanding. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2579–2591.

Bao, H., Dong, L., Wei, F., Wang, W., Yang, N., Liu, X., Wang, Y., Gao, J., Piao, S., Zhou, M., & Hon, H.-W. (2020). UniLMv2: Pseudo-masked language models for unified language model pre-training. Proceedings of the 37th International Conference on Machine Learning, 119, 642–652.

Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020). Don’t stop pretraining: Adapt language models to domains and tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 8342–8360.

DAVULURI, P. N. (2022). Cloud-Native Data Platform Modernization for Regulatory Compliance in Global Banking. Kurdish Studies.

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020). BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7871–7880.

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.

Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., Davison, J., Shleifer, S., von Platen, P., Ma, C., Jernite, Y., Plu, J., Xu, C., Le Scao, T., Gugger, S., Drame, M., Lhoest, Q., & Rush, A. M. (2020). Transformers: State-of-the-art natural language processing. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 38–45.

Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., & Zhou, M. (2020). LayoutLM: Pre-training of text and layout for document image understanding. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 1192–1200.

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 4171–4186.

Ein-Dor, L., Gera, A., Toledo-Ronen, O., Halfon, A., Sznajder, B., Dankin, L., Bilu, Y., Katz, Y., & Slonim, N. (2019). Financial event extraction using Wikipedia-based weak supervision. Proceedings of the Second Workshop on Economics and Natural Language Processing, 10–15.

Mangala, N. (2022). Real-Time Data Quality Monitoring and Gating Frameworks in Cloud-Based Data Pipelines. International Journal of Research and Applied Innovations, 5(6), 8197-8219.

Gentzkow, M., Kelly, B., & Taddy, M. (2019). Text as data. Journal of Economic Literature, 57(3), 535–574.

Oral, B., Emekligil, E., Arslan, S., & Eryiğit, G. (2019). Extracting complex relations from banking documents. Proceedings of the Second Workshop on Economics and Natural Language Processing, 1–9.

Mattaparthi, R. (2022). Engineering Predictive Industrial Systems Through IoT-Driven Asset Monitoring and Machine Learning Prognostics. International Journal of Future Innovative Science and Technology (IJFIST), 5(1), 7790.

Palm, R. B., Laws, F., & Winther, O. (2019). Attend, copy, parse end-to-end information extraction from documents. 2019 International Conference on Document Analysis and Recognition, 329–336.

Peddi, R. K. (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21.

Downloads

Published

31.08.2023

How to Cite

Sasidhar Reddy Mondeddula. (2023). Natural Language Processing for Intelligent Automation of Financial Documents and Banking Operations. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 605–612. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/8527

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.