Secure Agentic Feature Extraction for Financial Documents
Keywords:
Edge Cloud Ecosystem, Secure Feature Extraction, Financial Document Privacy, Generative AI Feature Synthesis, Agentic AI Supervision, Privacy Preserving Machine Learning, Edge Layer Intelligence, Cloud Layer Analytics, Decentralized AI Architecture, Sensitive Data Protection, Semantic Feature Enrichment, Downstream Task Accuracy, Synthetic Feature Generation, Private Label AI Models, Zero Data Leakage Design, Secure AI Pipelines, Confidential Document Processing, Trustworthy AI Systems, Edge AI Security, Federated Intelligence.Abstract
Ensuring security and privacy during the feature extraction of financial documents is of paramount importance. Traditional concerns around data loss during this process can be naturally mitigated by performing feature extraction at the edge layer of an edge-cloud ecosystem. Recent advances in Generative and Agentic AI create new opportunities for generating task-relevant features without sharing or exposing sensitive underlying data. We refer to this capability as Generative and Agentic AI-enhanced feature extraction — an approach that leverages the agentic capabilities of Generative AI to supervise the feature extraction process while also producing additional semantic representations that improve downstream task accuracy. This framework extends naturally across both edge-layer and cloud-layer processing, and security and privacy requirements at the feature-extraction stage are satisfied by applying standard security mechanisms, without altering the core principles or operation of the extraction process itself.
Generative AI is an emerging technology with wide-ranging applications across domains. Its recent ability to generate new data and labels from a small set of samples — without sharing or leaking private details from the original data — introduces an important additional dimension: it not only conceals the origin of the samples but also safeguards the sensitive nature of the underlying data. In this way, Generative AI reinforces the benefits of decentralization by further reducing reliance on central proxy-service providers within edge and cloud ecosystems. Rather than consuming and storing all edge- and cloud-layer data, such providers can instead focus on supplying the infrastructure needed to empower other users in the ecosystem with the private-label Generative AI capabilities required for their specific workloads.
References
1. 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, 993–1003.
2. 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.
3. Xu, Y., Lv, T., Cui, L., Wang, G., Lu, Y., Florencio, D., Zhang, C., & Wei, F. (2021). LayoutXLM: Multimodal pre-training for multilingual 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.
4. 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.
5. Li, Y., Qian, Y., Yu, Y., Qin, X., Zhang, C., Liu, Y., Yao, K., Han, J., Liu, J., & Ding, E. (2021). StrucTexT: Structured text understanding with multi-modal transformers. Proceedings of the 29th ACM International Conference on Multimedia.
6. 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, 2200–2209.
7. 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.
8. Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., & Chua, T.-S. (2021). TAT-QA: A question answering benchmark on a hybrid of tabular and textual content in finance. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 3277–3287.
9. Liou, Y.-T., Chen, C.-C., Huang, H.-H., & Chen, H.-H. (2021). Dynamic graph transformer for implicit tag recognition. Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume.
10. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
11. Kim, G., Hong, T., Yim, M., Park, J., Yim, J., Hwang, W., Yun, S., Han, D., & Park, S. (2022). Donut: Document understanding transformer without OCR. In A. Karlinsky, J. Michaeli, & R. S. A. (Eds.), Computer Vision – ECCV 2022 (pp. 498–517). Springer.
12. Wang, J., Jin, L., & Ding, K. (2022). LiLT: A simple yet effective language-independent layout transformer for structured document understanding. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 7747–7757.
13. 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.
14. Li, J., Xu, Y., Lv, T., Cui, L., Zhang, C., & Wei, F. (2022). DiT: Self-supervised pre-training for document image transformer. Proceedings of the 30th ACM International Conference on Multimedia, 3530–3539.
15. Gu, Z., Meng, C., Wang, K., Lan, J., Wang, W., Gu, M., & Zhang, L. (2022). XYLayoutLM: Towards layout-aware multimodal networks for visually-rich document understanding. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4573–4582.
16. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
17. Loukas, L., Fergadiotis, M., Chalkidis, I., Spyropoulou, E., Malakasiotis, P., Androutsopoulos, I., & Paliouras, G. (2022). FiNER: Financial numeric entity recognition for XBRL tagging. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 4419–4431.
18. Saha, A., Ni, J., Hassanzadeh, O., Gittens, A., Srinivas, K., & Yener, B. (2022). SPOCK at FinCausal 2022: Causal information extraction using span-based and sequence tagging models. Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022, 108–111.
19. Lyu, C., Ji, T., Sun, Q., & Zhou, L. (2022). DCU-Lorcan at FinCausal 2022: Span-based causality extraction from financial documents using pre-trained language models. Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022.
20. Mariko, D., Abi-Akl, H., Trottier, K., & El-Haj, M. (2022). The Financial Causality Extraction Shared Task (FinCausal 2022). Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022.
21. Yan, S. (2022). Disentangled variational topic inference for topic-accurate financial report generation. Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing, 18–24.
22. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
23. Khanna, U., Ghodratnama, S., Mollá, D., & Beheshti, A. (2022). Transformer-based models for long document summarisation in financial domain. Proceedings of the 4th Financial Narrative Processing Workshop @LREC2022.
24. Davis, B., Morse, B., Price, B., Tensmeyer, C., Wigington, C., & Morariu, V. (2022). End-to-end document recognition and understanding with Dessurt. Proceedings of the European Conference on Computer Vision.
25. Tang, Z., Yang, Z., Wang, G., Fang, Y., Liu, Y., Zhu, C., Zeng, M., Zhang, C., & Bansal, M. (2023). Unifying vision, text, and layout for universal document processing. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 19254–19264.
26. Liao, H., RoyChowdhury, A., Li, W., Bansal, A., Zhang, Y., Tu, Z., Satzoda, R. K., Manmatha, R., & Mahadevan, V. (2023). DocTr: Document transformer for structured information extraction in documents. Proceedings of the IEEE/CVF International Conference on Computer Vision, 19584–19594.
27. Tu, Y., Guo, Y., Chen, H., & Tang, J. (2023). LayoutMask: Enhance text-layout interaction in multi-modal pre-training for document understanding. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 15200–15212.
28. 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.
29. Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research.
30. Guo, Y., Xu, Z., & Yang, Y. (2023). Is ChatGPT a financial expert? Evaluating language models on financial natural language processing. Findings of the Association for Computational Linguistics: EMNLP 2023, 815–821.
