Implementing Gen AI for Increasing Robustness of US Financial and Regulatory System

Satyadhar Joshi

doi:10.55524/ijirem.2024.11.6.19

Abstract

With Gen AI models becoming more evolved, their application in enhancing the robustness of the US Financial System is more viable. Financial risk modeling can take advantage of these development and aid regulatory framework by integrating these novel technologies to make their models more robust. In this work, we have used the latest Gen AI model by Open AI also known as Chat-GPT 4o and 40 mini and Google Gemini Version 2.0 and 1.5 to generate relevant questions from govt websites and measure the accuracy and relevance in checking the the pre trained logistic regression models. We have rated the accuracy of the questions by taking a survey of three Risk Analysts (volunteers) and found that Gen AI is 70-80% accurate in terms of the question for the models it generated. The new and the old model for open ai vs Gemini were compared. We have also documented how different models are sensitive to different prompts as they want to save computational cost and keep the output relevant. These questions generated can be used and integrated in the backend and auto curate the models under analyst supervision. We proposed a full stack framework as an end to end solution to address issues related to privacy and ethical considerations limiting exposure of property data and models. We have used all-MiniLM-L6-v2 as the bridging APIs for creating variants of the queries.

Keywords

Gen AI, US Financial System, Risk and Regulatory Modeling, Robustness and Integrity

References

A. Balakrishnan, "Leveraging artificial intelligence for enhancing regulatory compliance in the financial sector," Social Science Research Network, 2024. Available from: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4842699

O.A. Bello, "Machine learning algorithms for credit risk assessment: An economic and financial analysis," International Journal of Management Technology, vol. 10, no. 1, Dec. 2023. Available from: https://doi.org/10.37745/ijmt.2013

C. Challoumis, "What challenges does AI present to the cycle of money and economocracy?" SSRN Electronic Journal, 2024. Available from: https://doi.org/10.2139/ssrn.4975035

X. Cheng et al., "Combating emerging financial risks in the big data era: A perspective review," Fundamental Research, vol. 1, no. 5, pp. 595–606, Sep. 2021. Available from: https://doi.org/10.1016/j.fmre.2021.08.017

W. of Conferences, "XVI international scientific conference," Tallinn, Estonia, 17-18 Oct. 2024. Available from: https://doi.org/10.1109/APEIE59731.2023

M. Doumpos et al., "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, vol. 306, no. 1, pp. 1–16, Apr. 2023. Available from: https://doi.org/10.1016/j.ejor.2022.04.027

I. Goldstein et al., "Big data in finance," The Review of Financial Studies, vol. 34, no. 7, pp. 3213–3225, Jun. 2021. Available from: https://doi.org/10.1093/rfs/hhab038

H. Herrmann and B. Masawi, "Three and a half decades of artificial intelligence in banking, financial services, and insurance: A systematic evolutionary review," Strategic Change, vol. 31, no. 6, pp. 549–569, 2022. Available from: https://doi.org/10.1002/jsc.2525

D.K. Nguyen et al., "Big data, artificial intelligence and machine learning: A transformative symbiosis in favour of financial technology," European Financial Management, vol. 29, no. 2, pp. 517–548, Mar. 2023. Available from: https://doi.org/10.1111/eufm.12365

H. Sadok et al., "Artificial intelligence and bank credit analysis: A review," Cogent Economics & Finance, vol. 10, no. 1, pp. 2023262, Dec. 2022. Available from: https://doi.org/10.1080/23322039.2021.2023262

Y. Wang et al., "Can fintech improve the efficiency of commercial banks?—An analysis based on big data," Research in International Business and Finance, vol. 55, p. 101338, Jan. 2021. Available from: https://doi.org/10.1016/j.ribaf.2020.101338

K. Yu et al., "A deep reinforcement learning approach to enhancing liquidity in the U.S. municipal bond market: An intelligent agent-based trading system," International Journal of Innovative Research in Engineering and Management, vol. 11, no. 6, pp. 43–54, Dec. 2024. Available from: https://doi.org/10.5281/zenodo.14184756

Hugging Face, "all-MiniLM-L6-v2: A lightweight model for generating sentence embeddings," Hugging Face, Oct. 2020. DOI: 10.5281/zenodo.4633956. Available from: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2

Cites this article as

S. Joshi "Implementing Gen AI for Increasing Robustness of US Financial and Regulatory System", International Journal of Innovative Research in Engineering & Management (IJIREM), Vol-11, Issue-6, Page No-175-179, 2024. Available from: https://doi.org/10.55524/ijirem.2024.11.6.19