Published
2025-02-24
Section
Research Articles
License
The journal adopts the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0), which means that anyone can reuse and redistribute the materials for non-commercial purposes as long as you follow the license terms and the original source is properly cited.
Author(s) shall retain the copyright of their work and grant the Journal/Publisher rights for the first publication with the work concurrently licensed since 2023 Vol.8 No.2.
Under this license, author(s) will allow third parties to download, reuse, reprint, modify, distribute and/or copy the content under the condition that the authors are given credit. No permission is required from the authors or the publisher.
This broad license intends to facilitate free access, as well as the unrestricted use of original works of all types. This ensures that the published work is freely and openly available in perpetuity.
By providing open access, the following benefits are brought about:
- Higher Visibility, Availability and Citations-free and unlimited accessibility of the publication over the internet without any restrictions increases citation of the article.
- Ease of search-publications are easily searchable in search engines and indexing databases.
- Rapid Publication – accepted papers are immediately published online.
- Available for free download immediately after publication at https://esp.as-pub.com/index.php/ESP

Copyright Statement
1.The authors certify that the submitted manuscripts are original works, do not infringe the rights of others, are free from academic misconduct and confidentiality issues, and that there are no disputes over the authorship scheme of the collaborative articles. In case of infringement, academic misconduct and confidentiality issues, as well as disputes over the authorship scheme, all responsibilities will be borne by the authors.
2. The author agrees to grant the Editorial Office of Environment and Social Psychology a licence to use the reproduction right, distribution right, information network dissemination right, performance right, translation right, and compilation right of the submitted manuscript, including the work as a whole, as well as the diagrams, tables, abstracts, and any other parts that can be extracted from the work and used in accordance with the characteristics of the journal. The Editorial Board of Environment and Social Psychology has the right to use and sub-licence the above mentioned works for wide dissemination in print, electronic and online versions, and, in accordance with the characteristics of the periodical, for the period of legal protection of the property right of the copyright in the work, and for the territorial scope of the work throughout the world.
3. The authors are entitled to the copyright of their works under the relevant laws of Singapore, provided that they do not exercise their rights in a manner prejudicial to the interests of the Journal.
About Licence
Environment and Social Psychology is an open access journal and all published work is available under the Creative Commons Licence, Authors shall retain copyright of their work and grant the journal/publisher the right of first publication, and their work shall be licensed under the Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
Under this licence, the author grants permission to third parties to download, reuse, reprint, modify, distribute and/or copy the content with attribution to the author. No permission from the author or publisher is required.
This broad licence is intended to facilitate free access to and unrestricted use of original works of all kinds. This ensures that published works remain free and accessible in perpetuity. Submitted manuscripts, once accepted, are immediately available to the public and permanently accessible free of charge on the journal’s official website (https://esp.as-pub.com/index.php/ESP). Allowing users to read, download, copy, print, search for or link to the full text of the article, or use it for other legal purposes. However, the use of the work must retain the author's signature, be limited to non-commercial purposes, and not be interpretative.
Click to download <Agreement on the Licence for the Use of Copyright on Environmental and Social Psychology>.
How to Cite
Exploring the role of AI in shaping social behavior: An Intersectional psychological perspective on financial risk assessment through digital platforms
Ma Howard
aSSIST University, 136-791, South Korea
Guo Wei
Tsinghua University, Beijing, 100084, China
DOI: https://doi.org/10.59429/esp.v10i1.3375
Keywords: artificial intelligence, financial analytics, digital finance platforms
Abstract
Artificial intelligence analytics in digital finance platforms is important in the modern digital world. AI can conduct analytics quickly and provide the outcomes for the system users to make informed, data-driven conclusions. AI can scan through large datasets and provide meaningful information on social media platforms, historical quantitative transactions, and finances to give critical findings, unlike traditional systems. This review article assessed previous research articles on financial risk evaluation using AI analytics in the finance industry and digital finance platforms. The outcomes outlined the capabilities of financial risks evaluated with the help of AI in digital finance platforms. The key identified risks were credit risks, market risks, operational risks, fraud risks, and compliance risks. The study outlined the key capabilities of AI in shielding firms against such risks through predictive analytics, anomaly detection, sentiment analysis, and credit scoring. The AI systems should be hosted on the cloud to have access to large datasets to give accurate, data-driven conclusions. The identified challenges are algorithm bias, data privacy, regulatory compliance (especially across platforms and countries), and skill gaps in the market. In conclusion, using AI in digital finance platforms has increased the efficiency in making informed decisions for sustainability and strategic growth.
References
[1]. M. Bouchetara, M. Zerouti, and A. R. Zouambi, “LEVERAGING ARTIFICIAL INTELLIGENCE (AI) IN PUBLIC SECTOR FINANCIAL RISK MANAGEMENT: INNOVATIONS, CHALLENGES, AND FUTURE DIRECTIONS,” EDPACS, vol. 69, no. 9, pp. 124–144, Sep. 2024, doi: 10.1080/07366981.2024.2377351.
[2]. L. Vanneschi, D. M. Horn, and M. Castelli, “An artificial intelligence system for predicting customer default in e-commerce,” Expert Syst Appl, vol. 104, no. 8, pp. 1–21, 2018.
[3]. X. Yu and Y. Peng, “Application and Challenges of Artificial Intelligence in Financial Risk Management,” Southern Finance, no. 9, p. 5, 2017.
[4]. F. Butaru, Q. Chen, B. Clark, S. Das, A. W. Lo, and A. Siddique, “Risk and risk management in the credit card industry,” J Bank Financ, vol. 72, pp. 218–239, Nov. 2016, doi: 10.1016/J.JBANKFIN.2016.07.015.
[5]. Y. Ma, H. Liu, G. Zhai, and Z. Huo, “Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model,” Wirel Commun Mob Comput, vol. 2021, no. 1, p. 7819011, Jan. 2021, doi: 10.1155/2021/7819011.
[6]. X. Hua and Y. Huang, “Understanding China’s fintech sector: development, impacts and risks,” European Journal of Finance, vol. 27, no. 4–5, pp. 321–333, 2021, doi: 10.1080/1351847X.2020.1811131.
[7]. P. J. Morgan, “Assessing the Risks Associated with Green Digital Finance and Policies for Coping with Them,” Economics, Law, and Institutions in Asia Pacific, pp. 51–68, 2022, doi: 10.1007/978-981-19-2662-4_3.
[8]. D. A. Zetzsche, W. A. Birdthistle, D. W. Arner, and R. P. Buckley, “Digital Finance Platforms: Toward a New Regulatory Paradigm,” University of Pennsylvania Journal of Business Law, vol. 23, 2020, Accessed: Jan. 10, 2025. [Online]. Available: https://heinonline.org/HOL/Page?handle=hein.journals/upjlel23&id=277&div=&collection=
[9]. M. Zhao, “Research on Financial Risk Assessment Based on Artificial Intelligence,” SHS Web of Conferences, vol. 151, p. 01017, 2022, doi: 10.1051/SHSCONF/202215101017.
[10]. K. Du, F. Xing, R. Mao, and E. Cambria, “Financial Sentiment Analysis: Techniques and Applications,” ACM Comput Surv, vol. 56, no. 9, p. 220, Oct. 2024, doi: 10.1145/3649451/ASSET/C6E561D5-EB82-4402-9140-444D133B3216/ASSETS/GRAPHIC/CSUR-2022-0579-F02.JPG.
[11]. Z. Jiang, D. Xu, and J. Liang, “A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem,” Jun. 2017, Accessed: Jan. 10, 2025. [Online]. Available: http://arxiv.org/abs/1706.10059
[12]. S. W. K. Chan and M. W. C. Chong, “Sentiment analysis in financial texts,” Decis Support Syst, vol. 94, pp. 53–64, Feb. 2017, doi: 10.1016/J.DSS.2016.10.006.
[13]. M. Kraus and S. Feuerriegel, “Decision support from financial disclosures with deep neural networks and transfer learning,” Decis Support Syst, vol. 104, pp. 38–48, Dec. 2017, doi: 10.1016/j.dss.2017.10.001.
[14]. V. Siva and P. Nimmagadda, “AI-Powered Predictive Analytics for Credit Risk Assessment in Finance: Advanced Techniques, Models, and Real-World Applications,” Distributed Learning and Broad Applications in Scientific Research, vol. 5, pp. 251–286, Jul. 2019, doi: 10.3115/v1/d14-1181.
[15]. K. R. Jammalamadaka and S. Itapu, “Responsible AI in automated credit scoring systems,” AI and Ethics 2022 3:2, vol. 3, no. 2, pp. 485–495, Jun. 2022, doi: 10.1007/S43681-022-00175-3.
[16]. M. Faheem, “AI-Driven Risk Assessment Models: Revolutionizing Credit Scoring and Default Prediction,” 2021, Accessed: Jan. 06, 2025. [Online]. Available: https://www.researchgate.net/profile/Muhammad-Ashraf-Faheem/publication/385074551_AI-Driven_Risk_Assessment_Models_Revolutionizing_Credit_Scoring_and_Default_Prediction/links/6713e02e035917754c09413e/AI-Driven-Risk-Assessment-Models-Revolutionizing-Credit-Scoring-and-Default-Prediction.pdf
[17]. V. Siva and P. Nimmagadda, “AI-Powered Predictive Analytics for Credit Risk Assessment in Finance: Advanced Techniques, Models, and Real-World Applications,” Distributed Learning and Broad Applications in Scientific Research, vol. 5, pp. 251–286, Jul. 2019, doi: 10.3115/v1/d14-1181.
[18]. M. H. BARI, “A SYSTEMATIC LITERATURE REVIEW OF PREDICTIVE MODELS AND ANALYTICS IN AI-DRIVEN CREDIT SCORING,” SSRN Electronic Journal, Oct. 2024, doi: 10.2139/SSRN.5050068.
[19]. C. Chen, K. Lin, C. Rudin, Y. Shaposhnik, S. Wang, and T. Wang, “An Interpretable Model with Globally Consistent Explanations for Credit Risk,” Nov. 2018, Accessed: Jan. 09, 2025. [Online]. Available: http://arxiv.org/abs/1811.12615
[20]. P. Golbayani, D. Wang, and I. Florescu, “Application of Deep Neural Networks to assess corporate Credit Rating,” Mar. 2020, Accessed: Jan. 09, 2025. [Online]. Available: http://arxiv.org/abs/2003.02334
[21]. L. Marceau, L. Qiu, N. Vandewiele, and E. Charton, “A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data,” Jul. 2019, Accessed: Jan. 09, 2025. [Online]. Available: http://arxiv.org/abs/1907.12363
[22]. S. Shi, R. Tse, W. Luo, S. D’Addona, and G. Pau, “Machine learning-driven credit risk: a systemic review,” Neural Comput Appl, vol. 34, no. 17, pp. 14327–14339, Sep. 2022, doi: 10.1007/S00521-022-07472-2/TABLES/6.
[23]. A. M. Rahmani, B. Rezazadeh, M. Haghparast, W. C. Chang, and S. G. Ting, “Applications of Artificial Intelligence in the Economy, Including Applications in Stock Trading, Market Analysis, and Risk Management,” IEEE Access, vol. 11, pp. 80769–80793, 2023, doi: 10.1109/ACCESS.2023.3300036.
[24]. P. Cerchiello and P. Giudici, “Big data analysis for financial risk management,” J Big Data, vol. 3, no. 1, pp. 1–12, Dec. 2016, doi: 10.1186/S40537-016-0053-4/FIGURES/3.
[25]. Q. Zhang, K. J. Wu, and M. L. Tseng, “Exploring Carry Trade and Exchange Rate toward Sustainable Financial Resources: An application of the Artificial Intelligence UKF Method,” Sustainability, vol. 11, no. 12, p. 3240, 2019.
[26]. F. G. D. C. Ferreira, A. H. Gandomi, and R. T. N. Cardoso, “Artificial Intelligence Applied to Stock Market Trading: A Review,” IEEE Access, vol. 9, pp. 30898–30917, 2021, doi: 10.1109/ACCESS.2021.3058133.
[27]. V. M. Sangala, S. Alamanda, and P. Tirumalareddy, “AI-Infused Finance: Predicting Stock Prices Through News and Market Data Analysis,” Lecture Notes in Networks and Systems, vol. 1049 LNNS, pp. 390–403, 2024, doi: 10.1007/978-3-031-64779-6_38.
[28]. F. Alharbi and A. I. Al-Alawi, “Labor Market Prediction Using Machine Learning Methods: A Systematic Literature Review,” 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024, pp. 478–482, 2024, doi: 10.1109/ICETSIS61505.2024.10459632.
[29]. D. Acemoglu, D. Autor, J. Hazell, and P. Restrepo, “Artificial intelligence and jobs: evidence from online vacancies,” J Labor Econ, vol. 40, no. S1, pp. S293–S340, Apr. 2022, doi: 10.1086/718327.
[30]. O. Melnychenko, “Is Artificial Intelligence Ready to Assess an Enterprise’s Financial Security?,” Journal of Risk and Financial Management 2020, Vol. 13, Page 191, vol. 13, no. 9, p. 191, Aug. 2020, doi: 10.3390/JRFM13090191.
[31]. A. K. Mishra, S. Anand, N. C. Debnath, P. Pokhariyal, and A. Patel, “Artificial Intelligence for Risk Mitigation in the Financial Industry,” Artificial Intelligence for Risk Mitigation in the Financial Industry, pp. 1–355, Jan. 2024, doi: 10.1002/9781394175574.
[32]. C. Maple et al., “The AI Revolution: Opportunities and Challenges for the Finance Sector,” Aug. 2023, Accessed: Jan. 10, 2025. [Online]. Available: https://arxiv.org/abs/2308.16538v1
[33]. M. Ahmed Salamkar and S. J. Associate at Morgan Chase, “Real-time Analytics: Implementing ML algorithms to analyze data streams in real-time,” Journal of AI-Assisted Scientific Discovery, vol. 3, no. 2, pp. 587–612, Sep. 2023, Accessed: Jan. 10, 2025. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/223
[34]. V. Baulkaran and P. Jain, “Behavioral Biases of Financial Planners: The Case of Retirement Funding Recommendations,” Journal of Behavioral Finance, 2024, doi: 10.1080/15427560.2024.2305412.
[35]. V. S. Athota, V. Pereira, Z. Hasan, D. Vaz, B. Laker, and D. Reppas, “Overcoming financial planners’ cognitive biases through digitalization: A qualitative study,” J Bus Res, vol. 154, p. 113291, Jan. 2023, doi: 10.1016/J.JBUSRES.2022.08.055.
[36]. C. Rastogi, Y. Zhang, D. Wei, K. R. Varshney, A. Dhurandhar, and R. Tomsett, “Deciding Fast and Slow: The Role of Cognitive Biases in AI-assisted Decision-making,” Proc ACM Hum Comput Interact, vol. 6, no. CSCW1, Apr. 2022, doi: 10.1145/3512930.
[37]. R. K. Inampudi, T. Pichaimani, and Y. Surampudi, “AI-Enhanced Fraud Detection in Real-Time Payment Systems: Leveraging Machine Learning and Anomaly Detection to Secure Digital Transactions,” Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, pp. 483–523, Mar. 2022, Accessed: Jan. 10, 2025. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/189
[38]. R. T. Potla, “AI in Fraud Detection: Leveraging Real-Time Machine Learning for Financial Security,” Journal of Artificial Intelligence Research and Applications, vol. 3, no. 2, pp. 534–549, Oct. 2023, Accessed: Jan. 10, 2025. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/189
[39]. O. A. Bello, A. Ogundipe, D. Mohammed, F. Adebola, and O. A. Alonge, “AI-Driven Approaches for Real-Time Fraud Detection in US Financial Transactions: Challenges and Opportunities,” 2023, Accessed: Jan. 10, 2025. [Online]. Available: https://eajournals.org/ejcsit/
[40]. J. Rashid, T. Mahmood, M. W. Nisar, and T. Nazir, “Phishing Detection Using Machine Learning Technique,” Proceedings - 2020 1st International Conference of Smart Systems and Emerging Technologies, SMART-TECH 2020, pp. 43–46, Nov. 2020, doi: 10.1109/SMART-TECH49988.2020.00026.
[41]. O. K. Sahingoz, E. Buber, O. Demir, and B. Diri, “Machine learning based phishing detection from URLs,” Expert Syst Appl, vol. 117, pp. 345–357, Mar. 2019, doi: 10.1016/J.ESWA.2018.09.029.
[42]. M. Noor and U. Milon, “Gravitating towards Artificial Intelligence on Anti-Money Laundering A PRISMA Based Systematic Review,” International Journal of Religion, vol. 5, no. 7, pp. 303–315, May 2024, doi: 10.61707/PY0FE669.
[43]. L. L. Dhirani, N. Mukhtiar, B. S. Chowdhry, and T. Newe, “Ethical Dilemmas and Privacy Issues in Emerging Technologies: A Review,” Sensors 2023, Vol. 23, Page 1151, vol. 23, no. 3, p. 1151, Jan. 2023, doi: 10.3390/S23031151.
[44]. O. O. Adeyelu, C. E. Ugochukwu, and M. A. Shonibare, “AUTOMATING FINANCIAL REGULATORY COMPLIANCE WITH AI: A REVIEW AND APPLICATION SCENARIOS,” Finance & Accounting Research Journal, vol. 6, no. 4, pp. 580–601, Apr. 2024, doi: 10.51594/FARJ.V6I4.1035.
[45]. S. Patel and A. Rahman, “Data Privacy in the Digital Age: Navigating Compliance and Ethical Challenges,” Baltic Multidisciplinary Research Letters Journal, vol. 1, no. 3, pp. 13–24, Nov. 2024, Accessed: Jan. 10, 2025. [Online]. Available: https://www.bmrlj.com/index.php/Baltic/article/view/21
[46]. D. Banciu and C. E. Cirnu, “AI Ethics and Data Privacy compliance,” 2022 14th International Conference on Electronics, Computers and Artificial Intelligence, ECAI 2022, 2022, doi: 10.1109/ECAI54874.2022.9847510.
[47]. S. Gupta, S. Bagga, and D. K. Sharma, “Intelligent Data Analysis,” Intelligent Data Analysis, pp. 1–15, Jun. 2020, doi: 10.1002/9781119544487.CH1.
[48]. G. Deipenbrock, “Artificial intelligence and machine learning in the financial sector: Legal-methodological challenges of steering towards a regulatory ‘whitebox,’” Routledge Handbook of Financial Technology and Law, pp. 3–26, Jan. 2021, doi: 10.4324/9780429325670-1/ARTIFICIAL-INTELLIGENCE-MACHINE-LEARNING-FINANCIAL-SECTOR-GUDULA-DEIPENBROCK.
[49]. P. Wirawan, “Leveraging Predictive Analytics in Financing Decision-Making for Comparative Analysis and Optimization,” Advances in Management & Financial Reporting, vol. 1, no. 3, pp. 157–169, Sep. 2023, doi: 10.60079/AMFR.V1I3.209.
[50]. T. D. Olorunyomi, I. C. Okeke, O. G. Ejike, and A. G. Adeleke, “Using Fintech innovations for predictive financial modeling in multi-cloud environments,” Computer Science & IT Research Journal, vol. 5, no. 10, pp. 2357–2370, 2024.
[51]. F. Ekundayo, I. Atoyebi, A. Soyele, and E. Ogunwobi, “Predictive Analytics for Cyber Threat Intelligence in Fintech Using Big Data and Machine Learning,” Int J Res Publ Rev, vol. 5, no. 11, pp. 1–15, 2024.
[52]. A. O. Abhulimen, E. E. Agu, A. Nkemchor Obiki-Osafiele, O. S. Osundare, I. A. Adeniran, and C. P. Efunniyi, “4 Nigeria Inter-bank Settlement System Plc (NIBSS). 5 International Association of Computer Analysts and Researchers,” UK. International Journal of Frontline Research in Multidisciplinary Studies, vol. 2024, no. 02, pp. 20–029, 2024, doi: 10.56355/ijfrms.2024.3.2.0026.
[53]. B. Jadhav, A. Kulkarni, P. Kulkarni, and S. Kulkarni, “Artificial Intelligence and Big Data Analytics in Digital Gig Finance,” Synergy of AI and Fintech in the Digital Gig Economy, pp. 96–108, Sep. 2024, doi: 10.1201/9781032720104-6.
[54]. A. Khang, B. Jadhav, V. Abdullayev Hajimahmud, and I. Satpathy, “Synergy of AI and Fintech in the Digital Gig Economy,” Synergy of AI and Fintech in the Digital Gig Economy, Aug. 2024, doi: 10.1201/9781032720104/SYNERGY-AI-FINTECH-DIGITAL-GIG-ECONOMY.
[55]. Y. Liang, D. Quan, F. Wang, X. Jia, M. Li, and T. Li, “Financial big data analysis and early warning platform: A case study,” IEEE Access, vol. 8, pp. 36515–36526, 2020, doi: 10.1109/ACCESS.2020.2969039.
[56]. N. R. Boinapalli, “AI-Driven Predictive Analytics for Risk Management in Financial Markets,” Silicon Valley Tech Review, vol. 2, no. 1, pp. 41–53, 2023.
[57]. A. Zainal, “Role of Artificial Intelligence and Big Data Technologies in Enhancing Anomaly Detection and Fraud Prevention in Digital Banking Systems,” International Journal of Advanced Cybersecurity Systems, Technologies, and Applications, vol. 7, no. 12, pp. 1–10, Dec. 2023, Accessed: Jan. 06, 2025. [Online]. Available: https://theaffine.com/index.php/IJACSTA/article/view/2023-12-04
[58]. S. R. Gayam, “AI-Driven Fraud Detection in E-Commerce: Advanced Techniques for Anomaly Detection, Transaction Monitoring, and Risk Mitigation,” Distributed Learning and Broad Applications in Scientific Research, vol. 6, pp. 124–151, Nov. 2020, Accessed: Jan. 06, 2025. [Online]. Available: https://dlabi.org/index.php/journal/article/view/108
[59]. S. Dixit, “Advanced Generative AI Models for Fraud Detection and Prevention in FinTech: Leveraging Deep Learning and Adversarial Networks for Real-Time Anomaly Detection in Financial Transactions,” Journal of Artificial Intelligence Research, vol. 4, no. 2, pp. 51–77, Oct. 2024, Accessed: Jan. 06, 2025. [Online]. Available: https://nucleuscorp.org/JAIR/article/view/402
[60]. S. Agrawal, “Enhancing Payment Security Through AI-Driven Anomaly Detection and Predictive Analytics,” International Journal of Sustainable Infrastructure for Cities and Societies, vol. 7, no. 2, pp. 1–14, Apr. 2022, Accessed: Jan. 06, 2025. [Online]. Available: https://vectoral.org/index.php/IJSICS/article/view/99
[61]. J. Liu, F. Xu, and X. Liu, “Financial Market Sentiment Analysis and Investment Strategy Formulation Based on Social Network Data,” J. Electrical Systems, vol. 20, no. 9, pp. 655–660, 2024.
[62]. N. R. Mitta, “Leveraging Natural Language Processing (NLP) for AI-Based Sentiment Analysis in Financial Markets: Real-Time Insights for Trading Strategies and Risk Management,” African Journal of Artificial Intelligence and Sustainable Development, vol. 3, no. 2, pp. 398–434, Dec. 2023, Accessed: Jan. 06, 2025. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/209
[63]. S. García-Méndez, F. de Arriba-Pérez, A. Barros-Vila, and F. J. González-Castaño, “Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages,” Expert Syst Appl, vol. 218, p. 119611, May 2023, doi: 10.1016/J.ESWA.2023.119611.
[64]. S. G. Anton and A. E. A. Nucu, “Firm value and corporate cash holdings. Empirical evidence from the Polish listed firms,” E a M: Ekonomie a Management, vol. 22, no. 3, pp. 121–134, 2019, doi: 10.15240/TUL/001/2019-3-008.
[65]. K. B. Hansen and C. Borch, “Alternative data and sentiment analysis: Prospecting non-standard data in machine learning-driven finance,” https://doi.org/10.1177/20539517211070701, vol. 9, no. 1, Jan. 2022, doi: 10.1177/20539517211070701.
[66]. C. Xiang, J. Zhang, F. Li, H. Fei, and D. Ji, “A semantic and syntactic enhanced neural model for financial sentiment analysis,” Inf Process Manag, vol. 59, no. 4, p. 102943, Jul. 2022, doi: 10.1016/J.IPM.2022.102943.
[67]. M. A. Faheem, “AI-Driven Risk Assessment Models: Revolutionizing Credit Scoring and Default Prediction,” 2021.
[68]. F. Doshi-Velez and B. Kim, “Towards A Rigorous Science of Interpretable Machine Learning,” Feb. 2017, Accessed: Jan. 10, 2025. [Online]. Available: http://arxiv.org/abs/1702.08608
[69]. S. Putha, “AI-Enabled Predictive Analytics for Enhancing Credit Scoring Models in Banking,” Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, pp. 290–330, Feb. 2021, Accessed: Jan. 09, 2025. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/200
[70]. M. T. Ribeiro, S. Singh, and C. Guestrin, “‘Why should i trust you?’ Explaining the predictions of any classifier,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016, pp. 1135–1144, Aug. 2016, doi: 10.1145/2939672.2939778.
[71]. H. Cramer, J. Garcia-Gathright, A. Springer, and S. Reddy, “Assessing and addressing algorithmic bias in practice,” Interactions, vol. 25, no. 6, pp. 58–63, Nov. 2018, doi: 10.1145/3278156.
[72]. D. Roselli, J. Matthews, and N. Talagala, “Managing bias in AI,” The Web Conference 2019 - Companion of the World Wide Web Conference, WWW 2019, pp. 539–544, May 2019, doi: 10.1145/3308560.3317590.
[73]. L. H. Nazer et al., “Bias in artificial intelligence algorithms and recommendations for mitigation,” PLOS Digital Health, vol. 2, no. 6, p. e0000278, Jun. 2023, doi: 10.1371/JOURNAL.PDIG.0000278.
[74]. A. Oseni, N. Moustafa, H. Janicke, P. Liu, Z. Tari, and A. Vasilakos, “Security and Privacy for Artificial Intelligence: Opportunities and Challenges,” Feb. 2021, Accessed: Jan. 10, 2025. [Online]. Available: http://arxiv.org/abs/2102.04661
[75]. K. R. Gade, “Data Analytics: Data Privacy, Data Ethics, Data Monetization,” MZ Computing Journal, vol. 1, no. 1, Apr. 2020, Accessed: Jan. 10, 2025. [Online]. Available: https://mzjournal.com/index.php/MZCJ/article/view/409
[76]. N. I. Qureshi, S. S. Choudhuri, Y. Nagamani, R. A. Varma, and R. Shah, “Ethical Considerations of AI in Financial Services: Privacy, Bias, and Algorithmic Transparency,” 2024 International Conference on Knowledge Engineering and Communication Systems, ICKECS 2024, 2024, doi: 10.1109/ICKECS61492.2024.10616483.
[77]. V. Bharath Munagandla, S. Surya, V. Dandyala, and B. Chandra Vadde, “The Future of Data Analytics: Trends, Challenges, and Opportunities,” Revista de Inteligencia Artificial en Medicina, vol. 13, no. 1, pp. 421–442, Nov. 2022, Accessed: Jan. 10, 2025. [Online]. Available: https://redcrevistas.com/index.php/Revista/article/view/171
[78]. L. Pham, B. O’Sullivan, T. Scantamburlo, and T. Mai, “Addressing Digital and AI Skills Gaps in European Living Areas: A Comparative Analysis of Small and Large Communities,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 38, no. 21, pp. 23119–23127, Mar. 2024, doi: 10.1609/AAAI.V38I21.30357.
[79].






