Can Artificial Intelligence Improve Customer Acquisition in Digital Stock Broking Platforms?
DOI:
https://doi.org/10.64137/31080030/IJFEMS-V2I4P103Keywords:
Customer Acquisition, Artificial Intelligence, Stock Broking, Reinforcement Learning, Deep Learning, Portfolio ManagementAbstract
Stock broking and other financial services are constantly being transformed by Artificial Intelligence (AI) with personalization, improved data analysis, consumer engagement, and smart decision-making. This study aims to investigate how customer acquisition can be improved by AI in stock broking, while focusing on its uses in sentiment analysis, predictive analytics, portfolio management, reinforcement learning, and risk analysis. This study uses a quantitative research design and collects secondary data from recent studies published in peer-reviewed journals, as well as from publications, stock exchanges, reports, official websites, and other credible sources. Customer acquisition can be supported by AI through personalized services, more relevant and faster information, better customer experiences, and more efficient services for digital stock broking platforms. AI models like Reinforcement Learning and Deep Learning are highly prevalent in financial trading research because they can identify complex patterns and adapt to market changes. The study also explores potential benefits for trading, investment profitability, portfolio management, and risk assessment. However, data quality, transaction costs, model bias, overfitting, cybersecurity issues, lack of interpretability and uncertain market conditions limit AI's potential. Therefore, AI's success in driving customer acquisition depends on transparency, responsible use, compliance, reliable data, and strong market knowledge.
References
[1] S. R. Agrawal and D. Mittal, “Measuring CRM Effectiveness in Indian Stock Broking Services,” Journal of Global Information Management, vol. 27, no. 1, pp. 144–164, Jan. 2019, doi: 10.4018/jgim.2019010108.
[2] S. Ahmed, M. M. Alshater, A. El Ammari, and H. Hammami, “Artificial Intelligence and Machine Learning in Finance: A Bibliometric Review,” Research in International Business and Finance, vol. 61, no. 101646, Oct. 2022, doi: 10.1016/j.ribaf.2022.101646.
[3] S. Anappindi and M. S. Manohar, “I Am Here to Stay! The Stock Broking Way (an Indian Perspective): Part 1,” Industrial and Commercial Training, vol. 43, no. 5, pp. 297–306, July 2011, doi: 10.1108/00197851111145899.
[4] Areiqat, Ahmad Yousef; Alheet, Ahmad Fathi; Qawasmeh, Rasha A.A. et al. / Artificial intelligence and its drastic impact on e-commerce progress. In: Academy of Strategic Management Journal. 2021 ; Vol. 20, No. SpecialIssue2. pp. 1-11.
[5] Y. Areiqat, A. Hamdan, A. F. Alheet, and B. Alareeni, “Impact of Artificial Intelligence on E-Commerce Development,” The Importance of New Technologies and Entrepreneurship in Business Development: In The Context of Economic Diversity in Developing Countries, pp. 571–578, 2021, doi: 10.1007/978-3-030-69221-6_43.
[6] Bahrammirzaee, “A Comparative Survey of Artificial Intelligence Applications in Finance: Artificial Neural Networks, Expert System and Hybrid Intelligent Systems,” Neural Computing and Applications, vol. 19, no. 8, pp. 1165–1195, June 2010, doi: 10.1007/s00521-010-0362-z.
[7] H. K. Baker and G. Filbeck, Eds., Portfolio Theory and Management. Oxford University Press, 2013. doi: 10.1093/acprof:oso/9780199829699.001.0001.
[8] Eyo Bassey, S. O. Effiok, and O. E. Eton, “The Impact of Environmental Accounting and Reporting on Organizational Performance of Selected Oil and Gas Companies in Niger Delta Region of Nigeria,” Research Journal of Finance and Accounting, vol. 4, no. 3, pp. 57–73, Accessed: Oct. 22, 2026. [Online]. Available: https://iiste.org/Journals/index.php/RJFA/article/view/4962
[9] R. E. Bawack, S. F. Wamba, K. D. A. Carillo, and S. Akter, “Artificial Intelligence in E-Commerce: A Bibliometric Study and Literature Review,” Electronic Markets, vol. 32, no. 1, Mar. 2022, Accessed: Oct. 22, 2026. [Online]. Available: https://link.springer.com/article/10.1007/s12525-022-00537-z
[10] J. P. Bharadiya, “A Comparative Study of Business Intelligence and Artificial Intelligence With Big Data Analytics,” American Journal of Artificial Intelligence, vol. 7, no. 1, June 2023, doi: 10.11648/j.ajai.20230701.14.
[11] J. Brzeszczyński, J. Gajdka, and A. M. Kutan, “Investor Response to Public News, Sentiment and Institutional Trading in Emerging Markets: A Review,” International Review of Economics & Finance, vol. 40, pp. 338–352, Nov. 2015, doi: 10.1016/j.iref.2015.10.042.
[12] H. Buehler, L. Gonon, J. Teichmann, and B. Wood, “Deep Hedging,” Quantitative Finance, vol. 19, no. 8, pp. 1271–1291, Feb. 2019, doi: 10.1080/14697688.2019.1571683.
[13] S. Rao Challa, “Artificial Intelligence and Big Data in Finance: Enhancing Investment Strategies and Client Insights in Wealth Management,” International Journal of Science and Research (IJSR), vol. 12, no. 12, pp. 2230–2246, Dec. 2023, doi: 10.21275/sr231215165201.
[14] S. C. Chow, Y. Hui, J. P. Vieito, and Z. Zhu, “Market Liberalizations and Efficiency in Latin America,” Studies in Economics and Finance, vol. 33, no. 4, pp. 553–575, Oct. 2016, doi: 10.1108/sef-01-2015-0014.
[15] F. Dakalbab, M. A. Talib, Q. Nasir, and T. Saroufil, “Artificial Intelligence Techniques in Financial Trading: A Systematic Literature Review,” Journal of King Saud University. Computer and information sciences/Maǧalaẗ ǧamʼaẗ al-malīk Saud: ùlm al-ḥasib wa al-maʼlumat, vol. 36, no. 3, pp. 102015–102015, Mar. 2024, doi: 10.1016/j.jksuci.2024.102015.
[16] Q.-V. Dang, “Reinforcement Learning in Stock Trading,” pp. 311–322, Dec. 2019, doi: 10.1007/978-3-030-38364-0_28.
[17] Y. Deng, F. Bao, Y. Kong, Z. Ren, and Q. Dai, “Deep Direct Reinforcement Learning for Financial Signal Representation and Trading,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 653–664, Mar. 2017, doi: 10.1109/tnnls.2016.2522401.
[18] O. Entrop, B. Frijns, and M. Seruset, “The Determinants of Price Discovery on Bitcoin Markets,” Journal of Futures Markets, vol. 40, no. 5, pp. 816–837, Feb. 2020, doi: 10.1002/fut.22101.
[19] E. Ferrara, “Fairness and Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, and Mitigation Strategies,” Sci, vol. 6, no. 1, p. 3, Dec. 2023, doi: 10.3390/sci6010003.
[20] 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, no. 2169–3536, pp. 30898–30917, Feb. 2021, doi: 10.1109/access.2021.3058133.
[21] R. Füss, “Financial Liberalization and Stock Price Behavior in Asian Emerging Markets,” Economic Change and Restructuring, vol. 38, no. 1, pp. 37–62, Mar. 2005, doi: 10.1007/s10644-005-4522-6.
[22] K. Gai, M. Qiu, and X. Sun, “A Survey on FinTech,” Journal of Network and Computer Applications, vol. 103, no. 1, pp. 262–273, Feb. 2018, doi: 10.1016/j.jnca.2017.10.011.
[23] V. Garg, S. Aggarwal, P. Tiwari, and P. Chatterjee, Applications of Artificial Intelligence in Business and Finance. Boca Raton: Apple Academic Press, 2021. doi: 10.1201/9781003129639.
[24] X. Geng et al., “Data‐Driven and Artificial Intelligence Accelerated Steel Material Research and Intelligent Manufacturing Technology,” Materials Genome Engineering Advances, vol. 1, no. 1, Sept. 2023, doi: 10.1002/mgea.10.
[25] Ghimire, S. Thapa, A. K. Jha, S. Adhikari, and A. Kumar, “Accelerating Business Growth With Big Data and Artificial Intelligence,” IEEE Xplore, Oct. 01, 2020. https://ieeexplore.ieee.org/abstract/document/9243318 (accessed Oct. 22, 2026).
[26] P. Giudici, “Fintech Risk Management: A Research Challenge for Artificial Intelligence in Finance,” Frontiers in Artificial Intelligence, vol. 1, no. 1, Nov. 2018, doi: 10.3389/frai.2018.00001.
[27] J. W. Goodell, S. Kumar, W. M. Lim, and D. Pattnaik, “Artificial Intelligence and Machine Learning in Finance: Identifying Foundations, Themes, and Research Clusters From Bibliometric Analysis,” Journal of Behavioral and Experimental Finance, vol. 32, no. 1, p. 100577, Dec. 2021, doi: 10.1016/j.jbef.2021.100577.
[28] S. GRAMATOVICI and C.-M. MORTICI, “RANDOM WALK HYPOTHESIS on BUCHAREST STOCK EXCHANGE,” Review of the Air Force Academy, vol. 16, no. 2, pp. 59–74, Oct. 2018, doi: 10.19062/1842-9238.2018.16.2.7.
[29] W. Guo and M. E. Wohar, “IDENTIFYING REGIME CHANGES in MARKET VOLATILITY,” Journal of Financial Research, vol. 29, no. 1, pp. 79–93, Jan. 2006, doi: 10.1111/j.1475-6803.2006.00167.x.
[30] Y. J. Hilpisch, Artificial Intelligence in Finance: A Python-Based Guide. O’Reilly, 2021.https://www.oreilly.com/library/view/artificial-intelligence-in/9781492055426/.
[31] M. Horn and A. Oehler, “Automated Portfolio Rebalancing: Automatic Erosion of Investment Performance?,” Journal of Asset Management, vol. 21, no. 6, pp. 489–505, Sept. 2020, doi: 10.1057/s41260-020-00183-0.
[32] J. Sen and T. Datta Chaudhuri, “A Robust Predictive Model for Stock Price Forecasting,” Oct. 2021, doi: 10.36227/techrxiv.16778611.
[33] Z. Huriye, “The Ethics of Artificial Intelligence: Examining the Ethical Considerations Surrounding the Development and Use of AI,” American Journal of Technology, vol. 2, no. 1, pp. 37–45, Apr. 2023, doi: 10.58425/ajt.v2i1.142.
[34] P. Kalia, “Artificial Intelligence in E-Commerce,” Artificial Intelligence, pp. 9–19, June 2021, doi: 10.1201/9781003095910-2.
[35] Kiyasseh et al., “Human Visual Explanations Mitigate Bias in AI-based Assessment of Surgeon Skills,” npj Digital Medicine, vol. 6, no. 1, pp. 1–12, Mar. 2023, doi: 10.1038/s41746-023-00766-2.
[36] H. T. Koh and M. N. Mohd Noor, “The DEVELOPMENT of RESEARCH FRAMEWORK for the DECISION to PARTICIPATE in STOCK MARKET: A SYSTEMATIC REVIEW of LITERATURES FROM 2011-2021,” Social and Management Research Journal, vol. 18, no. 2, pp. 67–85, Sept. 2021, doi: 10.24191/smrj.v18i2.14897.
[37] Michel, E. F. Sbruzzi, Jose, and C. L. Nascimento, “Trading Switching Setup Based on Reinforcement Learning Applied to a Multiagent System Simulation of Financial Markets,” Apr. 2019, doi: 10.1109/syscon.2019.8836887.
[38] L. Xucheng and P. Zhihao, “A Novel Algorithmic Trading Approach Based on Reinforcement Learning,” 2019 11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), pp. 394–398, Apr. 2019, doi: 10.1109/icmtma.2019.00093.
[39] Y. LI and L. ZHANG, “Short Selling Pressure, Stock Price Behavior, and Management Forecast Precision: Evidence From a Natural Experiment,” Journal of Accounting Research, vol. 53, no. 1, pp. 79–117, Jan. 2015, doi: 10.1111/1475-679x.12068.
[40] Y. Li, P. Ni, and V. Chang, “Application of Deep Reinforcement Learning in Stock Trading Strategies and Stock Forecasting,” Computing, vol. 102, no. 6, pp. 1305–1322, Dec. 2019, doi: 10.1007/s00607-019-00773-w.
[41] M. Mathur, S. Mhadalekar, S. Mhatre, and V. Mane, “Algorithmic Trading Bot,” ITM Web of Conferences, vol. 40, p. 03041, 2021, doi: 10.1051/itmconf/20214003041.
[42] S. McLennan, A. Fiske, D. Tigard, R. Müller, S. Haddadin, and A. Buyx, “Embedded Ethics: A Proposal for Integrating Ethics Into the Development of Medical AI,” BMC Medical Ethics, vol. 23, no. 1, Jan. 2022, doi: 10.1186/s12910-022-00746-3.
[43] S. Mehtab and J. Sen, “Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Time Series,” arXiv.org, Aug. 02, 2021. https://arxiv.org/abs/2001.09769 (accessed Oct. 22, 2026).
[44] T. L. Meng and M. Khushi, “Reinforcement Learning in Financial Markets,” Data, vol. 4, no. 3, p. 110, July 2019, doi: 10.3390/data4030110.
[45] B. Mittelstadt, “Principles Alone Cannot Guarantee Ethical AI,” Nature Machine Intelligence, vol. 1, no. 11, pp. 501–507, Nov. 2019, doi: 10.1038/s42256-019-0114-4.
[46] M. Mourelatos, C. Alexakos, T. Amorgianiotis, and S. Likothanassis, “Financial Indices Modeling and Trading Utilizing Deep Learning Techniques: The ATHENS SE FTSE/ASE Large Cap Use Case,” IEEE Xplore, July 01, 2018. https://ieeexplore.ieee.org/abstract/document/8466286 (accessed Oct. 22, 2026).
[47] Northcott, The Complete Guide to Using Candlestick Charting: How to Earn High Rates of Return-Safely. Ocala, Fla.: Atlantic Pub. Group, 2009. ISBN: 9781601382948.
[48] O. Ogundimu, “Artificial Intelligence in Financial Markets: Enhancing Risk Assessment and Decision-Making in Stockbrokering and Banking,” Cognizance Journal of Multidisciplinary Studies, vol. 5, no. 6, pp. 239–263, June 2025, doi: 10.47760/cognizance.2025.v05i06.018.
[49] R Pal (2022). APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN COMPANY MANAGEMENT, E-COMMERCE, AND FINANCE:
[50] A REVIEW, International Journal of Multidisciplinary Educational Research.
[51] H. Pallathadka, E. H. Ramirez-Asis, T. P. Loli-Poma, K. Kaliyaperumal, R. J. M. Ventayen, and M. Naved, “Applications of Artificial Intelligence in Business Management, E-Commerce and Finance,” Materials Today: Proceedings, vol. 80, no. 3, July 2023, doi: 10.1016/j.matpr.2021.06.419.
[52] M. R. Rabbani, A. Lutfi, M. A. Ashraf, N. Nawaz, and W. Ahmad Watto, “Role of Artificial Intelligence in Moderating the Innovative Financial Process of the Banking Sector: A Research Based on Structural Equation Modeling,” Frontiers in Environmental Science, vol. 10, Jan. 2023, doi: 10.3389/fenvs.2022.978691.
[53] N. Rane, S. Choudhary, and J. Rane, “Artificial Intelligence and Machine Learning in Business Intelligence, Finance, and E-Commerce: A Review,” Social Science Research Network, Jan. 2024, doi: 10.2139/ssrn.4843988.
[54] Dr. S. S. Uttareshwar, “Artificial Intelligence in Stock Trading: Enhancing Decision-Making and Predictive Analytics,” International Scientific Journal of Engineering and Management, vol. 04, no. 07, pp. 1–9, July 2025, doi: 10.55041/isjem04810.
[55] J. Sen and Tamal Datta Chaudhuri, “A Robust Predictive Model for Stock Price Forecasting,” INDIGO (University of Illinois at Chicago), Oct. 2021, doi: 10.36227/techrxiv.16778611.v1.
[56] S. Shaikh, K. Khan, F. K. Sherwani, and M. U. Khan, “Smart Trading: Unlocking Artificial Intelligence in Stock Market,” The Business and Management Review, vol. 15, no. 03, Jan. 2025, doi: 10.24052/bmr/v15nu03/art-17.
[57] M. Shanmuganathan, “Behavioral Finance in an Era of Artificial Intelligence: Longitudinal Case Study of Robo-Advisors in Investment Decisions,” Journal of Behavioral and Experimental Finance, vol. 27, pp. 100–297, Mar. 2020, doi: 10.1016/j.jbef.2020.100297.
[58] N. Sommerfeld, D. Webster-Clark, Q. de Louvencourt, and L. Johns, “Your Next Customer Will Find You Using AI. Now What?,” Bain, Apr. 07, 2026. https://www.bain.com/insights/your-next-customer-will-find-you-using-ai-now-what/ (accessed Oct. 22, 2026).
[59] S. Garrison Hongyu and A. Jain, “Can Artificial Intelligence Beat the Stock Market?” Studies in Economics and Finance, vol. 39, no. 5, pp. 772–785, Apr. 2022, doi: 10.1108/SEF-03-2022-0133.
[60] X. Song, S. Yang, Z. Huang, and T. Huang, “The Application of Artificial Intelligence in Electronic Commerce,” Journal of Physics: Conference Series, vol. 1302, no. 3, pp. 1–6, Aug. 2019, doi: 10.1088/1742-6596/1302/3/032030.
[61] K. Sutiene, P. Schwendner, C. Sipos, and L. Lorenzo, “Enhancing Portfolio Management Using Artificial Intelligence: Literature Review,” Frontiers in Artificial Intelligence, vol. 7, Apr. 2024, doi: 10.3389/frai.2024.1371502.
[62] Van D. Ta, C.-M. Liu, and D. Addis, “Prediction and Portfolio Optimization in Quantitative Trading Using Machine Learning Techniques,” Proceedings of the Ninth International Symposium on Information and Communication Technology - SoICT 2018, 2018, doi: 10.1145/3287921.3287963.
[63] M.-H. Tran, N. Q. Nguyen, and H. T. Pham, “A New Hope in the Fight Against Antimicrobial Resistance With Artificial Intelligence,” Infection and Drug Resistance, vol. 15, pp. 2685–2688, May 2022, doi: 10.2147/idr.s362356.
[64] P. Treleaven, M. Galas, and V. Lalchand, “Algorithmic Trading Review,” Communications of the ACM, vol. 56, no. 11, pp. 76–85, Nov. 2013, doi: 10.1145/2500117.
[65] L. Vanneschi, D. M. Horn, M. Castelli, and A. Popovič, “An Artificial Intelligence System for Predicting Customer Default in E-Commerce,” Expert Systems with Applications, vol. 104, pp. 1–21, Aug. 2018, doi: 10.1016/j.eswa.2018.03.025.
[66] G. Wang, G. Yu, and X. Shen, “The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach,” Complexity, vol. 2020, pp. 1–11, Dec. 2020, doi: 10.1155/2020/4754025.
[67] Q. Wang, K. Kang, Z. Zhang, and D. Cao, “Application of LSTM and CONV1D LSTM Network in Stock Forecasting Model,” Artificial Intelligence Advances, vol. 3, no. 1, pp. 36–43, Apr. 2021, doi: 10.30564/aia.v3i1.2790.
[68] Y. Wang, C. Yu, and X. Zhao, “Does Herding Effect Help Forecast Market Volatility? ‐ Evidence From Chinese Stock Market,” Journal of Forecasting, Feb. 2023, doi: 10.1002/for.2968.
[69] X. Yu, W. Wu, X. Liao, and Y. Han, “Dynamic Stock-Decision Ensemble Strategy Based on Deep Reinforcement Learning,” Applied Intelligence, vol. 53, no. 2, pp. 2452–2470, May 2022, doi: 10.1007/s10489-022-03606-0.
