DOI: https://doi.org/10.32515/2664-262X.2025.11(42).2.3-10
Method and Technological Solution of an AI-Based Adaptive Investor Survey Service for Determining an Individual Risk Profile
About the Authors
Oleksandr Korniienko, PhD student, Kherson National Technical University, Kherson, Ukraine, ORCID: https://orcid.org/0009-0008-6234-784X, e-mail: aleksandrkornienko19992106@gmail.com
Nataliia Kozub, Candidate of Technical Sciences, Associate Professor, Kherson National Technical University, Kherson, Ukraine, https://orcid.org/0000-0002-0406-0161, e-mail: actinis1@gmail.com
Oleksandr Dorenskyi , Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of Cybersecurity and Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, https://orcid.org/0000-0002-7625-9022, e-mail: dorenskyiop@kntu.kr.ua
Abstract
An adaptive investor survey model employing advanced machine learning is presented to generate a continuous risk profile. Using conditional logic, weighting coefficients, and a continuous risk scale, it overcomes traditional questionnaire limitations to enhance accuracy and personalization. The system built on React, Node.js/NestJS, and Python/FastAPI efficiently processes responses and delivers tailored investment recommendations. The research also includes the results of a comparative analysis, a description of the data transformation methodology, and a secure data transfer scheme, confirming the practical effectiveness of the proposed solutions. The developed method, model, and technological solution of the AI-driven adaptive survey service enhance the accuracy and personalization of risk profiling.
Keywords
digital transformation, machine learning, adaptive polling, investor risk profile, conditional logic, continuous risk scale, personalized recommendations
Full Text:
PDF
References
1. Sharpe, W. F. (1999). Investments. Prentice Hall.
2. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. John Wiley & Sons.
3. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
4. USAID Financial Sector Transformation Project. (2021). Financial literacy, financial inclusion, and financial well-being in Ukraine (2021). Retrieved April 18, 2025, from https://bank.gov.ua/admin_uploads/article/ Research_Financial_Literacy_Inclusion_Welfare_2021.pdf
5. Riskalyze. (n.d.). Risk number methodology. Retrieved April 18, 2025, from https://www.riskalyze.com
6. Grable, J. E., & Lytton, R. H. (1999). Financial risk tolerance revisited: The development of a risk assessment instrument. Financial Services Review, 8(3), 163–181.
7. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
8. Lo, A. W. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
9. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
10. van der Linden, W. J. (2000). Computerized Adaptive Testing: Theory and Practice. Springer.
11. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009
12. Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Sage Publications.
13. Weiss, D. J. (1988). Computerized Adaptive Testing: Theory and Practice. Springer.
14. Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace.
15. Node.js Foundation. (n.d.). Node.js Documentation. Retrieved April 18, 2025, from https://nodejs.org/en/docs/
16. Dorenskyi, O., Drobko, O., & Drieiev, O. (2022). Improved model and software of the digital information service of the municipal health care institutions. Tsentralʹnoukrainsʹkyi naukovyi visnyk. Tekhnichni nauky, 5(36), Pt. 2, 3–10. https://doi.org/10.32515/2664-262X.2022.5(36).2.3-10.
17. Kachurivskyi, V., Kotovskyi, A., Lykhodid, T., Kachurivska, H., & Dorenskyi, O. (2025). The concept of digital transformation of monitoring scientific activity of participants in educational process of the Ukrainian HEI. Tsentralʹnoukrainsʹkyi naukovyi visnyk. Tekhnichni nauky, 11(42), Pt. 1, 27–36. https://doi.org/10.32515/2664-262X.2025.11(42).1.27-36.
Citations
1. Sharpe, W. F. Investments. Prentice Hall, 1999. 962 с.
2. Markowitz, H. Portfolio Selection: Efficient Diversification of Investments. John Wiley & Sons, 1959. 351 с.
3. Murphy, K. P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012. 1104 с.
4. USAID Проєкт «Трансформація фінансового сектору». Фінансова грамотність, фінансова інклюзія та фінансовий добробут в Україні у 2021 році [Електронний ресурс]. URL: https://bank.gov.ua/admin_uploads/ article/Research_Financial_Literacy_Inclusion_Welfare_2021.pdf (дата звернення: 18.04.2025).
5. Riskalyze. Risk Number Methodology [Електронний ресурс]. URL: https://www.riskalyze.com (дата звернення: 18.04.2025).
6. Grable, J. E., Lytton, R. H. Financial Risk Tolerance Revisited: The Development of a Risk Assessment Instrument. Financial Services Review. 1999. Vol. 8, № 3. С. 163–181.
7. Kahneman, D., Tversky, A. Prospect Theory: An Analysis of Decision Under Risk. Econometrica. 1979. Vol. 47, Issue 2. С. 263–291.
8. Lo, A. W. Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press, 2017. 504 с.
9. Breiman, L. Random Forests. Machine Learning. 2001. Vol. 45(1). С. 5–32. DOI: 10.1023/A:1010933404324.
10. Van der Linden, W. J. Computerized Adaptive Testing: Theory and Practice. Springer, 2000. 324 с.
11. Gu, S., Kelly, B., Xiu, D. Empirical Asset Pricing via Machine Learning. The Review of Financial Studies. 2020. Vol. 33, Issue 5. С. 2223–2273. DOI: 10.1093/rfs/hhaa009.
12. Hambleton, R. K., Swaminathan, H., Rogers, H. J. Fundamentals of Item Response Theory. Sage Publications, 1991. 184 с.
13. Weiss, D. J. Computerized Adaptive Testing: Theory and Practice. Springer, 1988. 308 с.
14. Van Rossum, G., Drake, F. L. Python 3 Reference Manual. CreateSpace, 2009. 242 с.
15. Node.js Foundation. Node.js Documentation [Електронний ресурс]. URL: https://nodejs.org/en/docs/ (дата звернення: 18.04.2025).
16. Dorenskyi, O., Drobko, O., Drieiev, O. Improved Model and Software of the Digital Information Service of the Municipal Health Care Institutions. Центральноукраїнський науковий вісник. Технічні науки. 2022. Вип. 5(36), ч. 2. С. 3–10. DOI: 10.32515/2664-262X.2022.5(36).2.3-10.
17. Kachurivskyi, V., Kotovskyi, A., Lykhodid, T., Kachurivska, H., Dorenskyi, O. The Concept of Digital Transformation of Monitoring Scientific Activity of Participants in Educational Process of the Ukrainian HEI. Центральноукраїнський науковий вісник. Технічні науки. 2025. Вип. 11(42), ч. 1. С. 27–36. DOI: 10.32515/2664-262X.2025.11(42).1.27-36.
Copyright (c) 2025 Oleksandr Korniienko, Nataliia Kozub, Oleksandr Dorenskyi
Method and Technological Solution of an AI-Based Adaptive Investor Survey Service for Determining an Individual Risk Profile
About the Authors
Oleksandr Korniienko, PhD student, Kherson National Technical University, Kherson, Ukraine, ORCID: https://orcid.org/0009-0008-6234-784X, e-mail: aleksandrkornienko19992106@gmail.com
Nataliia Kozub, Candidate of Technical Sciences, Associate Professor, Kherson National Technical University, Kherson, Ukraine, https://orcid.org/0000-0002-0406-0161, e-mail: actinis1@gmail.com
Oleksandr Dorenskyi , Associate Professor, PhD in Information Technology (Candidate of Technical Sciences), Associate Professor of Cybersecurity and Software Academic Department, Central Ukrainian National Technical University, Kropyvnytskyi, Ukraine, https://orcid.org/0000-0002-7625-9022, e-mail: dorenskyiop@kntu.kr.ua
Abstract
Keywords
Full Text:
PDFReferences
1. Sharpe, W. F. (1999). Investments. Prentice Hall.
2. Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. John Wiley & Sons.
3. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
4. USAID Financial Sector Transformation Project. (2021). Financial literacy, financial inclusion, and financial well-being in Ukraine (2021). Retrieved April 18, 2025, from https://bank.gov.ua/admin_uploads/article/ Research_Financial_Literacy_Inclusion_Welfare_2021.pdf
5. Riskalyze. (n.d.). Risk number methodology. Retrieved April 18, 2025, from https://www.riskalyze.com
6. Grable, J. E., & Lytton, R. H. (1999). Financial risk tolerance revisited: The development of a risk assessment instrument. Financial Services Review, 8(3), 163–181.
7. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. https://doi.org/10.2307/1914185
8. Lo, A. W. (2017). Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press.
9. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
10. van der Linden, W. J. (2000). Computerized Adaptive Testing: Theory and Practice. Springer.
11. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223–2273. https://doi.org/10.1093/rfs/hhaa009
12. Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of Item Response Theory. Sage Publications.
13. Weiss, D. J. (1988). Computerized Adaptive Testing: Theory and Practice. Springer.
14. Van Rossum, G., & Drake, F. L. (2009). Python 3 Reference Manual. CreateSpace.
15. Node.js Foundation. (n.d.). Node.js Documentation. Retrieved April 18, 2025, from https://nodejs.org/en/docs/
16. Dorenskyi, O., Drobko, O., & Drieiev, O. (2022). Improved model and software of the digital information service of the municipal health care institutions. Tsentralʹnoukrainsʹkyi naukovyi visnyk. Tekhnichni nauky, 5(36), Pt. 2, 3–10. https://doi.org/10.32515/2664-262X.2022.5(36).2.3-10.
17. Kachurivskyi, V., Kotovskyi, A., Lykhodid, T., Kachurivska, H., & Dorenskyi, O. (2025). The concept of digital transformation of monitoring scientific activity of participants in educational process of the Ukrainian HEI. Tsentralʹnoukrainsʹkyi naukovyi visnyk. Tekhnichni nauky, 11(42), Pt. 1, 27–36. https://doi.org/10.32515/2664-262X.2025.11(42).1.27-36.
Citations
1. Sharpe, W. F. Investments. Prentice Hall, 1999. 962 с.
2. Markowitz, H. Portfolio Selection: Efficient Diversification of Investments. John Wiley & Sons, 1959. 351 с.
3. Murphy, K. P. Machine Learning: A Probabilistic Perspective. MIT Press, 2012. 1104 с.
4. USAID Проєкт «Трансформація фінансового сектору». Фінансова грамотність, фінансова інклюзія та фінансовий добробут в Україні у 2021 році [Електронний ресурс]. URL: https://bank.gov.ua/admin_uploads/ article/Research_Financial_Literacy_Inclusion_Welfare_2021.pdf (дата звернення: 18.04.2025).
5. Riskalyze. Risk Number Methodology [Електронний ресурс]. URL: https://www.riskalyze.com (дата звернення: 18.04.2025).
6. Grable, J. E., Lytton, R. H. Financial Risk Tolerance Revisited: The Development of a Risk Assessment Instrument. Financial Services Review. 1999. Vol. 8, № 3. С. 163–181.
7. Kahneman, D., Tversky, A. Prospect Theory: An Analysis of Decision Under Risk. Econometrica. 1979. Vol. 47, Issue 2. С. 263–291.
8. Lo, A. W. Adaptive Markets: Financial Evolution at the Speed of Thought. Princeton University Press, 2017. 504 с.
9. Breiman, L. Random Forests. Machine Learning. 2001. Vol. 45(1). С. 5–32. DOI: 10.1023/A:1010933404324.
10. Van der Linden, W. J. Computerized Adaptive Testing: Theory and Practice. Springer, 2000. 324 с.
11. Gu, S., Kelly, B., Xiu, D. Empirical Asset Pricing via Machine Learning. The Review of Financial Studies. 2020. Vol. 33, Issue 5. С. 2223–2273. DOI: 10.1093/rfs/hhaa009.
12. Hambleton, R. K., Swaminathan, H., Rogers, H. J. Fundamentals of Item Response Theory. Sage Publications, 1991. 184 с.
13. Weiss, D. J. Computerized Adaptive Testing: Theory and Practice. Springer, 1988. 308 с.
14. Van Rossum, G., Drake, F. L. Python 3 Reference Manual. CreateSpace, 2009. 242 с.
15. Node.js Foundation. Node.js Documentation [Електронний ресурс]. URL: https://nodejs.org/en/docs/ (дата звернення: 18.04.2025).
16. Dorenskyi, O., Drobko, O., Drieiev, O. Improved Model and Software of the Digital Information Service of the Municipal Health Care Institutions. Центральноукраїнський науковий вісник. Технічні науки. 2022. Вип. 5(36), ч. 2. С. 3–10. DOI: 10.32515/2664-262X.2022.5(36).2.3-10.
17. Kachurivskyi, V., Kotovskyi, A., Lykhodid, T., Kachurivska, H., Dorenskyi, O. The Concept of Digital Transformation of Monitoring Scientific Activity of Participants in Educational Process of the Ukrainian HEI. Центральноукраїнський науковий вісник. Технічні науки. 2025. Вип. 11(42), ч. 1. С. 27–36. DOI: 10.32515/2664-262X.2025.11(42).1.27-36.