Digital Twins of Consumers: Building Predictive Economic Identities for Autonomous Financial Decision Systems

The convergence of artificial intelligence, behavioural data, and financial systems is rapidly converging, creating the potential to transform the fundamental paradigm of the idea of reactive analytics based on historical data to predictive simulation based on current evolving models. The paper suggests the notion of consumer digital twins: dynamic, data-driven computational replicas of individuals designed to model, simulate and forecast future economic behaviour under a broad range of conditions and time horizons.
In contrast to older predictive models, which assume consumers are a static entity that is defined solely by past transactions, digital twins are continually updating their behavioural, transactional, contextual and psychographic data to generate proactive simulations of financial decisions. This paper builds a prototype three-layered model of building consumer digital twins and illustrates how these can be applied in four critical areas, namely, predicting lifetime customer value, estimating the risk of financial default, detecting behavioural drift and simulating response to macroeconomic shocks such as income cuts and inflation spikes.
According to the results of empirical simulations, the digital twin approach has higher performance in both accuracy (reaching up to 92% prediction accuracy as compared to approximately 70% with the use of conventional static credit models) and adaptability, especially under volatile economic conditions. The implications of the findings to the banking, insurance, wealth management, and personalized marketing sectors are quite profound. Nevertheless, the use of predictive economic identities elicits very important concerns on data ownership, consent schemes, and the bias of algorithms, surveillance capitalism, and the loss of personal autonomy. The paper contends that consumer digital twins will serve as core infrastructure of next-generation economic systems, and argues that a pressing redefinition of identity, privacy, and decision-making governance is necessary in the digital economy

Consumer Digital Twin; AI-Driven Economic Simulation; Dynamic Behavioural Modelling; Financial Decision Forecasting

1. Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589–609. [Google Scholar] [Crossref]

2. Arrieta, A. B., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., ... & Herrera, F. (2020). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. [Google Scholar] [Crossref]

3. Barocas, S., Hardt, M., & Narayanan, A. (2019). Fairness and Machine Learning. fairmlbook.org. [Google Scholar] [Crossref]

4. Brown, I., & Mues, C. (2012). An experimental comparison of classification algorithms for imbalanced credit scoring data sets. Expert Systems with Applications, 39(3), 3446–3453. [Google Scholar] [Crossref]

5. Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 785–794). [Google Scholar] [Crossref]

6. Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. In Proceedings of the 3rd Innovations in Theoretical Computer Science Conference (pp. 214–226). [Google Scholar] [Crossref]

7. European Parliament. (2018). General Data Protection Regulation (GDPR). Official Journal of the European Union. [Google Scholar] [Crossref]

8. Government of India. (2023). Digital Personal Data Protection Act. Ministry of Electronics and Information Technology. [Google Scholar] [Crossref]

9. Grieves, M. (2002). Digital twin: Manufacturing excellence through virtual factory replication. White Paper. [Google Scholar] [Crossref]

10. Hildebrandt, M. (2015). Smart Technologies and the End(s) of Law: Novel Entanglements of Law and Technology. Edward Elgar Publishing. [Google Scholar] [Crossref]

11. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263–291. [Google Scholar] [Crossref]

12. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787. [Google Scholar] [Crossref]

13. Lanier, J. (2018). Ten Arguments for Deleting Your Social Media Accounts Right Now. Henry Holt and Company. [Google Scholar] [Crossref]

14. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. [Google Scholar] [Crossref]

15. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. C. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research. European Journal of Operational Research, 247(1), 124–136. [Google Scholar] [Crossref]

16. Posner, E. A., & Weyl, E. G. (2018). Radical Markets: Uprooting Capitalism and Democracy for a Just Society. Princeton University Press. [Google Scholar] [Crossref]

17. Shafto, M., Conroy, M., Doyle, R., Glaessgen, E., Kemp, C., LeMoigne, J., & Wang, L. (2010). DRAFT modeling, simulation, information technology & processing roadmap: Technology area 11. NASA. [Google Scholar] [Crossref]

18. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., & Sui, F. (2018). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9), 3563–3576. [Google Scholar] [Crossref]

19. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press. [Google Scholar] [Crossref]

20. Xu, L., Skoularidou, M., Cuesta-Infante, A., & Veeramachaneni, K. (2019). Modeling tabular data using conditional GAN. In Advances in Neural Information Processing Systems, 32 [Google Scholar] [Crossref]

• Assessing Tax Revenue and FDI on Economic Growth: Employing the Moderating Role of Institutional Quality in Ghana.