Evolutionary Paradigms in Predictive Analytics: Integrating Bayesian Inference and Machine Learning for Financial Risk Assessment and Consumer Behavioral Modeling

Da Eun Kang

Open Access

Evolutionary Paradigms in Predictive Analytics: Integrating Bayesian Inference and Machine Learning for Financial Risk Assessment and Consumer Behavioral Modeling

PDF

Da Eun Kang ¹ ,

⁴ Department of Artificial Intelligence and FinTech, Sungkyunkwan University, Suwon, South Africa

Abstract

The rapid digitization of financial services and consumer interactions has necessitated a shift toward more robust, scalable, and granular predictive modeling techniques. This research provides a comprehensive synthesis of contemporary methodologies in machine learning and statistical inference, specifically examining the efficacy of generative versus discriminative classifiers in the context of credit scoring, churn prediction, and hyper-personalized marketing. By integrating traditional Bayesian frameworks with modern distributed representation learning and advanced decision-tree ensembles, this study elucidates how firms navigate the complexities of high-dimensional, noisy data. The investigation highlights the evolution from classical Naive Bayes assumptions to complex Variational Auto-encoding and Bayesian neural networks, which allow for a more nuanced quantification of predictive uncertainty. Through a critical examination of literature spanning the last four decades, this paper explores the transition from static, rule-based credit evaluation to dynamic, data-driven systems capable of real-time behavioral adaptation. The analysis further addresses the challenges of algorithmic transparency, the impact of late-payment behaviors on institutional profitability, and the role of artificial intelligence in mitigating the energy burden of individual consumers. Ultimately, this work offers a roadmap for practitioners and researchers to bridge the gap between theoretical machine learning advancements and their pragmatic application in digital-age retail, insurance, and financial technology sectors. By scrutinizing the trade-offs between performance metrics and computational overhead, the research provides a rigorous foundation for developing next-generation decision-making engines.

Keywords

Predictive Analytics, Bayesian Inference, Financial Risk, Consumer Behavior

References

📄 Dutta, A., Kumar, S., and Basu, M. (2020). A Gated Recurrent Unit Approach to Bitcoin Price Prediction. Journal of Risk and Financial Management, 13(2):Article 23.

📄 Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar – A GARCH volatility analysis. Finance Research Letters, 16:85–92.

📄 Eck, D. and Schmidhuber, J. (2002). Finding temporal structure in music: blues improvisation with LSTM recurrent networks. In Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing, pages 747–756. IEEE.

📄 Economic Policy Uncertainty (2022). Daily Infectious Disease Equity Market Volatility Tracker.

📄 Joshi, R., Patel, N., Iyer, M. and Iyer, S., 2021. Leveraging Reinforcement Learning and Natural Language Processing for AI-Driven Hyper-Personalized Marketing Strategies. International Journal of AI ML Innovations, 10(1).

📄 Kiguchi, M., Saeed, W. and Medi, I., 2022. Churn prediction in digital game-based learning using data mining techniques: Logistic regression, decision tree, and random forest. Applied Soft Computing, 118, p.108491.

📄 Kingma, D.P., Welling, M. Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013).

📄 Kondapaka, K.K., 2022. Enhancing Customer Experience in Insurance Through AI-Driven Personalization. African Journal of Artificial Intelligence and Sustainable Development, 2(2), pp.246-289.

📄 Krishnan, G., Bhat, A. K., & Shah, J. (2025). Decision engine: Propensity prediction in the financial industry based on customer data features. In Artificial Intelligence and Sustainable Innovation (pp. 107-112). CRC Press.

📄 Kumar, V. and Ayodeji, O.G., 2021. E-retail factors for customer activation and retention: An empirical study from Indian e-commerce customers. Journal of Retailing and Consumer Services, 59, p.102399.

📄 Kumari, M., Sinha, P.C., Sinha, P.C., Hasnain, M.G., Sah, V.K. and Kumar, D., 2020. The Role Of AI In Robotic Marketing: Enhancing Customer Engagement And Conversions. Webology, 17(4).

📄 Lamrhari, S., El Ghazi, H., Oubrich, M. and El Faker, A., 2022. A social CRM analytic framework for improving customer retention, acquisition, and conversion. Technological Forecasting and Social Change, 174, p.121275.

📄 Leonard, K.J. Empirical Bayes analysis of the commercial loan evaluation process. Statistics & Probability Letters, 18 (1993), pp. 289-296.

📄 Lewis, D.D. Naive (Bayes) at forty: The independence assumption in information retrieval. European conference on machine learning, Springer (1998), pp. 4-15.

📄 Li, F.-C. The hybrid credit scoring strategies based on knn classifier. 2009 sixth international conference on fuzzy systems and knowledge discovery, Vol. 1, IEEE (2009), pp. 330-334.

📄 Liaw, A., Wiener, M., et al. Classification and regression by randomforest. R News, 2 (2002), pp. 18-22.

📄 Liu, H., Lobschat, L., Verhoef, P.C. and Zhao, H., 2019. App adoption: The effect on purchasing of customers who have used a mobile website previously. Journal of Interactive Marketing, 47(1), pp.16-34.

📄 Mariani, M.M. and Wamba, S.F., 2020. Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies. Journal of Business Research, 121, pp.338-352.

📄 McCallum, A., Nigam, K., et al. A comparison of event models for naive Bayes text classification. AAAI-98 workshop on learning for text categorization, vol. 752, Citeseer (1998), pp. 41-48.

📄 Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems (2013), pp. 3111-3119.

📄 Mimaroglu, S., Yang, Z. Using AI for predicting personalized energy burden and gas bill. (2022).

📄 Mishra, S. and Tyagi, A.K., 2022. The role of machine learning techniques in data driven insights.

📄 Moradi, S., Rafiei, F.M. A dynamic credit risk assessment model with data mining techniques: Evidence from Iranian banks. Financial Innovation, 5 (2019), p. 15.

📄 Neal, R.M. Bayesian learning for neural networks. vol. 118, Springer Science & Business Media (2012).

📄 Ng, A.Y., Jordan, M.I. On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. Advances in neural information processing systems (2002), pp. 841-848.

📄 Paul, S.Y., Devi, S.S., Teh, C.G. Impact of late payment on firms' profitability: Empirical evidence from Malaysia. Pacific-Basin Finance Journal, 20 (2012), pp. 777-792.

📄 Pawlowski, N., Brock, A., Lee, M.C., Rajchl, M., Glocker, B. Implicit weight uncertainty in neural networks. arXiv preprint arXiv:1711.01297 (2017).

📄 Quinlan, J.R. C4.5: Programs for machine learning. Elsevier (2014).

📄 Reichert, A.K., Cho, C.-C., Wagner, G.M. An examination of the conceptual issues involved in developing credit-scoring models. Journal of Business & Economic Statistics, 1 (1983), pp. 101-114.

📄 Rish, I., et al. An empirical study of the naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence, vol. 3 (2001), pp. 41-46.

📄 Safavian, S.R., Landgrebe, D. A survey of decision tree classifier methodology. IEEE Transactions on Systems, Man and Cybernetics, 21 (1991), pp. 660-674.

📄 Selz, D. From electronic markets to data driven insights. Electronic Markets (2020), pp. 1-3.

📄 Shridhar, K., Laumann, F., Liwicki, M. A comprehensive guide to Bayesian convolutional neural network with variational inference. arXiv preprint arXiv:1901.02731 (2019).

📄 Sokolova, M., Lapalme, G. A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45 (2009), pp. 427-437.

📄 Srinivasan, V., Kim, Y.H. Credit granting: A comparative analysis of classification procedures. The Journal of Finance, 42 (1987), pp. 665-681.

How to Cite

Da Eun Kang. (2026). Evolutionary Paradigms in Predictive Analytics: Integrating Bayesian Inference and Machine Learning for Financial Risk Assessment and Consumer Behavioral Modeling. Global Multidisciplinary Journal, 5(01), 172-178. https://www.grpublishing.org/journals/index.php/gmj/article/view/367

Download Citation

Most read articles by the same author(s)

<< < 1 2 3 4 5 6 7 8 9 10 > >>

Global Multidisciplinary Journal

Evolutionary Paradigms in Predictive Analytics: Integrating Bayesian Inference and Machine Learning for Financial Risk Assessment and Consumer Behavioral Modeling

Abstract

Keywords

References

How to Cite

Most read articles by the same author(s)

Similar Articles