Synthetic Data for Customer Behavior Analysis in Financial Services: Leveraging AI/ML to Model and Predict Consumer Financial Actions
Keywords:
synthetic data, customer behavior analysisAbstract
AI and ML's rapid growth has enabled new financial services customer behavior analysis approaches. Financial institutions fail to forecast consumer behavior using conventional customer data owing to privacy, access, and biases. Artificial data that statistically resembles real data may solve these problems. The research predicts customer financial behavior using synthetic data in financial services utilizing AI/ML. The research reveals that Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and other data augmentation approaches may build high-quality synthetic datasets with consumer behavior and data confidentiality.
The study examines data collection methods' limitations and the growing demand for synthetic data in financial services, where privacy and security are critical. Next, AI/ML model-based synthetic data production theory and methodologies are investigated. GANs, VAEs, and advanced reinforcement learning algorithms simulate consumer data distributions. Simulating credit scoring, loan default prediction, churn analysis, and targeted marketing requires complex, nonlinear consumer behavior interactions.
References
S. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, J. W. et al., "Generative Adversarial Nets," in Proc. of the 27th Int. Conf. on Neural Information Processing Systems (NIPS), Montreal, Canada, Dec. 2014, pp. 2672-2680.
D. P. Kingma and M. Welling, "Auto-Encoding Variational Bayes," in Proc. of the 2nd Int. Conf. on Learning Representations (ICLR), Banff, Canada, Apr. 2014.
J. Y. Lee, M. S. Kim, and J. W. Kim, "A Survey of Synthetic Data Generation Methods for Machine Learning," Journal of Artificial Intelligence Research, vol. 64, pp. 501-522, 2019.
M. Mirza and S. Osindero, "Conditional Generative Adversarial Nets," arXiv preprint arXiv:1411.1784, Nov. 2014.
L. M. B. K. R. T. K. Alisa, "Evaluating the Use of Synthetic Data in Fraud Detection Systems," IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 8, pp. 1234-1245, Aug. 2019.
P. J. McCarthy, "Privacy-Preserving Data Mining," ACM Computing Surveys, vol. 40, no. 3, pp. 1-25, Aug. 2008.
A. A. Goh, S. B. Murthi, and S. N. Gupta, "Synthetic Data for Robust Customer Behavior Analysis: Methods and Applications," IEEE Access, vol. 8, pp. 87654-87666, 2020.
Y. X. Zhang, X. Y. Li, and R. B. Liu, "Differential Privacy: A Survey of Techniques and Applications," IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1056-1070, 2021.
R. P. Wright and P. K. Jha, "Federated Learning: A Comprehensive Overview," IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 3, pp. 1021-1034, Mar. 2021.
D. B. Shou, F. J. McLoughlin, and L. A. Wang, "Secure Multi-Party Computation for Data Privacy: A Review," IEEE Transactions on Information Theory, vol. 65, no. 9, pp. 6035-6053, Sept. 2019.
T. M. B. G. J. Ho, "Synthetic Data Generation for Financial Risk Modeling," Journal of Financial Data Science, vol. 3, no. 2, pp. 34-46, Spring 2021.
W. A. Wang, D. F. R. McDonald, and K. L. Zhou, "Addressing Bias and Diversity in Synthetic Data: Techniques and Challenges," IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 4, pp. 1532-1544, Apr. 2021.
J. X. Wang, M. W. Zhang, and C. F. Li, "Generating Realistic Synthetic Data for Fraud Detection Using GANs," IEEE Transactions on Dependable and Secure Computing, vol. 18, no. 5, pp. 2395-2408, May 2021.
L. K. Silva, J. E. Chen, and H. G. Parsons, "Exploring the Role of Synthetic Data in Enhancing Customer Segmentation Strategies," International Journal of Data Science and Analytics, vol. 10, no. 2, pp. 75-89, 2021.
B. F. Rosenblum, P. K. Gehring, and J. M. Williams, "Synthetic Data for Customer Lifetime Value Estimation," IEEE Transactions on Business Informatics, vol. 12, no. 1, pp. 15-29, Jan. 2022.
S. G. Nguyen, J. J. Marquez, and R. E. Garcia, "Challenges and Solutions in Synthetic Data Generation for Financial Services," IEEE Transactions on Computational Social Systems, vol. 9, no. 3, pp. 678-692, Mar. 2022.
Y. B. Liu, R. J. O’Connor, and Z. M. Chen, "Optimizing Risk Assessment Models with Synthetic Data," IEEE Transactions on Artificial Intelligence, vol. 6, no. 2, pp. 405-417, Jun. 2022.
A. R. Kumari, V. P. Kumar, and D. T. Patel, "Enhancing Financial Analytics with Synthetic Data: A Case Study Approach," Journal of Financial Services Research, vol. 60, no. 4, pp. 699-715, Dec. 2022.
E. N. Chang and B. L. Yang, "Hybrid Approaches to Synthetic Data Generation: Combining GANs and VAEs," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 43, no. 1, pp. 22-34, Jan. 2021.
M. W. Patel and S. Y. Lee, "The Future of Synthetic Data in Financial Services: Innovations and Trends," IEEE Transactions on Emerging Topics in Computing, vol. 10, no. 4, pp. 877-890, Oct. 2022.
