Synthetic Wallet-Transaction Generator for Risk-Model Training
Keywords:
synthetic data, diffusion models, financial transactions, fraud detection, data augmentation, seasonality modelingAbstract
The objective of this paper is to propose a diffusion-based generative models to generate synthetic financial transaction sequences based on temporal seasonality, merchant category codes (MCCs), and client behavioural archetypes. The proposed technique provides privacy-compliant, statistically consistent wallet traces that can be used to supplement transactional information.
Downloads
References
I. Goodfellow et al., "Generative adversarial nets," Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680, 2014.
X. Zhu, J. Goldberg, "Introduction to Semi-Supervised Learning," Synthesis Lectures on Artificial Intelligence and Machine Learning, vol. 3, no. 1, pp. 1–130, 2009.
Y. LeCun, Y. Bengio, G. Hinton, "Deep learning," Nature, vol. 521, pp. 436–444, 2015.
J. Friedman, T. Hastie, R. Tibshirani, The Elements of Statistical Learning, 2nd ed., Springer, 2009.
T. Chen and C. Guestrin, "XGBoost: A scalable tree boosting system," Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining, pp. 785–794, 2016.
A. Radford, L. Metz, S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," arXiv preprint arXiv:1511.06434, 2015.
R. Salakhutdinov and G. Hinton, "Deep Boltzmann machines," Proc. International Conference on Artificial Intelligence and Statistics, pp. 448–455, 2009.
C. Dwork, "Differential privacy," Automata, Languages and Programming, vol. 4052, pp. 1–12, 2006.
M. Abadi et al., "Deep learning with differential privacy," Proc. ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318, 2016.
Y. Kim et al., "Modeling consumer spending behavior with hidden Markov models," Journal of Consumer Research, vol. 35, no. 3, pp. 540–557, 2008.
P. Y. Chen et al., "Rule-based simulation of credit card transactions for fraud detection," Journal of Financial Crime, vol. 23, no. 3, pp. 575–591, 2016.
M. J. D. Powell, "Approximation theory and numerical analysis," in Handbook of Numerical Analysis, vol. 1, P. G. Ciarlet and J. L. Lions, Eds., North-Holland, 1990.
J. Song and S. Ermon, "Generative modeling by estimating gradients of the data distribution," arXiv preprint arXiv:1705.01964, 2017.
H. He, E. A. Garcia, "Learning from imbalanced data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 9, pp. 1263–1284, 2009.
M. Abadi et al., "TensorFlow: Large-scale machine learning on heterogeneous systems," arXiv preprint arXiv:1603.04467, 2016.
J. Shokri et al., "Membership inference attacks against machine learning models," Proc. IEEE Symposium on Security and Privacy, pp. 3–18, 2017.
European Parliament and Council, "General Data Protection Regulation (GDPR)," Official Journal of the European Union, 2016.
S. J. Stolfo et al., "Credit card fraud detection using meta-learning: Issues and initial results," Proc. AAAI Workshop on AI Approaches to Fraud Detection and Risk Management, 1997.
P. Domingos, "A few useful things to know about machine learning," Communications of the ACM, vol. 55, no. 10, pp. 78–87, 2012.
T. Mikolov et al., "Distributed representations of words and phrases and their compositionality," Advances in Neural Information Processing Systems, vol. 26, pp. 3111–3119, 2013.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
