| October 1, 2020
Project Overview
Article [1] presents an elaborate fraud detection architecture that addresses several aspects of the problem, in particular the conceptual drift, i.e. the change in customers’ consumption habits. This architecture for machine learning is complex: it combines data encoding in the form of graphs, it uses memory networks [2] which are a special class of learning models, and attention mechanisms. The results presented in [1] are very good and we would like to confirm them by reimplementing this model to test it on real data.
References:
[1] Yang, K., & Xu, W. (2019, janvier 8). FraudMemory : Explainable Memory-Enhanced Sequential Neural Networks for Financial Fraud Detection. https://doi.org/10.24251/HICSS.2019.126 [2] Weston, J., Chopra, S., & Bordes, A. (2014). Memory Networks. ArXiv:1410.3916 [Cs, Stat]. http://arxiv.org/abs/1410.3916