Davit Bzhalava holds PhD degree in bioinformatics. Previously, he has been active with bio-medical research. Shaheer Mansoor holds a MSc in statistics and machine learning. Prior to Swedbank he has been worked as lead data scientist within the fin-tech and consulting industries. At Swedbank, along with rest of the Analytics & AI group, the speakers and their colleagues conduct extensive work and research to better leverage the data within the bank as well as creating frameworks for more efficient and customer-oriented banking processes using deep learning techniques and advanced hardware platforms.
Anomaly detection has numerous applications in a wide variety of fields. In banking, with ever growing heterogeneity and complexity, the difficulty of discovering deviating cases using conventional techniques and scenario definitions is on the rise. In our talk, we'll present an outline of Swedbank's ways of constructing and leveraging scalable pipelines based on Spark and Tensorflow in combination with an in-house tailor-made platform to develop, deploy and monitor deep anomaly detection models. In summary, this talk will present Swedbank's approach on building, unifying and scaling an end-to-end solution using large amounts of heterogeneous imbalanced data. In this talk we will include sections with the following topics: Feature engineering: transactions2vec; Anomaly detection and its applications in banking; Deep anomaly detection methods: Deep SVDD and Generative adversarial networks, Model overview and code snippets in Tensorflow estimator API; Model Deployment: An overview of how the different puzzle pieces outlined above are put together and operationalized to create and end-to-end deployment.