Class-imbalanced data, in which some classes contain far more samples than others, is ubiquitous in real-world applications.
We devise a hybrid deep learning approach to solving tabular data problems.
In the environment of fair lending laws and the General Data Protection Regulation (GDPR), the ability to explain a model's prediction is of paramount importance.
Task oriented dialogue (TOD) requires the complex interleaving of a number of individually controllable components with strong guarantees for explainability and verifiability.
Temporal data are ubiquitous in the financial services (FS) industry -- traditional data like economic indicators, operational data such as bank account transactions, and modern data sources like website clickstreams -- all of these occur as a time-indexed sequence.
In data science, there is a long history of using synthetic data for method development, feature selection and feature engineering.
Financial transactions constitute connections between entities and through these connections a large scale heterogeneous weighted graph is formulated.
Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks.
With the rising interest in graph representation learning, a variety of approaches have been proposed to effectively capture a graph's properties.
There has been considerable growth and interest in industrial applications of machine learning (ML) in recent years.
In this paper, we present a novel application of representation learning to bipartite graphs of credit card transactions in order to learn embeddings of account and merchant entities.
Graph embedding is a popular algorithmic approach for creating vector representations for individual vertices in networks.