Detecting anomalies and novel events is vital to the financial industry. These events may often be indicative of illegal activities such as credit card fraud, identity theft, network intrusion, and money laundering. Left unchecked, these types of anomalies can cost organizations billions of dollars, as well as their brands' reputation. A number of new ideas are emerging to tackle this problem, including semi-supervised learning methods, deep learning based approaches and network/graph based solutions. These approaches must often be able to work in real-time and be able handle large volumes of data. The purpose of this workshop is to bring together researchers and practitioners to discuss both the anomalous events that are harmful to financial institutions, but, more importantly, potential solutions to combat them. This half-day workshop will consist of a series of sessions, including spotlight talks and short paper presentations, which will showcase ongoing research and novel applications.

We invite papers on anomaly and novelty detection with applications for the financial industry. Topics of interest include, but are not limited to, the following:
Unsupervised approaches
Semi-supervised approaches
Spectral methods
Model-based algorithms (including neural nets, Bayesian nets, SVM’s, rule-based)
Statistical methods
Information theoretic approaches
Graph/Network algorithms
Use-Cases:
Anti-Money Laundering (AML)
Fraud
Identity Theft and Fake Account Registration
Risk Modeling
Account Takeover
Promotion Credit Abuse
Customer Behavior Analytics
Cyber Security