Study of anomaly detection in diverse populations using probabilistic graphical models
Date
2020
Authors
Tarume, Isaac
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Abstract
Most credit card transactions are characterised by randomly changing patterns of behavioural characteristics of the card users involved.
The behavioural features tend to vary greatly due to the diversity inherent in most populations. In order to detect fraud and anomalies
efficiently in these typical diverse populations using machine learning
models, the models must be robust enough to capture the complex
user behaviours in their variety. To address these challenges, we used
base Hidden Markov Models (HMMs) and then a more richly expressive model called hierarchical Hidden Markov Model (HHMM) that
can capture more dimensions and latent variables, which enables it
to perform better. Furthermore, the model also reduces the learning
times, which is very important when it comes to evaluating diverse
population domain such as online credit card transactions as these
impacts the response times. We evaluate the performance of HHMM
in both individual and diverse population-based credit card transaction anomalies and labelled real-world timeseries data
Description
A thesis submitted to the Faculty of Science, University of the Witwatersrand, in fulfilment of the requirements for the degree of Master of Science, School of Computer Science and Applied Mathematics, 2020