Masters Degrees (Logistics)

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Browsing Masters Degrees (Logistics) by Subject "Automated tellers"

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    Automated payment fraud detection using logistic regression and support vector machines
    (Stellenbosch : Stellenbosch University, 2021-03) Thetard, Heinrich Mathias; Nel, J. H.; Stellenbosch University. Faculty of Economic and Management Science. Dept. of Logistics.
    ENGLISH ABSTRACT: The financial technology sector is a fast moving environment. There are many innovations I nthe automation and efficiency spheres where human intervention is required less and processing speed is rapidly increasing. In the payments space this is evident as payments are processed faster each year with the vast majority of these transactions driven automatically. This has opened up a platform for fraudsters to operate on. The use of Machine Learning (ML) in fraud detection has grown in popularity. Two methods, logistic regression (LR) and support vector machines (SVMs), are used to identify fraud and are investigated in this thesis. LR is less complex as compared to SVMs, but SVMs have unique situations where it will outperform any other ML model [31]. Either method is assessed based on application conditions and measured based on a certain set of confusion matrix based metrics. The two methods are applied to a data set from a bank which participates in the automated payment environment. It was evident that the sample proportions selected had a major impact on the model performance especially with regards to sensitivity and specificity. This was an exercise of fraud identification where sensitivity is the most important. This may not be the case for all data sets and environments as the cost to investigate false positives may be higher than the actual cost of fraud prevented. Condition testing and post model application diagnostics were applied in this research. It was evident principle component analysis (PCA) feature selection was inferior to stepwise feature selection. The relatively poor performance of the PCA feature selection models is due to a loss of information when variables are removed when choosing the components. When considering the odds ratios for LR, there were several variables that were protective factors and others that were risk factors. These factors either increased or decreased the odds of a case being fraudulent. It was found that when a debit order (DO) was associated with an older person it was more likely to be fraudulent than when the DO was associated with a younger person. It was also found that if a DO had a value of R99 or R45 then the odds of the case being fraudulent would increase several-fold. LR models produced equivalent results to the more complex SVM models with a much better run time. From a practical point of view, this means that LR is preferred on larger data sets.