Identifying clusters of anomalous payments in the salvadorian payment system

We develop an unsupervised methodology to group payments and identify possible anomalies. With our methodology, we identify clusters based on a set of network features, using transactional (unlabeled) information from a systemically important payment system of El Salvador. We first preprocess network features, such as degree and strength, through a principal components analysis we reduce the dimensionality of the newly defined data, then we place the main variables into clustering algorithms (k-means and DBSCAN) to analyze anomalous payments. We then analyze, these clusters using random forest to obtain the main network feature. Our results suggest that the proposed methodology works very well to detect anomalous payments, and it is very important to study the case of El Salvador, because of the recent restructuring of the Massive Payment System in El Salvador (promoted by the Transfer365 project), because the authorities want to increase financial inclusion. This change will make the SPM available to the public, to diversify services and incorporate more participants because, historically, it has operated with only three active participants. We expected that Transfer365 will interconnect the LBTR participants' systems with their banking core, the systems of the Ministry of Finance, and other authorized participants to channel large payment flows. Then, identifying possible anomalies through methodology will enhance risk monitoring and management by payment systems overseers

Fraud: and anomaly detection in healthcare: an unsupervised machine learning approach

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsFraud and abuse in healthcare are critical and cause significant damage. However, the auditing of healthcare encounters is cumbersome, and the detection of fraud and abuse is challenging and binds capacity. Data-driven fraud and anomaly detection models can help to overcome these issues. This work proposes several unsupervised learning methods to understand patterns and detect abnormal healthcare encounters which might be fraudulent or abusive. The ensemble of models is split into sub-processes and applied on a healthcare data set belonging to Future Healthcare group, a Portuguese group acting in health insurance. One major part of the ensemble is the implementation of the Isolation Forest algorithm, which achieves good results in precision and recall and detect new potential fraudulent abnormal behaviour. Due to unlabelled data and the application of unsupervised learning methods, the proposed model detects new fraudulent patterns instead of learning from existing patterns. Besides the model to predict whether new incoming medical encounters are fraudulent or abusive, this work illustrates a visual method to detect suspicious networks among medical providers. In addition, this work contains an approach to predict whether a customer will cancel the insurance based on anomalous behaviour. This internship report aims to contribute to science and be public, even though some parts could not be explained in detail due to confidentiality

Trajectory outlier detection: Algorithms, taxonomies, evaluation, and open challenges

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