No title
This thesis explores the use of machine learning to detect anomalies in OTC derivatives. The purpose is to evaluate whether machine learning can be used in a new domain within the financial industry and to investigate whether anomaly detection can enhance the portfolio reconciliation process. The study compares three different approaches to anomaly detection: logistic regression, isolation forest
