Money laundering is an increasing problem for the global economy. To combat money laundering, banks use transaction monitoring models with particular thresholds to detect unusual transaction behaviour. However, it is a challenge to determine and evaluate the suitability of a threshold level to ensure that the risk of misclassification of transactions falls within the bank’s risk appetite. In the threshold tuning process, the suitability of a threshold level can be evaluated with a sample of the transactions below or above a threshold level which are reviewed by an analyst. One problem is that the review process of transactions during the threshold tuning process is timeconsuming. In addition, banks want to be able to quantify the risk of misclassification of transactions to determine whether this falls within their risk appetite. This underlines the need to develop a threshold tuning strategy to accelerate the threshold tuning process in which the risk of misclassification of transactions can be quantified to determine whether it falls within the bank’s risk appetite. To accelerate the threshold tuning process, a framework was developed and five threshold tuning strategies were established which evaluate the suitability of different threshold levels with a given strategy. In addition, several methods to determine a confidence interval were examined to quantify the risk of misclassification and to ensure that it falls within the bank’s risk appetite. The threshold tuning strategies were compared and evaluated on the required amount of reviews of transactions and the difference between the found and true threshold level using synthetic data sets. Overall, the bisection threshold tuning strategy is recommended, since this strategy resulted in the lowest number of required reviews of transactions and resulted in a small difference between the found and true threshold level. The results of the synthetic data sets were promising, but more experiments with preferably real transaction data or other distributions are required to further evolve and fully validate the framework and proposed bisection strategy. The work presented in this thesis contributed to a more risk-based approach to enhance the efficiency and effectiveness of the threshold tuning process of transaction monitoring models.

During oil drilling, the position of wells is indirectly determined by measurements near the drilling bit. However, the measurements are disturbed by noise causing errors in the measurements. It is important for large oil companies like Shell to determine the position of wells with a certain degree of uncertainty. Therefore, the main objective of this thesis is to assess the uncertainty in the nal well position of MSA corrected survey data. Two methods are investigated in this thesis to determine the positional uncertainty; Multi-Station Analysis in combination with the Minimum Curvature Method and Multi-Station Analysis in combination with the Covariance analysis method. In addition to Multi-Station Analysis, a second method has been investigated to determine sensor errors estimates and their uncertainty, namely the probabilistic estimation from Weighted Least Squares method. The methods with their assumptions are discussed, whereby the Covariance analysis method is discussed in more detail. Thereafter, the methods are tested with various test trajectories. The test results are compared to subsequently draw conclusions. It is clear that Multi-Station Analysis is the most reliable method to estimate sensor errors with their uncertainty. However, it was striking when the azimuth angle is not xed but varies, that certain errors are very poor. In addition, it could be concluded that the choice to systematically or randomly consider positional uncertainty has a major in uence on the positional uncertainty that arises. The thesis is completed with recommendations for further research.