AbstractThe purpose of this thesis was to construct a program capable of detecting outliers, that is datapoints that do not follow trends that can be found within a dataset, by using Causal AI. Detectionof outliers has a very wide range of use since the term outliers can be adjusted to fit differenttypes of problems. This specific program can therefore be used in different manors to achievediverse beneficial results. In this specific thesis the program were used to detect suspicioustransactions which can eliminate unnecessary or wrongful purchases which can contribute toeconomic growth. The implementation of Causal AI was performed by using python and theDoWhy package. The Causal AI was used to determine and evaluate causal relationshipbetween input parameters in the dataset where outliers were to be detected. The identificationof outliers was then performed by letting the values of the data points be compared to theestablished causal relations. Data points that did not follow the causal flow was then labeled asoutliers. The result was a causal thinking machine learning model capable of detecting outliersas well as explaining the reason behind why the data point was labeled as an outlier. Theperformance was deemed to be satisfactory since the results seemed to follow reasonablecausal thinking as well as achieving similar results with different training data. The model turnedout to be very flexible with a wide range of uses. This flexibility was greater than what wasoriginally anticipated. Being able to replicate causal thinking using a machine learning model incombination with the models’ flexibility results in a program with such a wide area of use manydifferent problems can be automated. One example of this is the implementation of the programto make sure a sustainability policy is being followed resulting in contributing to a sustainabledevelopment in the world.