| Summary: | The use of predictive analysis is becoming more commonplace with each passing day, which lends increased credence to the fact that even governmental institutions should adopt it. Kronofogden is in the middle of a digitization process and is therefore in a unique position to implement predictive analysis into the core of their operations. This project aims to study if methods from predictive analysis can predict how many debts will be received for a first-time debtor, through the use of uplift modeling. The difference between uplift modeling and conventional modeling is that it aims to measure the difference in behavior after a treatment, in this case guidance from Kronofogden. Another aim of the project is to examine whether the scarce literature about uplift modeling have it right about how the conventional two-model approach fails to perform well in practical situations. The project shows similar results as Kronofogden’s internal evaluations. Three models were compared: random forests, gradient-boosted models and neural networks, the last performing the best. Positive uplift could be found for 1-5% of the debtors, meaning the current cutoff level of 15% is too high. The models have several potential sources of error, however: modeling choices, that the data might not be informative enough or that the actual expected uplift for new data is equal to zero.
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