Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits
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.
2016. , 70 p.