*Tid:* **7 juni 2012 kl 13.00-14.00.**
**Seminarierummet 3721, Institutionen för Matematik, KTH, Lindstedts väg 25, plan 7.**
Karta!
*Föredragshållare:*
Lennart Mumm
**Titel:**
Reject Inference in Online Purchases
(Examensarbete - Master thesis)
**Abstract**
As accurately as possible, creditors wish to determine if a potential debtor will repay
the borrowed sum. To achieve this mathematical models known as credit scorecards
quantifying the risk of default are used. In this study it is investigated whether the
scorecard can be improved by using reject inference and thereby include the characteristics
of the rejected population when refining the scorecard. The reject inference method used is
parcelling. Logistic regression is used to estimate probability of default based on
applicant characteristics. Two models, one with and one without reject inference, are
compared using Gini coefficient and estimated profitability. The results yield that, when
comparing the two models, the model with reject inference both has a slightly higher Gini
coefficient as well as showing an increase in profitability. Thus, this study suggests that
reject inference does improve the predictive power of the scorecard, but in order to verify
the results additional testing on a larger calibration set is needed.
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