- Tytuł:
- Feature Engineering for Anti-Fraud Models Based on Anomaly Detection
- Autorzy:
- Przekop, Damian
- Powiązania:
- https://bibliotekanauki.pl/articles/2075464.pdf
- Data publikacji:
- 2020
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
fraud detection
application fraud
feature engineering
anomaly detection
risk modeling - Opis:
- The paper presents two algorithms as a solution to the problem of identifying fraud intentions of a customer. Their purpose is to generate variables that contribute to fraud models’ predictive power improvement. In this article, a novel approach to the feature engineering, based on anomaly detection, is presented. As the choice of statistical model used in the research improves predictive capabilities of a solution to some extent, most of the attention should be paid to the choice of proper predictors. The main finding of the research is that model enrichment with additional predictors leads to the further improvement of predictive power and better interpretability of anti-fraud model. The paper is a contribution to the fraud prediction problem but the method presented may generate variable input to every tool equipped with variable- selection algorithm. The cost is the increased complexity of the models obtained. The approach is illustrated on a dataset from one of the European banks.
- Źródło:
-
Central European Journal of Economic Modelling and Econometrics; 2020, 3; 301-316
2080-0886
2080-119X - Pojawia się w:
- Central European Journal of Economic Modelling and Econometrics
- Dostawca treści:
- Biblioteka Nauki
Artykuł