Network-Based Models to Improve Credit Scoring Accuracy
| Autor: | Branka Hadji Misheva, Paolo Giudici, Valentino Pediroda |
|---|---|
| Přispěvatelé: | IEEE, Pediroda, Valentino, Giudici, Paolo, Hadji Misheva, Branka |
| Rok vydání: | 2018 |
| Předmět: |
credit scoring
Cart 050208 finance Offset (computer science) business.industry Computer science 05 social sciences Big data Disintermediation centrality Logistic regression network-based models Information asymmetry User experience design network-based model 0502 economics and business Econometrics 050207 economics business Credit risk |
| Zdroj: | DSAA |
| DOI: | 10.1109/dsaa.2018.00080 |
| Popis: | Technological advancements have prompted the emergence of peer-to-peer credit services which improve user experience and offer significant reductions in costs. These advantages may be offset by a higher credit risk, due to disintermediation and information asymmetries. We postulate that network-based information can be employed as a tool for reducing risks through an improved credit scoring model that increases the accuracy of default predictions. Our research assumption is proven by means of empirical analysis that shows how including network parameters in classical scoring algorithms, such as logistic regression and CART, does indeed improve predictive accuracy. |
| Databáze: | OpenAIRE |
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