Abstract
Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid a fraud detection team in their work. To this end, we designed two novel dashboards combining various state-of-the-art explanation techniques. These enable the domain expert to analyze and understand predictions, dramatically speeding up the process of filtering potential fraud cases.
| Original language | English |
|---|---|
| Number of pages | 1 |
| Publication status | Published - 27 Nov 2018 |
| Event | 5th Data Science Summit (DSSE 2018) - Muziekgebouw Frits Philips, Eindhoven, Netherlands Duration: 27 Nov 2018 → 27 Nov 2018 Conference number: 5 |
Other
| Other | 5th Data Science Summit (DSSE 2018) |
|---|---|
| Abbreviated title | DSSE 2018 |
| Country | Netherlands |
| City | Eindhoven |
| Period | 27/11/18 → 27/11/18 |
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Keywords
- Interpretability
- Explanation
- Machine learning
- Sensitivity analysis
- Local rule extraction
- Instance-level explanations
- Fraud detection
- Case study
Cite this
Collaris, D. A. C., Vink, L. M., & van Wijk, J. J. (2018). Instance-level explanations for fraud detection. Poster session presented at 5th Data Science Summit (DSSE 2018), Eindhoven, Netherlands.