Abstract
Original language | English |
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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) |
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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
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Instance-level explanations for fraud detection. / Collaris, D.A.C.; Vink, Leo M.; van Wijk, J.J.
2018. Poster session presented at 5th Data Science Summit (DSSE 2018), Eindhoven, Netherlands.Research output: Contribution to conference › Poster › Academic
TY - CONF
T1 - Instance-level explanations for fraud detection
AU - Collaris, D.A.C.
AU - Vink, Leo M.
AU - van Wijk, J.J.
PY - 2018/11/27
Y1 - 2018/11/27
N2 - 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.
AB - 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.
KW - Interpretability
KW - Explanation
KW - Machine learning
KW - Sensitivity analysis
KW - Local rule extraction
KW - Instance-level explanations
KW - Fraud detection
KW - Case study
M3 - Poster
ER -