Abstract
| Original language | English |
|---|---|
| Pages | 28-33 |
| Number of pages | 6 |
| Publication status | Published - 19 Jun 2018 |
| Event | 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018) - Stockholmsmässan, Stockholm, Sweden Duration: 14 Jul 2018 → 14 Oct 2018 Conference number: 3 https://sites.google.com/view/whi2018 |
Workshop
| Workshop | 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018) |
|---|---|
| Abbreviated title | WHI 2018 |
| Country | Sweden |
| City | Stockholm |
| Period | 14/07/18 → 14/10/18 |
| Other | Part of W17 of IJCAI-ECAI 2018 |
| Internet address |
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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: a case study. / Collaris, D.A.C.; Vink, Leo M.; van Wijk, J.J.
2018. 28-33 Paper presented at 2018 ICML Workshop on Human Interpretability in Machine Learning (WHI 2018), Stockholm, Sweden.Research output: Contribution to conference › Paper › Academic
TY - CONF
T1 - Instance-level explanations for fraud detection: a case study
AU - Collaris, D.A.C.
AU - Vink, Leo M.
AU - van Wijk, J.J.
PY - 2018/6/19
Y1 - 2018/6/19
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. Finally, we discuss the lessons learned and outline open research issues.
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. Finally, we discuss the lessons learned and outline open research issues.
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 - Paper
SP - 28
EP - 33
ER -