Instance-level explanations for fraud detection

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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 languageEnglish
Number of pages1
Publication statusPublished - 27 Nov 2018
Event5th Data Science Summit (DSSE 2018) - Muziekgebouw Frits Philips, Eindhoven, Netherlands
Duration: 27 Nov 201827 Nov 2018
Conference number: 5

Other

Other5th Data Science Summit (DSSE 2018)
Abbreviated titleDSSE 2018
CountryNetherlands
CityEindhoven
Period27/11/1827/11/18

Keywords

  • Interpretability
  • Explanation
  • Machine learning
  • Sensitivity analysis
  • Local rule extraction
  • Instance-level explanations
  • Fraud detection
  • Case study

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  • 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.