Drilling into dashboards: responding to computer recommendation in fraud analysis

Research output: Contribution to journalArticle

Authors

  • Natan Morar
  • Irina Skarbovsky
  • Alexander Artikis
  • Ivo Correai

External organisations

  • IBM Research
  • N.C.S.R. Demokritos
  • Feedzai

Abstract

Credit card fraud analysis is almost entirely automated. However, there may be occasions when a human analyst is required to intervene. In this paper, we consider situations in which a transaction triggers an automated alert but not sufficiently to allow automated response. On such occasions, automated analysis makes a recommendation as to the fraud pattern that has been identified and the human analyst decides, if this recommendation is correct and what action to take. In order to support the analyst, a “dashboard” can be used to display information that is relevant to the fraud pattern. Thus, a computer could analyze transaction data, define this as a known fraud pattern, and then present the analyst with a dashboard to illustrate how the transaction might fit the pattern. We explore the efficiency with which people respond to the computer's recommendation, and whether computer confidence has an impact on this response. We define efficiency in terms of information search: the user will either have the relevant information on screen or will need to drill-into a dashboard (i.e., open additional windows for information). The results show that participants adapt their decision making to the confidence of the automated support, and, although they drill-down even when not required, are efficient in terms of time spent looking at relevant information.

Details

Original languageEnglish
Pages (from-to)633-641
Number of pages9
JournalIEEE Transactions on Human-Machine Systems
Volume49
Issue number6
Publication statusPublished - Dec 2019

Keywords

  • Automation transparency, credit card fraud, decision support systems, human-automation interaction