SCAN: A Case-Based Reasoning Model for Generating Information System Control Recommendations

This paper describes SCAN, a case-based reasoning model for generating information system control recommendations. The purpose of the paper is to explain how a case-based reasoner may be used to support inexperienced information system auditors in evaluating controls and proposing audit recommendations. As a case-based reasoner, SCAN functions by reasoning by analogy to similar past cases. SCAN models audit experience as traces of past cases which are stored in a case knowledge base. In addition to a database of past audit cases, SCAN consists of indices for storing and retrieving cases, a similarity metric for measuring case similarity, and rules for using similar cases to generate control recommendations. SCAN uses past cases to remind the user of previous control failures, to set expectations about case features and controls, to use as a pattern against which to compare a client's controls and to help justify or explain its recommendations. SCAN's recommendations were judged to be more like those of an experienced auditor than either a student or a textbook model.

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