Discussion
The set of analytics and heuristics used in CPAS will ultimately include a wide variety of algorithms ranging from flow-based rules to expert algorithms developed using techniques in knowledge engineering12.[13] These algorithms will be used both in the auditor platform, as analytical supplements, as well as impounded into software probes in the monitoring stage. Audit knowledge is needed to supplement the simple comprehension of the system being audited and to deal with the very complex stage of data gathering, analysis, and knowledge organization [Buchanan and Shortliffe, 1984] necessary for programming the auditing probes. The CPAS prototype was tested on two very large financial systems and is currently being applied to a third. The first application of the CPAS technology was an evolving system whose features changed rapidly. The idea was to put a prototype in place that contained basic analytics and then work with the auditors, as they used CPAS, to build more expertise into the system. It was found that only a few heuristics really existed, perhaps because of the nature of tools available to the auditor or because of the lack of longevity of auditors on the job. With the use of CPAS, auditors started to suggest heuristics that previously required cumbersome or not economically feasible audit procedures (e.g., time series tracking of discrepancies in a particular reconciliation). Another explanation for the limited number of heuristics identified is that the problem domain in question tended to be one with “diffuse knowledge” [Halper et al., 1989], where a large set of sources of knowledge were necessary and where knowledge ultimately was captured from a much wider set of experts than originally conceived.
