Data Mining As A Financial Auditing Tool by Sirikulvadhana S. PDF

By Sirikulvadhana S.

The target of this thesis is to figure out if info mining instruments can directlyimprove audit functionality. the chosen try zone was once the pattern choice step of thetest of keep an eye on strategy. The learn facts was once in response to accounting transactionsprovided via AVH PricewaterhouseCoopers Oy. numerous samples have been extracted fromthe try out info set utilizing information mining software program and generalized audit software program and theresults evaluated. IBM's DB2 clever Miner for info model 6 was once chosen torepresent the information mining software program and ACL for home windows Workbook model five waschosen for generalized audit software.Based at the result of the try out and the reviews solicited from experiencedauditors, the belief is that, in the scope of this study, the result of datamining software program are extra fascinating than the result of generalized audit software.However, there's no proof that the knowledge mining procedure brings out materialmatters or current major enhancement over the generalized audit software program. Furtherstudy in a special audit quarter or with a extra entire facts set may yield a differentconclusion.

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Characteristics of Data Mining Packages Among a plethora of data mining packages available, some characteristics of data mining packages in general are: - Automated capability: The ideal objective of data mining is to automatically discover useful information from a data set. Though today’s data mining packages are still not completely automated, only the guidance to ensure that the results are interesting and evaluation of the results require intensive human efforts. - High complexity: How data mining algorithms work is sometimes mysterious because of their complexity.

Moreover, the target users of statistical tools are statisticians while data mining is designed for business people. This simply means that data mining tools are enhancement of statistical tools that blend many statistical algorithms together and possess a capability of handling more data in an automated manner as well as a userfriendly interface. The choice of an appropriate technique and timing depend on the nature of the data to be analyzed, the size of data sets and the type of methods to be mined.

Moreover, the performance of the model needs to be evaluated on a regular basis. The sequence of those phases is not rigid so moving back and forth between phases is allowed. Besides, the relationship could exist between any phases. At each review point, the next step has to be specified -- a step that can be either forward or backward. The lesson learned during and at the end of each phase should be documented as a guideline for the next phase. Besides, the documentation of all phases as well as the result of deployment should be documented for the next engagement.

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Data Mining As A Financial Auditing Tool by Sirikulvadhana S.


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