Data Mining As A Financial Auditing Tool by Sirikulvadhana S.

By Sirikulvadhana S.

The target of this thesis is to figure out if facts mining instruments can directlyimprove audit functionality. the chosen try out region used to be the pattern choice step of thetest of keep watch over approach. The study info was once in line with accounting transactionsprovided through AVH PricewaterhouseCoopers Oy. quite a few samples have been extracted fromthe try out info set utilizing facts mining software program and generalized audit software program and theresults evaluated. IBM's DB2 clever Miner for facts 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 attempt and the evaluations solicited from experiencedauditors, the realization is that, in the scope of this examine, the result of datamining software program are extra attention-grabbing than the result of generalized audit software.However, there isn't any proof that the knowledge mining approach brings out materialmatters or current major enhancement over the generalized audit software program. Furtherstudy in a distinct audit quarter or with a extra whole info set may well yield a differentconclusion.

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2: Example of association rule Some techniques for dependency analysis are nonlinear regression, rule induction, statistic sampling, data normalization, Apriori algorithm, Bayesian networks and data visualization. 3. Classification and Prediction Classification is the process of finding models, also known as classifiers, or functions that map records into one of several discrete prescribed classes. It is mostly used for predictive purpose. Typically, the model construction begins with two types of data sets -training and testing.

The advantage of CART is that it automates the - 39 - pruning process by cross validation and other optimizers. It is capable of handling missing data and it sets the unqualified records apart from the training data sets. CHAID is another decision tree algorithm that uses contingency tables and the chi-square test to create the tree. The disadvantage of CHAID comparing to CART is that it requires more data preparation process. 3. Neural Networks Nowadays, neural networks, or more correctly the artificial neural networks (ANN), attract the most interest among all data mining algorithms.

The only certain data available for all audit engagements is general ledger or accounting transactions for the audited period. Therefore, this section is focused on what data mining can contribute when data available is only current period general ledger. As a general note, data mining methods that require historical data as a training data set cannot be done. Examples are classification and prediction, dependency analysis and evolution analysis. However, in some cases, data from previous months can be used as training data sets for the following months.

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