when the AP model detects a seeded error in the hold-out sample, we examine the consequences on subsequent error detection if the seeded error is corrected by substitution of the original value before the model is used again. For comparison purpose, we test how our candidate CE models work with and without real time error correction. Unlike continuous auditing, anomalies are detected but usually not corrected immediately in traditional auditing. To simulate this scenario, we don’t correct any errors we seeded in the hold-out sample even if the AP model detects them. [Insert here] One can argue that it is possible in principle to utilize error correction in traditional auditing. However, since traditional auditors will have to investigate all the anomalies at the end of the audit period, the time pressure of the audit is likely to prevent them from rerunning their analytical procedures and conducting additional investigations over and over again to detect additional anomalies after some previously detected errors are corrected. Overall, the results show lower false negative error rates for all CE models with error correction, especially when the error magnitude is large (100% or more). This finding is consistent with the prior expectation. Under some rare circumstances, the non-correction model has slightly better detection rate than the correction model. All these exceptions occur when the error magnitude is small (no larger than 50 percent). Higher false positive error rates, mostly when error magnitudes are large, are observed for the error-correction models, which means that the error-correction models can detect more anomalies but at a cost of triggering more false alarms. However, a further investigation reveals that the false alarms are mostly caused by certain records in the holdout sample, which are called in this study anomalies”. These original anomalies are very likely to be caused by measurement “original errors since our dataset consists of un-audited operational data. This measurement error problem with non-audited data is also reported by previous studies (Kinney and Salamon 1982, Wheeler and Pany 1990). Because the non-correction model would not correct those undetected errors, the impact of original anomalies, whose values remain constant, would be eclipsed by the increase in seeded error magnitude. Therefore, the non-correction model would trigger fewer false alarms when the magnitude in seeded error increases. On the other hand, the impact of original anomalies would not decrease as the error-correction model would correct all detected errors.
when the AP model detects a seeded error in the hold-out sample, we examine the consequences on subsequent error detection if the seeded error is corrected by substitution of the original value before the model is used again. For comparison purpose, we test how our candidate CE models work with and without real time error correction. Unlike continuous auditing, anomalies are detected but usually not corrected immediately in traditional auditing. To simulate this scenario, we don’t correct any errors we seeded in the hold-out sample even if the AP model detects them. [Insert here] One can argue that it is possible in principle to utilize error correction in traditional auditing. However, since traditional auditors will have to investigate all the anomalies at the end of the audit period, the time pressure of the audit is likely to prevent them from rerunning their analytical procedures and conducting additional investigations over and over again to detect additional anomalies after some previously detected errors are corrected. Overall, the results show lower false negative error rates for all CE models with error correction, especially when the error magnitude is large (100% or more). This finding is consistent with the prior expectation. Under some rare circumstances, the non-correction model has slightly better detection rate than the correction model. All these exceptions occur when the error magnitude is small (no larger than 50 percent). Higher false positive error rates, mostly when error magnitudes are large, are observed for the error-correction models, which means that the error-correction models can detect more anomalies but at a cost of triggering more false alarms. However, a further investigation reveals that the false alarms are mostly caused by certain records in the holdout sample, which are called in this study “original anomalies”. These original anomalies are very likely to be caused by measurement errors since our dataset consists of un-audited operational data. This measurement error problem with non-audited data is also reported by previous studies (Kinney and Salamon 1982, Wheeler and Pany 1990). Because the non-correction model would not correct those undetected errors, the impact of original anomalies, whose values remain constant, would be eclipsed by the increase in seeded error magnitude. Therefore, the non-correction model would trigger fewer false alarms when the magnitude in seeded error increases. On the other hand, the impact of original anomalies would not decrease as the error-correction model would correct all detected errors.
Analytical%20Procedures%20for%20Continuous%20Data%20Level%20Auditing%20Continuity%20Equations..docx