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This study provides management, accountants, and auditors with immediately applicable knowledge on supplier payment error discovery using a data warehouse. Despite the universality of supplier payment processing across government and non-government sectors, the associated error risks have received little attention in the academic literature and only limited attention in the practitioner literature. This paper seeks to articulate the data mining methods needed to perform such audits. A case study approach was used to analyze 15 client auditees in five countries using a variety of accounts payable systems. Action research methodology brought rigor to the development of incremental knowledge of error hypotheses and detection techniques. In this way, the prior knowledge gained from each case enriched the subsequent iterations. Even among clients whose management presumed human and system controls were robust, findings showed an average 0.19 percent error rate in payments to suppliers. These were mostly recoverable as cash refunds or credit notes. The audit cost-to-benefit ratio averaged 1:33, suggesting clients received financial returns 33 times higher than the cost of the audit, on average. Concurrently, internal controls were also hardened in accounts payable, tax compliance, and fraud prevention to secure additional future cost savings. Besides material cash recoveries and the occasional discovery of fraud, incremental lessons were also learned in data warehouse construction, data cleansing, privacy assurance, and tax compliance.

DETECTING SUPPLIER PAYMENT ERRORS AND FRAUD USING A DATA WAREHOUSE AUDIT APPROACH