A Prototype for detecting procurement fraud using data mining techniques: case of banking industry in Kenya

Muriithi, Francis W.
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Strathmore University
Fraud is a million-dollar business, and it is increasing every year. The numbers are shocking, all the more because over one third of all frauds are detected by 'chance' means. Given that the procurement process is part of the expenditure cycle that culminates with the payment of cash, it is rife with potential for exposing an organization to fraud and embezzlement. Today, whistle blowing, is the most common fraud detection method. However, this method does not proactively search for misconduct. As a result, a fraud detected through this means tends to be caught too late and after the organization has already lost millions of dollars. In this study, we propose a data driven fraud detection prototype to reduce the duration and cost of procurement fraud in Kenya’s banking industry. To achieve this, electronic data from the HR and ERP systems was analysed by the prototype using data mining techniques to identify potential fraud misconduct. The data mining techniques applied included rule-based, fuzzy string-matching, and z-score outlier analytics to crossmatch the data against procurement fraud red flag indicators. Thereafter, the prototype generated potential frauds notifications to the organization’s audit, risk, or forensic department for further investigation. The outcome of the investigation done by the audit team was also captured by the prototype to increase the accuracy of fraud detection and reduce future false positive alerts.
Submitted in partial fulfilment of the requirements for the degree of Master of Science in Information Technology at Strathmore University