Objectives

The analysis assists organizations in detecting possible fraudulent disbursements by using data mining and predictive modeling tools. Results summarized in a report can be used by examiners in their investigations. Payment types include: vendor bills, expense reports, payroll, grants, etc. The following chart demonstrates how analytical methods can be used to detect deviations from the norm. These anomalies colored in red can be further investigated to determine if the payments were fraudulent in nature.

Analysis and Financial Data

The following indicators and tests are applied to payment data:

• Payments actually deviate from the normal by using Anomaly Detection methods.
• Payments addressed to P.O. Boxes.
• Invoices from the same vendor but paid to multiple addresses.
• Invoices from multiple vendors paid to the same address.
• Invoices from the same vendor that were not sequentially numbered on the date submitted.
• Submission of an unreasonably large number of expense reports from the same employee over a period of time.
• Models built using different algorithms are used to analyze past payment activity (including known fraudulent payments); model(s) selected based on scored performance relative to fraud sensitivity and capability of limiting false alarms.

Benefits

The benefits that can be realized from utilizing these models:

• Reduce the substantial sum of money lost to fraudulent payments by assisting examiners in identify the possible perpetrators.
• Build expert models to detect instances of fraud as payments are being presented to the bank.