Employee Risk Scoring as a Tool for Preventing Internal Fraud in Bank
a. Heri Supriyadi b. D.S. Priyarsono c. Kumo Ratih
School of Business, IPB University, Doctorate Program of Business Management
Abstract
One of factors causing operational risk event in Indonesian banks is human factor, one of those is internal fraud. This paper is aimed to determine which factors significantly affecting employee to commit internal fraud, and to set simple model for scoring risk of employee to measure likelihood of committing internal fraud. Data was collected from human capital management system in a bank comprising personal identity, static data, liabilities, and other related data recorded at the system. The collected data was discussed through Focus Group Discussion (discussion members from several different departments in the bank) to be taken as important variables to affect employee to commit internal fraud. The variables were statistically tested to be determined as important and significant variables of model to score employee risk scoring. The output of scoring employee risk was categorized as Low, Low to Moderate, Moderate, Moderate to High and High. The result of correlation test shows that there are 9 variables significantly influencing employee to conduct fraud. Those comprise credit card outstanding, time length of employment, take home pay, time length of duty rotation, percentage of day leave taken, overtime job taken, internally soft loan outstanding, age and organization position. Based on the statistically testing, alternative solution by using expert judgment is proposed, i.e. using weighted scoring. The alternative solution applied to several employees in branch offices result then was confirmed to their managers. The selected managers confirmed that the alternative solution result is rationally accepted and could be used as a tool to measure likelihood level of employee committing fraud described as likelihood levels stated above, from Low to High. The next study with more detailed variables is recommended to more precisely estimate likelihood level of employees committing fraud. The Big Data also can be potentially used as more detailed and accurate data.
Keywords: Employee Risk Scoring, Fraud, Bank
Topic: Finance and Risk Management