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Do high customer bank deposits incite management fraud? Examining causes of management fraud in the Nigerian banking sector

Paul Ojeaga, Ochei Ailemen Ikpefan, Deborah Odejimi

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Abstract

The study investigates factors that incite fraud in the banking sector in Nigeria, using times series data for fraud obtained from CBN data from 1998 to 2010. It was found that high bank deposit were primarily responsible for a high rise fraudulent occurrences in the Nigerian banking sector particularly management fraud, some other factors that were also jointly responsible for these occurrences include high interest rates, low commercial bank lending and poor oversight function by the Central Bank and other financial regulatory agencies. The method of estimation used in the study is the quantile regression estimation method which is a non parametric estimation method based on the premise that the sample median will tend to that of the distributional median, it presents some obvious advantages over OLS (ordinary least squares) estimates, since the results are robust in the presence of outliers and heteroscedastic errors in the response measurement and allows for the exploration of other central tendencies and statistical dispersion properties of the dataset Machando and Silva (2013). The results are robust even after controlling for presence of heterscedastic error in the response measurement as well re-sampling the dataset (conducted by the bootstrapped quantile regression technique). Further explanation is also provided for the implication of variables identified to drive fraud occurrences using kernel density estimation.

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The study investigates factors that incite fraud in the banking sector in Nigeria, using times series data for fraud obtained from CBN data from 1998 to 2010. It was found that high bank deposit were primarily responsible for a high rise fraudulent occurrences in the Nigerian banking sector particularly management fraud, some other factors that were also jointly responsible for these occurrences include high interest rates, low commercial bank lending and poor oversight function by the Central Bank and other financial regulatory agencies. The method of estimation used in the study is the quantile regression estimation method which is a non parametric estimation method based on the premise that the sample median will tend to that of the distributional median, it presents some obvious advantages over OLS (ordinary least squares) estimates, since the results are robust in the presence of outliers and heteroscedastic errors in the response measurement and allows for the exploration of other central tendencies and statistical dispersion properties of the dataset Machando and Silva (2013). The results are robust even after controlling for presence of heterscedastic error in the response measurement as well re-sampling the dataset (conducted by the bootstrapped quantile regression technique). Further explanation is also provided for the implication of variables identified to drive fraud occurrences using kernel density estimation.

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Available abstract

The study investigates factors that incite fraud in the banking sector in Nigeria, using times series data for fraud obtained from CBN data from 1998 to 2010. It was found that high bank deposit were primarily responsible for a high rise fraudulent occurrences in the Nigerian banking sector particularly management fraud, some other factors that were also jointly responsible for these occurrences include high interest rates, low commercial bank lending and poor oversight function by the Central Bank and other financial regulatory agencies. The method of estimation used in the study is the quantile regression estimation method which is a non parametric estimation method based on the premise that the sample median will tend to that of the distributional median, it presents some obvious advantages over OLS (ordinary least squares) estimates, since the results are robust in the presence of outliers and heteroscedastic errors in the response measurement and allows for the exploration of other central tendencies and statistical dispersion properties of the dataset Machando and Silva (2013). The results are robust even after controlling for presence of heterscedastic error in the response measurement as well re-sampling the dataset (conducted by the bootstrapped quantile regression technique). Further explanation is also provided for the implication of variables identified to drive fraud occurrences using kernel density estimation.

Key concepts: Outlier, Estimation, Quantile regression, Econometrics, Ordinary least squares, Heteroscedasticity, Sample (material), Business

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