Available at SSRN: https://ssrn.com/abstract=3013174.
ISSN/ISBN: Not available at this time. DOI: 10.2139/ssrn.3013174
Abstract: We find evidence that material insider abuse and internal fraud were present in approximately 457 (37 percent) of the 1,237 U.S. failed commercial and mutual savings banks (hereafter, banks) between 1989 and 2015. Using a unique dataset of the incidence of insider abuse and internal fraud among U.S. failed banks we analyze the characteristics of failed banks with the ultimate goal of developing fraud detection models—parametric (logistic regression, Benford digit analysis) and non-parametric (neural networks). We obtain information on the incidence of insider abuse and internal fraud among failed banks from failing bank cases prepared for the FDIC Board of Directors, restitution orders (fines) supervisors assessed for bank employee fraud, and bond claims the FDIC made to recover fraud-related losses on failed banks. The supervisory data we use to quantify fraud among failed banks has not been used previously in published research and, we feel, provides more comprehensive information on fraud among failed banks than that available to academic researchers. Since fraudulent behavior lies outside the realm of rationale behavior modelled in economics, we develop a framework for internal bank fraud that provides rationale and support for our fraud detection models. This framework is based on previous studies of financial fraud and internal bank fraud in particular. We test this framework using regression analysis of the determinants of fraudulent behavior among failed banks between 1989 and 2015 and find that banks with insider abuse and fraud present overstated income and asset values, under-reported losses and consequently overstated net worth. The regression models of fraudulent failed banks provide information on the financial statement line items that can be used to identify fraud. We next use a recently developed second-order Benford digit test to identify those banks whose financial statements suggest tampering and purposeful misstatement. Our results suggest that material insider abuse and fraud at banks is detectable using Benford digit analysis of bank financial data for a period one-to-four years prior to failure. Unfortunately, we are unable to develop an accurate neural network model for fraud prediction.
Bibtex:
@misc{,
author = {John O'Keefe and Chiwon Yom},
title = {Offsite Detection of Insider Abuse and Bank Fraud among U.S. Failed Banks 1989-2015},
year = {2017},
doi = {10.2139/ssrn.3013174},
url = {https://ssrn.com/abstract=3013174},
}
Reference Type: Preprint
Subject Area(s): Accounting