Industry Insights

Using Data for Fraud Deterrence & Detection in Banking

April 2015
Author:  Jeremy Clopton

Jeremy Clopton

Director

Forensics & Valuation Services

910 E. St. Louis Street, Suite 200
P.O. Box 1190
Springfield, MO 65801-1190 (65806)

Springfield
417.865.8701

Fraud in banking takes many forms, from asset misappropriation to financial statement fraud.  According to the Association of Certified Fraud Examiners 2014 Report to the Nations on Occupational Fraud and Abuse, the average organization loses 5 percent of its revenues to occupational fraud.

Data Analytics as an Anti-Fraud Control

Many organizations wonder what they can do to prevent fraud. Based on the report, the most effective anti-fraud control is “proactive data monitoring and analysis” (data analysis). In fact, data analysis resulted in an almost 60 percent reduction in median fraud losses and a 50 percent reduction in median scheme duration in the cases studied. For these reasons—and the growing volume of data generated by organizations—it’s important to know how to use data analysis to deter and detect fraud.

Banking Schemes

The most common financial industry scheme is corruption, which the report defines as any scheme in which an employee misuses his or her influence in a business transaction that violates his or her duty to the employer in order to gain a direct or indirect benefit, e.g., schemes involving bribery or conflicts of interest.

Application of Data Analysis

Corruption schemes in banking usually are accomplished through one of two means:  disbursements through loans and deposit accounts or disbursements through accounts payable. Some methods used to identify corruption, e.g., conflicts of interest or potential kickbacks, in the loan and disbursement files include:

  • Identifying related parties – Looking for potential related parties or conflicts of interest by comparing the employee and vendor/customer files based on key attributes, e.g., name, address, phone or taxpayer identification number, may help identify high-risk relationships. Other beneficial analyses include geospatial analysis of business entities (customers or vendors) to identify entities located in residential areas, entities using a mailbox service and entities without an address.
  • Disbursements – One of the most effective analysis techniques related to disbursements is trend analysis focused on identifying accelerating patterns of activity or patterns outside well-defined expectations of normal activity.
  • Maintenance file analysis – Many reports reviewed by bank managers, examiners and others are at a point in time and don’t contain details about what transpired between the last and current reports. Therefore, banks should analyze the account and customer maintenance files for indications of manipulation. Examples include manipulating past due amounts, account balances, loan maturity dates and interest rates on accounts where a bank employee has a relationship.

Conclusion

Though data analysis is the most effective anti-fraud control, it alone isn’t enough to deter and detect fraud. The most effective fraud prevention program combines multiple elements that create an environment where fraud is less likely to take root. The report contains information regarding other anti-fraud controls to help deter and detect fraud in your institution. As you assess fraud risk in your organization, be sure to evaluate all possible anti-fraud controls.

For more information, contact your BKD advisor.

BKD LinkedIn BKD Twitter BKD Youtube BKD Google Plus