Companies of all capacities should be hyperaware of their financial transactions and take steps to limit and respond to fraudulent payments and activities to safeguard their financial assets and security. A lack of attention to this could put companies at risk for accounts payable fraud, which could have a negative affect on their reputation and financial health. This blog on "10 Ways to Identify Accounts Payable Fraud" uncovers a few kinds of stuff you can try to tighten up your AP audit.
The accounts payable services, responsible for paying suppliers and other vendors, is a common target for this fraud. Employees and vendors can commit fraud in the accounts payable department, the two working together, or by an outside party who wants to access the company's accounts payable systems.
Every business feels the effects of fraud. The Association of Certified Fraud Examiners (ACFE) says a typical organization loses 5% of its revenue annually to fraud, with a median loss of $125,000. (ACFE). An average of $8,300 a month is lost due to fraud that goes unknown for 14 months.
ACFE's fraud tree classifies accounts payable fraud as "asset misappropriation." Most typical forms of occupational fraud include these.
Payment schemes, check to tamper, and expenditure reimbursement schemes are the most typical fraudulent disbursements in the accounts payable services.
An employee running a billing scheme under the guise of a fictitious company could submit false invoices. For non-physical items, like consulting services, this can be a simpler scam to pull off. Employees can keep the money stolen from their employer's bank account by forging checks. Overbilling or double billing for services is a fraud that suppliers might do to pocket the extra money.
**AP frauds are numerous, but firms can take preventative measures to minimize their exposure. So our first stop should examine some ways to identify accounts payable fraud.
AP leakage caused by duplicate payments is both avoidable and recoverable in most situations, even if they may not result from fraud. 65% of respondents to the AFP Payments Fraud and Control Survey of 2021 reported an increase in fraud attempts due to Covid-19. According to Mark Van Holsbeck, Avery-Director Dennison of Enterprise Network Security, about 2% of firms make double payments. It may seem like a small percentage, but if your company's A/P invoices total $75 million, there might be $1.5 million in duplicate payments. The following are some interesting numbers to consider:
Medicare's Inspector General determined that Medicare paid out $89 million in duplicate payments in 1998.
We have observed that electronic funds payments for TDMA accounts have been debited twice from the customer's checking account when done online.
It is projected that as much as $31.1 million in further duplicate payments may have been made by Medicaid during our two-year audit period. We found at least $9.7 million in such payments.
Overpaid invoices have prompted many new businesses, including A/P Recap, Automated Auditors, AP Recovery, and ACL and CostRecoverySolutions. These companies are growing because many people still make the mistake of paying for the same thing repeatedly.
Many software packages can control duplicate invoices, but finding them all requires extensive searching. For example, if you enter the same vendor's invoice number twice in an accounting program, you'll get an error message. A simple "A" in the invoice number or the change of one cent can lead to a payment being made twice. In vendor files, the most typical mistake is to have multiple vendor numbers for the same vendor; this is the most prevalent cause of double payments.
Certified fraud examiners utilize various technologies to enhance their ability to detect fraud. Benford's Law, for example, is one of the most common. This mathematical theory says that even if a sequence of numbers appears random, it is likely to be part of a pattern.
Alternatively, there is a chance that a particular number will be used in a particular position. For example, 30.1 percent of the time, the number 1 will appear first in payment, such as $1XX. The normal trend is more likely to be broken by payment fraud.
AP departments can use Benford's Law to examine their payouts, but it isn't perfect.
Assume an employee has the authority to approve purchase orders and invoice payments totaling $2,000, for example. They are fully aware that no one other than themselves is necessary to sign off on the project. Anything over $2,000 necessitates the approval of higher-level executives. So, how do they handle large fraudulent payments? You can get around the restriction by dividing them into $2,000 chunks.
Data analytics makes it simple to catch this type of scam. Check for any PO approvals or payments within, say, 5% of an individual's authorization limit and are made within a specific time window.
When a manager with purchasing authority orders items or services for their use rather than the organization's, they engage in employee fraud.
Searching for consumer or household goods keywords is a simple data analysis test. First, add all the words you think are suspect (e.g., "Home Depot" or "Amazon") and item descriptions (e.g., "garage shelving") to a data table.
Data analytics for fraud detection need not be limited to purchases and payments. Using a vendor's master record, for example, an employee might enter their bank account information. The employee's bank account is credited as a result of this. The employee then goes into the vendor master file and undoes the transaction.
Vendor master change data can detect changes that are reverted within a short period using data analytics.
It's when an employee conspires with a vendor to submit bills for items and services that never existed in the first place. Once the employee has approved, the vendor is paid, and the employee receives a percentage of the proceeds.
The failure to match an invoice with the goods received system can be detected by data analytics in organizations that track the receipt of items using a well-received system. If necessary, a three-way match between a purchase order, goods received data, and an invoice can be performed.
An employee can work with a vendor to approve inflated purchases in exchange for a bribe from the vendor.
Analysis can compare the average prices paid for goods and services among a wide range of vendors that offer identical products.
Fraudsters, as we already stated, are often clever. Now we must add the qualifier "not always" to that. For example, they may process an invoice or payment in a way that isn't consistent with normal business practices, such as rounding the amount. Transactions can, of course, be rounded to the nearest whole number. However, rounding up in payment systems is rare in the real world, especially when considering sales tax and other factors.
Using the "round amount" data analytic, you can rapidly identify any amount that finishes in an unusually long string of zeros. A MOD function is often used to check whether a remainder is zero.
It's not uncommon for fraudsters to commit some incredibly foolish errors. As a case study, consider an employee who creates a phantom vendor account, invoices phony items and services, and does not consider how invoice numbers develop in the real world.
During a typical month in accounts payable, canceled and returned checks are not unheard of. Vendors have an unusually high number of bounced checks, or a trend of bounced checks should be avoided at all costs. As long as the check is returned to the correct person, it's usually a lawful transaction.
For each vendor, divide the number of returned or canceled checks by the total number of checks. Then, to find the most suspect merchants, sort this list by descending percentage.
The ten AP fraud detection analytics listed above are a decent start for most companies. However, it is common to alter processes once a series of very simple analytics have been completed and evaluated for their worth.
Newer ones can replace analytics that doesn't work in practice. I don't mind at all! You can anticipate regularly using a suite of automated analytics within a short period, establishing an important core of an ongoing fraud detection program.