Uncollectable Receivables

The attribute sampling method can be used to estimate the number of invalid account receivable funding in an account receivable aging. Attribute samples involve no calculations, and a degree in statistics is not required to understand it. Once the sampling risk is quantified and some estimations are made, the use of numerical tables is employed to develop the sample. The first step in determining the sample size is to assess the risk of over reliance.

The 2nd step is to determine the tolerable deviation rate, or the amount of uncollectable receivables that a lender deems acceptable. The 3rd and final step in determining sample size is to quantify the expected deviation rate. Attribute sampling allows lenders to efficiently choose a representative sample of a client's accounts receivable aging on a monthly basis; furthermore, because lenders are requesting verification of specific invoices, the response rate should be greater, freeing field examiners from unnecessary follow-up.

Asset-based lenders normally send out customer verifications on an ongoing basis to confirm the existence of a client's collateral. Normally, they are written requests to verify a customer's entire accounts receivable balance. As lenders know, this method has its deficiencies. Response rates on verifications can vary widely, and those that are returned may be marked incorrect, when in fact the balance may be accurate. These shortcomings are a function of the summary nature of the verifications themselves.

I have seen many verifications returned, with a harsh comment written across the top such as need detail, or "What makes up this balance?" Still others are returned as having an incorrect balance, only to have a field examiner later find that all the related invoices were paid in full. How can this be? This type of discrepancy most always results from timing differences; for example, if a customer remits payment on the 28th, his end-of-month payable aging would be reduced, but since the payment has not been received by our client at month end, our client's receivable aging will not reflect this payment. This is a common occurrence when a client and a supplier transact large volumes of business.

Statistical solutions These problems may be solved, then, not by verifying entire balances, but individual invoices. It would be too time consuming, however, to detail all the invoices in a customer's balance. Therefore, we will turn to the study of statistics to assist us in choosing a representative sample of individual invoices to verify a client's accounts receivable aging. This type of sampling is referred to as attribute sampling, and its purpose is to estimate the number of deviations in a population. This method is used to estimate the number of invalid receivables in an accounts receivable aging. Attribute sampling involves no calculations and you needn't have a degree in statistics to understand it. Once the sampling risk is quantified and some estimations are made, the use of numerical tables is employed to develop your sample.

Determination of sample size The first step in determining the sample size is to assess the risk of over reliance. This is the lender's risk of depending upon the client's accounting controls to prevent the occurrence of invalid receivables when the actual controls do not support this assumption. The greater the risk the lender is willing to take, the smaller the sample, and vice versa; for instance, if a lender was willing to accept no risk whatsoever (zero percent), he would verify all of a client's receivables on a constant basis.

Account Receivables