As the US economy has struggled during the past few years, estimating the collectibility of accounts receivable is perhaps more important than ever for a business's success. An improvement in the accounting measurement of collections, along with an improvement in cash flow forecasts and budget accuracy, can make the difference between a company's survival and its failure. Using matrix algebra and a methodology known as Markov chains may facilitate forecasting collections of accounts receivable or confirming estimates made from more traditional methods. Using Markov chains to estimate collections of accounts receivable used to be difficult unless a user had access to a mainframe computer. Now, however, Microsoft Excel easily manipulates matrix and vector operations and allows for enough accounts receivable aging categories to satisfy most users. Markov chains could be useful in performing sensitivity analyses of budget assumptions or estimating monthly accounts receivable collections. One complication of using Markov chains to forecast collections of accounts receivable is partial payments.
Forecasting Accounts Receivable Collections with Markov Chains and Microsoft ... August A Saibeni The CPA Journal; Apr 2010; 80,
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