Combining Supervised and Unsupervised Neural Networks for Improved Cash Flow Forecasting

Combining Supervised and Unsupervised Neural Networks for Improved Cash Flow Forecasting

Kate A. Smith (Monash University, Australia) and Larisa Lokmic (Monash University, Australia)
Copyright: © 2002 |Pages: 9
DOI: 10.4018/978-1-930708-31-0.ch015
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Abstract

This chapter examines the use of neural networks as both a technique for pre-processing data and forecasting cash flow in the daily operations of a financial services company. The problem is to forecast the date when issued cheques will be presented by customers, so that the daily cash flow requirements can be forecast. These forecasts can then be used to ensure that appropriate levels of funds are kept in the company’s bank account to avoid overdraft charges or unnecessary use of investment funds. The company currently employs an ad-hoc manual method for determining cash flow forecasts and is keen to improve the accuracy of the forecasts. Unsupervised neural networks are used to cluster the cheques into more homogeneous groups prior to supervised neural networks being applied to arrive at a forecast for the date each cheque will be presented. Accuracy results are compared to the existing method of the company, together with regression and a heuristic method.

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