SCOAL: A framework for simultaneous co-clustering and learning from complex data
ACM Transactions on Knowledge Discovery from Data (TKDD)
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This paper 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 customers will present issued checks, 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 checks into more homogeneous groups before supervised neural networks being applied to arrive at a forecast for the date each check will be presented. Accuracy results are compared to the existing method of the company, together with regression and a heuristic method.