Case-Based Reasoning for Cash Flow Forecasting using
Fuzzy Retrieval

Rosina Weber Lee1*, Ricardo Miranda Barcia2 and Suresh K. Khator1
1University of South Florida, Tampa, Fl, USA
2Federal University of Santa Catarina, Industrial Engineering,
Florianpolis, SC, BRAZIL.

	Abstract. Case-Based Reasoning (CBR) simulates the human way of solving
problems as it solves a new problem using a successful past experience applied to a
similar problem. In this paper we describe a CBR system that develops forecasts
for cash flow accounts. Forecasting cash flows to a certain degree of accuracy is an
important aspect of a Working Capital Decision Support System. Working Capital
(WC) management decisions reflect a choice among different options on how to
arrange the cash flow. The decision establishes an actual event in the cash flow
which means that one needs to envision the consequences of such a decision.
Hence, forecasting cash flows accurately can minimize losses caused by usually
unpredictable events. Cash flows are usually forecasted by a combination of
different techniques enhanced by human experts feelings about the future, which
are grounded in past experience. This makes the use of the CBR paradigm the
proper choice. Advantages of a CBR system over other Artificial Intelligence
techniques are associated to knowledge acquisition, knowledge representation,
reuse, updating, and justification. An important step in developing a CBR system is
the retrieval of similar cases. The proposed system makes use of fuzzy integrals to
calculate the synthetic evaluations of similarities between cases instead of the usual
weighted mean.
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