Soft Computing in Financial Decision Making

Managing organizational resources is a complex and difficult task at the hands of the managers. Finance, being one of those many resources, is the ‘lifeblood’ of any organization; hence its efficient and effective management is even more important. Decisions related to financial matters conventionally involve quantitative analysis and interpretation of facts and figures. These analyses are crisp and discrete in nature, and hence may not always lead to appropriate, pragmatic conclusions. Parameters involved in decision making process are more often than not, beyond the boundaries of numeric figures. Unlike the conventional (hard) computational techniques, soft computing includes provisions for approximations, partial truth, uncertainty and imprecisions. Soft computing primarily constitutes Fuzzy Logic (FL), Neural Computing (NC), Evolutionary Computing (EC), Machine Learning (ML), and Probabilistic Learning (PL). The idea behind soft computing is to model cognitive behavior of human mind. Soft computing is well suited for real world problems where ideal models are not available. This makes application of soft computing in financial decision making even more purposeful as it involves too many parameters for consideration and hence very complex to solve using conventional statistical techniques. This paper is an effort to put forward a completely conceptual model using Fuzzy Logic Control (FLC) and the Euclidean Distance concept to assist managers in making financial decisions in particular, and other management decisions in general.