Abstract
This paper develops a Mamdani fuzzy logic system (FLS) that has stochastic fuzzy input variables designed to identify cash-flow deficits in bank lending policies. These deficits do not cover the available cash-flow (CFA) resulting from the company's operating activity. Thus, due to these deficits, solutions must be identified to avoid companies' financial difficulties. The novelty of this paper lies in its using stochastic fuzzy variables, or those categories of variables that are defined by fuzzy sets, characterized by normally distributed density functions specific to random variables, and characterized by fuzzy membership functions. The variation intervals of the stochastic fuzzy variables allow identification of the probabilistic risk situations to which the company is exposed during the crediting period using the Mamdani-type fuzzy logic system. The mechanism of implementing the fuzzy logic system is based on two stages. The first is based on the determination of the cash-flow requirements resulting from loan reimbursement and interest rates. This stage has the role of determining the need for financial resources to cover the liabilities. The second stage is based on the identification of the stochastic fuzzy variables which have a role in influencing the cash flow deficits and the probability values estimation of these variables taking into account probability calculations. Based on these probabilistic values, using the Mamdani fuzzy logic system, estimations are computed for the available cash-flow (the output variable). The estimated values for CFA are then used to detect probability risk situations in which the company will not have enough resources to cover its liabilities to financial creditors. All the FLS calculations refer to future time periods. Testing and simulating the fuzzy controller confirms its functionality.
Highlights
Bank lending activities are extremely appealing to companies, as they allow them, within a relatively short period of time, to acquire the financial resources they need in order to invest in business activities
It is the first financial management tool that uses an artificial intelligence technique for bank lending issues; The Mamdani fuzzy logic system uses stochastic fuzzy variables, which allows for the identification of probable lending periods in which credit reimbursement risk may occur; It examines the lending risk for future periods during the reimbursement period according to two important economic indicators, namely, EBITDA and changes in working capital (∆WK )
The main characteristic of these stochastic input variables is that they allow for estimation of the values for future time periods in the form of interval limits
Summary
Bank lending activities are extremely appealing to companies, as they allow them, within a relatively short period of time, to acquire the financial resources they need in order to invest in business activities. Asset depreciation depends on asset value and estimated lifetime, which are two important elements of the depreciation system which determine the proportion of the asset value transferred to the products and services obtained This value transfer is an essential element in the process of cash-flow formation needed to repay debts to financial creditors. The gap between the loan repayment period and the asset depreciation period is the main cause of cash-flow deficits, which are covered from the company’s current activity These cash-flow deficits are random variables by nature that depend on the company’s economic performance and are detected in this paper using the Mamdani fuzzy logic system [1,2,3]. With the help of the Mamdani fuzzy logic system developed in this study, the probabilistic values of the influence factors in the credit process are identified—earnings before interest, taxes, depreciation, and amortization (EBITDA) and the change in working capital (∆WK )—in order to subsequently detect the risks due to probabilistic cash-flow deficits (∆CFD)
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