過去的企業藉著銷售活動,由市場行銷取回現金。如今,由於信用制度的擴大,企業很難再由銷售活動中即時收回現金,而必須透過應收帳款的步驟,才可以完成整個交易。顧名思義,應收帳款是指應該收得到錢的貨款,理論上,應收帳款收現天數愈短愈安全,但是也有可能遇到遲遲收不到貨款的情形,而且通常時間拖得愈久,收到貨款的機會愈悲觀,因為對方可能還不出錢,或是根本不想還錢,這筆應收帳款可能就變成呆帳了。 為了避免此一呆帳損失,以致於對企業造成不利的影響,呆帳的發生與預防已成為當前企業共同關注的問題。 景氣的影響造成許多原本的還款正常戶逾時繳款,被歸入壞帳,而這些壞帳與其他惡性倒閉的壞帳相比是比較容易催收的。所以本研究應用決策樹與類神經網路之探勘技術,根據客戶基本資料與交易活動後之應收帳款資料,組成分析變數,建立逾期預警模式,提供企業於應收帳款逾期之分析,與信用額度的檢討與設定,以降低企業呆帳的機率。 研究結果發現「交易後收款天數」、「收款風險評等」、「客戶類別」、「加盟時間」、「失業率」、「前6月交易次數」等6項變數,以決策樹與類神經網路建立的逾期預警模式,確實在經銷商客戶的應收帳款管理上,有不錯的逾期預測結果。
In the past, companies retrieve cash through selling and marketing activities. Nowadays, because of the extension of credit system which makes companies hard to receive cash immediately. Enterprises have to accomplish the transaction by means of accounts receivable. Theoretically, the shorter the collecting days, the safer and the least possible the accounts receivable turn into bad debt. To prevent companies from the disadvantages of bad debt, the occurrence and precaution of bad debt have become a hot issue problem in the present enterprises. The economic fluctuations cause many punctual debtors default their payments which were thus credited to bad debt. But this kind of bad debt is easier to collect, compared to the other types of bad debts. This study applies decision tree and neural networks technology, based on customers’ background information and the data of accounts receivable after transaction, to form the analytical variables. In addition, to build up an early warning overdue model for the enterprises to analyze overdue accounts receivable, review and reset the credit line; moreover, to decrease the possibility of bad debt. The result of the study found that collecting days after transaction, risk rating, customer classification, franchising time, unemployment rate, transaction times in the past six months are predictable variables in the early warning overdue model using decision tree and Neural Networks technology.