應用模糊層級分析於銀行授信因素權重分析-以電動車產業授信案件為例
Abstract
本研究企圖運用模糊層級分析法探討銀行授信之評估,提供銀行同業授信作業之參考。本研究經由文獻、專家訪談、層級分析問卷,歸納整理出影響電動車產業授信風險之主要因素,並於問卷回收後運用模糊層級分析進行量化分析。整理歸納獲致研究結果如下:
ㄧ、模糊層級分析法對新創產業授信評估之離散資料獲得聚斂的效果,有助於提升授信評估之品質。
二、影響電動車產業授信決策的主要因素涵蓋3大構面16項準則,構面權重高低依序為財務分析、經營管理和企業展望。
三、授信案件評估之關鍵準則計有16項,重要性最高的前5項分別為擔保品的品質及價值、總體經營週轉金需要量評估、獲利能力、現金流量穩定性及企業之成長力。另外重要性最低的後5項分別為產業環境、員工與公司互動情形、領先的製程技術、負責人信用狀況,及市場競爭情況。
四、本研究結果可提供對新創產業做更客觀評估之參考,進而建立實證事例與評估法則。
Based on the literature of credit evaluation, the researcher found out the key factors that impact the credit risks of the electric vehicles, though expert’s interviews to build up the validity of the key factors, and conducted the questionnaires to survey the senior bank executives. The senior bank executives in Taipei City were invited to complete the questionnaires, and the data were quantified and analyzed with FAHP analysis. The results of this study are as follows: 1. The study shows that the FAHP has convergent effect on discrete data and offers concrete empirical data as reference for startup industries during the credit granting evaluation process. These empirical data help to elevate the credit granting quality. 2. The three major areas, included 16key criteria, which influence the credit granting decision of the electric vehicles, the major areas are listed in priority: financial analysis, operating management, and industrial prospect. 3. There are total 16 key criteria of credit granting evaluation. The first 5 criteria with the highest importance are the quality and value of collateral, the assessment of overall working capital requirements, the profitability, the stability of cash flow, and the growth power of enterprises.In addition, the last 5 criteria with the lowest importance are the industrial environment, the interaction between staffs and the company, the leading manufacturing technology, the credit standing of the owner, and the market competition. 4. The results of this study provide more objective evaluations for the startup industries, and further establish verified cases and principles of evaluation.
Based on the literature of credit evaluation, the researcher found out the key factors that impact the credit risks of the electric vehicles, though expert’s interviews to build up the validity of the key factors, and conducted the questionnaires to survey the senior bank executives. The senior bank executives in Taipei City were invited to complete the questionnaires, and the data were quantified and analyzed with FAHP analysis. The results of this study are as follows: 1. The study shows that the FAHP has convergent effect on discrete data and offers concrete empirical data as reference for startup industries during the credit granting evaluation process. These empirical data help to elevate the credit granting quality. 2. The three major areas, included 16key criteria, which influence the credit granting decision of the electric vehicles, the major areas are listed in priority: financial analysis, operating management, and industrial prospect. 3. There are total 16 key criteria of credit granting evaluation. The first 5 criteria with the highest importance are the quality and value of collateral, the assessment of overall working capital requirements, the profitability, the stability of cash flow, and the growth power of enterprises.In addition, the last 5 criteria with the lowest importance are the industrial environment, the interaction between staffs and the company, the leading manufacturing technology, the credit standing of the owner, and the market competition. 4. The results of this study provide more objective evaluations for the startup industries, and further establish verified cases and principles of evaluation.
Description
Keywords
企業授信評等, 模糊層級分析, 電動車產業, Electric Vehicles, Fuzzy Analytic Hierarchy Process (FAHP), Credit Granting Evaluation