自美國次貸危機開始,金融海嘯襲捲全球,包括雷曼兄弟、美林銀行等一批歷史悠久,聲譽昭著的金融機構,都無法抵擋這個狂潮,一一破產或裁併。全球企業在這波狂潮中自然也無法脫身,銀行放款的縮減使得經營資金取得不易,消費能力的驟降更造成了企業經營的困難,無可避免的,客戶倒帳或信用不良的情形愈來愈多,在這個艱難的時刻,客戶信用風險控管顯得特別重要。 為提昇競爭力,企業資源規劃(Enterprise Resource Planning, ERP)已經是企業必備的系統之一,伴隨著企業資源規劃最佳化流程而來的,是龐大的日常交易資料;這些資料對企業來說,是彌足珍貴的資產,為有效分析這些資料,輔助企業經營上的決策,商業智慧系統藉由線上分析,在原有的交易系統上,提供即時、正確的決策參考,可以彌補企業資源規劃的不足。 本研究以企業內企業資源規劃系統所累積的銷售及收款資料為基礎,利用資料倉儲(Data Warehouse, DW)及線上分析處理(Online Analytical Processing, OLAP)技術,提供風險管理人員明確的風險管理指標,做為客戶信用風險的決策參考;並使用資料探勘(Data Mining)技術中的關聯規則和類神經網路演算法,分析風險管理指標的關聯性。我們使用Microsoft SQL Server及Strategy Companion Analyze等應用工具做資料的處理及分析,以多維度的方式呈現,即時掌握客戶銷售及收款信用狀況訊息,提供管理者一個明確的分析結果及清楚的策略輔助資訊,以控管客戶信用風險。
Since the beginning of the U.S. sub-loan crisis, the world face financial tsunami, including Lehman Brothers, Merrill Lynch and other banks that have a long history and reputation of financial institutions, can not resist the tide, cause merge or bankruptcy. Global business in this wave can not get out, it is difficulty of obtaining operating funds because bank loans made to reduce, and more difficult because purchasing power caused by the sharp fall. It is inevitable that customers collapse or bad credit, in this difficult time, customer credit risk management is of particular importance. To enhance the competitiveness, Enterprise Resource Planning (ERP) is one of the necessary information system, along with the best of enterprise resource planning process comes a huge transaction data; the data on enterprises is a very valuable asset, for the effective analysis of these data, supporting business decision-making, Business Intelligence through online analytical system, provide immediate, accurate decision-making on the existing transaction system, can make up for Enterprise Resource Planning inadequate. In this study, we use sales and receivables accumulated information based on the Enterprise Resource Planning. and use Data Warehousing (DW) and Online Analytical Processing (OLAP) technology to provide risk managers clear risk management indicators of credit risk as the customer decision-making, and use Association Rules of Data Mining(DM) technology to analysis relation of this indicators. We use Microsoft SQL Server and Strategy Companion Analyze tool to do data processing and analysis to a multi-dimensional way, and handle customer sales and receivables credit information, providing managers a clear analysis and clear strategy supporting information to manage customer credit risk.