貸款、現金卡、信用卡雖是銀行重要的獲利來源，卻也會產生很高的信用風險，客戶一旦未能如期還款，便會帶給銀行莫大的危機。不僅可能出現很多違約，甚至還會使得銀行倒閉！因此高報酬低風險一直是銀行最想達成的目標，所以銀行想藉由一些資訊來判斷是否繼續提供這些客戶服務，如果能確實找出不好的客戶並拒絕往來，就可有效減少風險的機率。本研究欲透過銀行A所提供的客戶資料，運用區別分析建立風險預測模型，本研究之模型亦將同時考量變數間共線性及類別變數轉換之處理問題。運用本模型以期能達到良好的風險控管，並降低風險的發生率。

Although loan, cash card, and credit card are the important resources of high returns for banks, they can produce high risk of default. As long as some customers don't return the money on time, it brings banks serious dangers. It may not only generate many bad debts but also make banks close down! High rewards and low risk are always the main objective for banks. They hope every customer's credit is excellent and would not cause any loss. If banks can earlier find out bad customers and refuse their requests, the probability of risk will be lowed down. The aim of the research is to construct a prediction model of risk to control the risk and reduce the probability of it by handling some important variables of the data and using Discrimination Analysis technique from the data which comes from bank A. Our model will take the collinear of variables and transformation of categorical variables into consideration at same time.