資料探勘應用在偵測電信系統之賴帳行為

The Detection in Telecoms Business by Data Mining Approach

羅宸逸(Chen-Yi Lo)

指導教授 ： 陳伯榮

淡江大學/工學院/資訊工程學系碩士在職專班/碩士(2007年)

https://doi.org/10.6846/TKU.2007.00234

摘要

政府因應電信產業市場發展，將電信產業開放民營，業者莫不投入相關通訊科技技術發展及各樣通訊科技產品，也因此業者間市場競爭也越趨激烈。業者為了提高顧客滿意度及忠誠度，並將客戶流失度降到最低的目標下，推出各類服務及促銷方案，目的即是為了保有自身在行動通訊市場的利益。
　　而正因電信市場的龐大商機，各業者費盡心思爭取更多的用戶數以維持自身收益。然而不法集團運用不正當手段進行電話盜撥，早已獲取暴利，形成電信業者莫大損失。我們研究業者呆帳中的賴帳行為，也就是拒繳費用與延遲付費。賴帳行為雖不會對電信業者有立即且明顯的損失，但會造成其他成本增加與設備佔用等問題。
　　在本論文中，我們使用了IM8.1(Intelligent Miner for Data 8.1)，運用資料挖掘的技術，將客戶資料庫及歷史資料做關聯式法則(association rules)與叢集化(Data Clustering)的相關分析運算，並將其規則存放入資料庫，預測存在高度風險之客戶，提升業者決策品質，降低業者呆帳損失。

關鍵字

電話盜撥 ； 賴帳行為 ； 關聯式法則 ； 叢集化

並列摘要

Because of the prosperous development of the telecommunication market, all companies all try their best to increase more customers and business to maintain their profit. At the same time, there exit many illegal groups are using a variety of tricks to execute telephony fraud to earn a lot of money and cause much loss for telecommunication companies. Telephony fraud is about 2% of total phone bill income for telecommunication companies. If the average bill 80 US dollars for a phone user, there are about 100,000 customers for a company, the phony fraud will cause telecommunication companies 1,920,000 US dollars loss per year. Based on the loss, it is a very serious problem. There are many ways to cause not paying bills, refusing to pay the bill and not paying the bill on time are two types of them. We call these two types “denying the bills”. If we can use the analysis technology of data mining effectively, analyze the customers database and their historical billing information, we should predict the character of high risk customers from analysis results. It can be applied to promote the decision making quality and competition capabilities. Therefore effective risk management can be conducted in a very short time to prevent losing customers and lower the bill loss. There are a lot of researches about fraud study proposed by many scholars. But there is little about behaviors of not paying the bills. The situation about not paying the bills is not like fraud, but it causes the telecommunication companies a lot obvious amount of loss. It also causes other loss for telecommunication companies, such as: cash flow decrease, manpower of urging customers paying bill increase, mobile base system occupation and customers’ loss. Therefore, the way to detect possible customers who will not pay the bills is the main goal of our system.

並列關鍵字

telephony fraud ； denying the bills

參考文獻

[15] 中華民國交通部電信總局

[1] 謝邦昌, “Microsoft SQL Server 2005 Data Mining 演算法 – 資料採礦產業應用實務”, 2007

[6] J.R Quinlan, “Induction of Decision Tree.” Machine Learning,vol. 1 no. 1, 1996.

[7] J.R Quinlan, “C4.5:Programs for machine Learning” , San Mateo, Calif.:Morgan Kaufmann, 1993.

[8] Stuart Russell, Peter Norving, “Artifical Intelligence a Modern Approach.” Prentice-Hall Internation Editions.