- 學位論文
灰色預測與人工智慧技術在促使電信業者營收確保之應用
The Application of Grey Model and Artificial Intelligence Techniques to Drive Revenue Assurance of the Telecommunication Operator
摘要
本篇論文提出一種智慧型推論系統，其中整合了灰色預測、模糊邏輯、基因演算法及類神經網路等人工智慧技術去實現一個精準的、比傳統風險管理及查帳方式更有效益的問題監控者，用以確保電信經營業者的營收。在電信業界中，營收短少肇生的主要成因可區分成兩大類，一類為實質上的營收流失，像是有人惡意倒帳、盜撥或帳務處理上的錯誤所引起的；另一類為可能的營收流失，像是忽略了那些認定為無效的或是無法計價的通信行為所引起的。這樣的營收流失依金錢來換算，每家電信業者每年損失達數億元。然而，營收確保上所遭遇到的實際問題大多充斥著複雜性與不確定性，僅能依靠人工來解決。在這樣困難四伏的情況下，問題無法有效的被預防與妥善處理。 我們提出一種可行的預測模型用以確保灰色預測的精確度並引入糊模邏輯、基因演算法及類神經網路間互助合作的人工智慧模式。另外，可行性預測模組的最佳化機制及利用精確度與涵蓋率的系統效能衡量方式也會被討論。最後，論文利用營收流失上的代表性錯誤來評估我們所提出方法的預測問題能力，並以此證明了系統運用於營收確保上的潛力及其可行性。
並列摘要
This paper presents an intelligent inference system for revenue assurance of a telecommunication company, where artificial intelligence (AI) techniques such as grey prediction, fuzzy logic (FL), genetic algorithms (GAs), and neural networks (NNs) will be combined to achieve a more accurate problem detector with a higher availability than those traditional risk management and audit approaches could provide. In the telecommunication companies, revenue leakage can take the form of actual loss like billing errors, fraud and bad debts, or from opportunity loss like billings foregone through incorrect calls. It costs hundred millions of dollars loss annually for each company. However, most practical problems in revenue assurance are complex, full of uncertainty and can only be solved by human resources. Due to such a difficult situation, the problems cannot be effectively prevented and overcome. A better prediction model from grey system is introduced and different AI techniques for synergism of FL, GAs, and NNs are presented. The optimization mechanisms for our prediction model and the measures of performance with precision and coverage are also discussed. The thesis finally exploits the empirical errors of revenue leakage for measuring prediction ability by using our proposed methods to demonstrate the system potential of revenue assurance.