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Full Project – Decision support system for telecommunication companies in Nigeria

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CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Decision support systems (DSS) are defined as interactive computer-based systems intended to help decision makers utilize data and models in order to identify problems, solve problems and make decisions. They provide support for decision making; they do not replace it (Gachet, 2014). The mission of decision support systems is to improve effectiveness, rather than the efficiency of decisions (Stanhope, 2012). Modern organizations use several types of decision support systems to facilitate decision support. In many cases, OLAP based tools are used in the business areas, which enable multiple views on data and through that a deductive approach to data analysis. Data mining extends the possibilities for decision support by discovering patterns and relationships hidden in data and therefore enabling the inductive approach of data analysis (Muller, & Turner, 2010).

Tariff plan designing in a telecom company involves complicated decision-making processes that require different types of information and knowledge. However, typical accounting systems do not readily provide the expected costs for different sales levels. For this, a model is required which separates out the fixed and variable costs and then allows one to predict what the costs would be as the sales or other variables, which impact upon the costs change (Izang et al., 2015).

Marakas (2009) opined that it has been estimated that it costs five times as much to attract a new customer as it does to retain an existing one. Creating a loyal customer is not only about maintaining numbers of customer overtime, but it is creating the continuous relationship with customers to encourage purchasing in the future. Instead of attracting new customers, they would like to perform as well as possible more business operations for customers in order to keep existing customers and build up long-term customer relationship.

Based on this reason, this study therefore tends to design and implement a decision support system (DSS) for Telecommunication Company in Nigeria, using Airtel as a study area and also a rule based approach is suggested for estimating the chance of porting out of a customer using a FIS (Fuzzy Inference System).

1.2 Statement of the Problem

Research has shown that fraud is a serious problem for telecommunication companies, leading to loss of billions of naira in revenue each year (Marakas, 2009). Fraud is divided into two categories: subscription fraud and super imposition fraud. Subscription fraud is when a customer opens an account with the intention of not paying for the account charges. Super imposition fraud involves a legitimate account with some legitimate activity, but also includes some “super imposed” illegitimate activity by a person other than the account holder. Super imposition fraud poses a bigger threat for the telecommunications industry and for this reason data mining technique is used for identifying this type of fraud. These applications should ideally operate in real-time using the call detail records and, once fraud is detected or suspected, should trigger some action. This action may be to immediately block the call and/or deactivate the account, or may involve opening an investigation, which will result in a call to the customer to verify the legitimacy of the account activity (Izang et al., 2015).

However, due to the above stated problems, this study tends to design and implement a decision support system (DSS) for Telecommunication Company in Nigeria, using Airtel as a study area, this will go a long way in reducing or minimizing the problems.

1.3 Aim and Objectives of the Study

The major aim of this research is to design and implement a decision support system (DSS) for Telecommunication Companies in Nigeria.

However, other objectives of the study are to;

Provide an overview of data mining.
Examine the various data mining techniques of telecommunication companies in Nigeria.
Identify and model the requirements specification to develop the system (Decision Support System).
Identify the challenges of data mining faced by telecommunication companies in Nigeria.
Proffer solution to the identified challenges by way of designing and implementing a Decision Support System for Telecommunication Companies in Nigeria.

1.3.1 Method of Achieving the Objectives

In achieving the objectives of this study, the researcher adopted two methods: primary and secondary methods. The primary method adopted interview and direct observation, whereas the secondary method adopted was exploitation of the services of the library, related articles, journals, magazines and the use of internet.

1.4 Scope of the Study

This project work focus mainly on the design and implementation of Decision Support System for Airtel Telecommunication Company, however, the system can be applicable to any other telecommunication company.

1.5 Significance of the Study

The design and implementation of Decision Support System for Airtel Telecommunication Company will educate Airtel Telecommunication Company and any other telecommunication company on data mining techniques of telecommunication companies in Nigeria, the data mining applications and how they can be used in fraud detection.

The study will also enlighten telecommunication companies on process of data mining which includes integrating the cables, selecting useful data for mining.

1.6 Limitation of the Study

This study “decision support system (DSS) for Airtel Telecommunication Company” has the following limitations:

Financial constraint– Insufficient fund tends to impede the efficiency of the researcher in sourcing for the relevant materials, literature or information and in the process of data collection (internet, questionnaire and interview).
Time constraint- The researcher will simultaneously engage in this study with other academic work. This consequently will cut down on the time devoted for the research work.

1.7 Definition of terms

Decision Support Systems (DSS): interactive computer-based systems intended to help decision makers utilize data and models in order to identify problems, solve problems and make decisions.

Subscription fraud: this is characterized by a fraudster using own, stolen or fabricated identity to get services with no intention of paying.

Data mining: it is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis/ is the process of examining large databases in order to generate new information

Bad Debt: This is a debt that is not collectible and therefore is worthless to the creditor.

Brain computer interface: It is a direct communication pathway between an enhanced or wired brain and an external device.

Churn rate: it is the percentage of subscribers to a service who discontinue their subscriptions to that service within a given period.

Data ware house: it is a large store of data accumulated from a wide range of sources within a range of sources within a company and used to guide management decisions.

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