TREND AND PATTERN OF ADVANCED AIRTIME/DATA LENDING AND ITS PROBABILITY DISTRIBUTION ON NIGERIAN TELECOMMUNICATION NETWORK

A. B. SOGUNRO; T. C. OBIWURU; S.M. OLANIYAN; I. K. OLAIYA; O. A OLUWOLE; R. O. AYORINDE

A. B. SOGUNRO Department of Actuarial Science and Insurance, Faculty of Management Sciences, University of Lagos, Akoka, Yaba, Lagos State, Nigeria.
T. C. OBIWURU Department of Actuarial Science and Insurance, Faculty of Management Sciences, University of Lagos, Akoka, Yaba, Lagos State, Nigeria.
S.M. OLANIYAN Department of Actuarial Science and Insurance, Faculty of Management Sciences, University of Lagos, Akoka, Yaba, Lagos State, Nigeria.
I. K. OLAIYA Department of Banking & Finance, Faculty of Administration and Management Science, Olabisi Onabanjo University, Ago-Iwoye, Ogun State
O. A OLUWOLE Department of Actuarial Science and Insurance, Faculty of Management Sciences, University of Lagos, Akoka, Yaba, Lagos State, Nigeria.
R. O. AYORINDE Department of Actuarial Science and Insurance, Faculty of Management Sciences, University of Lagos, Akoka, Yaba, Lagos State, Nigeria.

Keywords: Telecommunication, Lending, Recovery, Distribution-fits, Nigerian-Network

Abstract

With the ability of the telecommunications operators to develop business strategies in an environment where individuals and corporate organizations have been longing for services, pre-paid airtime/data (Top-up) has become a unique and innovative social business model. But the vendor is left with the problem of understanding the trend and pattern of advanced airtime/data lending and recovery and the probability distribution it follows. Hence, the main aim of this article is to examine the trend and pattern of advanced airtime/data lending and recovery and their probability distribution. The methodology of the article was designed by using Audit Command Language (R-Code) and Easy-Fit XL Evaluation Software with different ranking criteria to analyze secondary data covering 36 months, extracted from the Mobile Decisioning-MODE now ERL Telecoms Services limited and Globacom telecom operator’s intelligent network. The research findings revealed that lending and recovery are usually high especially when there is major disaster news, holidays, or festivity periods. The results also show that the drawings from the vendor's account output follow Weibull, Burr, and Pareto probability distribution function while the recovery output, rank Error, Nakagami, and Weibull as the best performing models distribution using Kolmogorov Smirnov, Anderson Darling, and Chi-Square ranking criteria respectively. The results of the study can assist the vendor on the amount to be reserve for the customers in the e-top bucket to be able to access the loan service when needed. It, therefore, recommends that the vendor should keep a sound revenue assurance unit that can assist in the reconciliation of all identified issues associated with the business data and also use the suggested distributions to project bucket account (e-top up) refilling. The study has established that there is no known airtime/data lending and recovery template for mobile telecommunication industries in Nigeria that can be used to determine the trend and pattern of airtime/data borrowed/lend by both subscribers and telecommunication providers that can use to determine the airtime/data bucket reserve.