Predicting Credit Default among Micro Borrowers in Ghana

Microfinance institutions play a major role in economic development in many developing countries. However many of these microfinance institutions are faced with the problem of default because of the non-formal nature of the business and individuals they lend money to. This study seeks to find the determinants of credit default in microfinance institutions. With data on 2631 successful loan applicants from a microfinance institution with braches all over the country we proposed a Binary logistic regression model to predict the probability of default. We found the following variables significant in determining default: Age, Gender, Marital Status, Income Level, Residential Status, Number of Dependents, Loan Amount, and Tenure. We also found default to be more among the younger generation and in males. We however found Loan Purpose not to be significant in determining credit default. Microfinance institutions could use this model to screen prospective loan applicants in order to reduce the level of default. Keywords: Microfinance, Loan Default, Default Prediction, Logistic Regressio

Empirical Mobile Network Traffic Prediction: Statistical Comparative Performance Analysis Using Mimo Rbfn Network Model

The exponential demand of telecommunication traffic require the development of different forecasting models in order to help industry players to plan for the future. The available forecasting traffic models are mostly developed for single-input single-output traffic data. The study applies multiple-input multiple-output (MIMO) radial basis neural network (RBFNN) model to instantaneously forecast five different time spans of telecommunication network traffic obtained from 4G and 3G networks operators. The data was taken from 3G uplink hourly, 3G daily voice, 4G weekly, 3G downlink monthly and 3G downlink quarterly from 2015 to 2017. The results prove that MIMO RBFNN (5-10-5) gives higher prediction accuracy than the other three MIMO models when subjected to different statistical tests