Ethio Telecom Airtime Credit Risk Prediction Using Machine Learning
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Date
2023-06-01
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Addis Ababa University
Abstract
Prepaid mobile consumers can airtime credit to use telecom services even after their balance has run out and pay for it later users will find this service useful and operators also make more money from it but there’s also a chance that subscribers wont pay their credits credits dank this study’s main focus is on how machine learning techniques are applied to ethio telecoms airtime credit service customers to evaluate credit risk. Ethiopia’s top telecom company ethio telecom needs to manage credit risk well to maintain financial stability and customer satisfaction the company can identify customers who are more likely to default on their airtime credit by using accurate credit risk Ethiopia’s top telecom company ethio telecom needs to manage credit risk well to maintain financial stability and customer satisfaction the company can identity customers who are more likely to default on their airtime credit by using accurate credit risk prediction which enables proactive measures to lower risks and boost financial performance the historical customer data include in this study include customer profile data call detail data loan history data and usage data preprocessing techniques are used before model training to handle missing values encode categorical variables and reduce features ensuring the quality and consistency of the dataset.
In order to predict the credit risk associated with airtime this study used supervised machine learning algorithms four different machine loaming algorithms including naïve byes classifiers logistic regression random forests and k-nearest neighbors were trained and tested using a dataset of 1.168.000 ethio telecom prepaid subscribers performance evaluation metrics like accuracy precision retail and FI- score are used to assess each models efficacy using class balancing strategies the models robustness and gencralizabilizbility are also validated
According to experimental results the random forest algorithm has successfully predicted airtime credit risk with 99% accuracy in order to identify customers who are highly likely to default on their airtime credit ethio telecom is able to take preventative actions with the help of this developed model like adjusting credit limits
The study’s findings can fill the gap on how credit risk is predicted in the telecommunications industry and demonstrate how machine learning can improve risk management strategies and financial performance the proposed method can be used as a foundation for the development of automated credit risk prediction systems for ethio telecom and comparable organizations resulting in enhanced decision-making processes and reduced financial losses
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Keywords
Machine Learning, Ethio Telecom, Airtime Credit