Abstract:
As the challenge of customer default persists, it continues to have a significant impact
on the loan repayment sector across various industries. This issue is particularly
pronounced in the Pay-As-You-Go (PAYGo) model of asset financing within the
renewable energy sector, especially in off-grid communities in Africa. Under the PAYGo
model, renewable energy companies provide Solar Home Systems (SHS) to customers,
with payments made incrementally through mobile money channels. Defaulting on
these payments directly affects the revenue of these companies, highlighting the
imperative need for early prediction of potential defaulters. In this study, an innovative
approach to enhance default prediction in the PAYGo model by integrating Particle
Swarm Optimization (PSO) with the Random Forest (RF) and xGBoost algorithms for
feature selection is introduced. Furthermore, Synthetic Minority Oversampling
Techniques (SMOTE) is used to tackle class imbalance issues. The PSO algorithm, rooted
in swarm intelligence, elevates classifier optimization, resulting in improved model
accuracy. Primary findings reveal that the PSO-Random Forest Classifier surpasses other
models in performance. The success of the PSO-RF model is attributed to its proficiency
in handling complex datasets and mitigating overfitting. Through the optimization of
RF hyperparameters, the PSO algorithm substantially enhances model performance. The
high precision value of 0.9961 underscores the classifier's accuracy in identifying positive
cases while effectively minimizing false positives (accuracy: 0.89), outperforming the
unoptimized random forest (accuracy: 0.76). In summary, this research yields valuable
insights into addressing challenges related to loan repayments within the PAYGo model,
thereby contributing to the sustainability and profitability of renewable energy
companies operating in off-grid communities. The optimized feature selection process
results in dynamic high accuracy, reducing default rates in PAYGo models for off-grid
communities. These findings hold broader implications for enhancing financial
sustainability and advancing renewable energy access in underserved regions, ultimately
fostering economic development and environmental sustainability.
