Evaluating autoregressive models and foundational models for microloan repayment trends forecasting. - Bonsu-Afrane, Kwaku (2025)
The greater chunk of survivability of a financial institution such as a microfinance or a co-operative is dependent on accurate predictions and effective assessment of loan repayment, which helps to reduce potential losses that may arise from loan defaults. This thesis compares the performance of two machine learning models, NeuralProphet, an open-source forecasting tool designed as an extension of Facebook Prophet specifically for time-series data and effective in handling datasets with strong seasonal components, inconsistent data, and non-linear trends with another machine learning model, a foundational transformer-based time series forecasting model based on a long short-term memory (LSTM) neural network architecture called Lag-Llama in their ability to forecast loan repayment trends of a microfinance operating in rural areas in Ghana. Two models (NeuralProphet & Lag-Llama) are compared to evaluate which performs better, with both assessed under optimal conditions using loan repayment dataset from the Epliclis microfinance company limited covering the period 2020 to 2024. The models are evaluated using the following metrics: mean absolute error (MAE) and root mean squared error (RMSE).