Predicting the Workload in Debt Collections to Improve the Efficiency of Manpower Planning

Abstract

This research aimed to predict volume workloads in debt collections and compare the accuracy of forecasting methods. Three forecasting methods were considered in the study: the SARIMA model, the random forest model, and long short-term memory. The data used in this study were a time series of a daily debt collection volume workload due 1, 5, 10, 15, 20, and 25 in the used car loan corporation. They were divided into 2 datasets. The first dataset containing the past data from June 2020 to October 2020 was used for selecting the most suitable model, and the second dataset containing the past data from November 2020 to December 2020 was used for comparing the prediction accuracy of each forecasting model in terms of mean absolute percentage error (MAPE). Consequently, the lowest MAPE achieved by LSTM for different due dates was 6.79%, 6.24%, 7.08%, 10.88%, 12.45%, 10.17%, respectively, while the MAPE achieved by random forest was 11.72%, 11.27%, 8.94%, 11.92%, 13.91%, 16.67%, respectively, and the MAPE achieved by SARIMA was 23.35%, 32.87%, 32.58%, 30.07%, 33.93%, 27.31%, respectively. It indicates that the LSTM model was the most accurate to forecast the daily debt collection volume workload of the field debt collector in advance of the future. The predicted workload can be used as a piece of supporting information for adequate workforce planning of the corporation’s used car loan.