Machine learning for predicting propensity-to-pay energy bills

Abstract

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations with many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and the occurrence of bad debt. With the advancements in data science; machine learning techniques can be used to build models to accurately predict a customer's propensity-to-pay. Creating effective machine learning models without access to large and detailed customer features presents some significant challenges. This paper presents a case study, conducted on a dataset from an energy organisation, to explore the uncertainty around the creation of machine learning models to predict residential customers entering financial hardship which then reduces their ability to pay energy bills. Incorrect predictions can result in inefficient resource allocation and vulnerable customers not being proactively identified. This study investigates machine learning models' ability to consider different contexts and estimate the uncertainty in the prediction. Eight models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Bayesian Neural Network for the binary classification problem of propensity-to-pay energy bills (i.e. tabular data with numerical and categorical variables) is proposed and explored for deployment.