Propensity-to-Pay: Machine Learning for Estimating Prediction Uncertainty

TL;DR

This paper explores machine learning models, including a novel Bayesian Neural Network approach, to predict residential customers' propensity-to-pay energy bills and estimate prediction uncertainty, aiming to improve resource allocation and customer support.

Contribution

It introduces a novel Bayesian Neural Network method for binary classification of propensity-to-pay and evaluates multiple models' ability to quantify uncertainty.

Findings

Bayesian Neural Network effectively estimates prediction uncertainty.

Multiple models demonstrate varying accuracy in predicting payment hardship.

Uncertainty estimation can enhance decision-making in energy customer management.

Abstract

Predicting a customer's propensity-to-pay at an early point in the revenue cycle can provide organisations many opportunities to improve the customer experience, reduce hardship and reduce the risk of impaired cash flow and 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 datasets 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 that are able 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. Seven models from four families of machine learning algorithms are investigated for their novel utilisation. A novel concept of utilising a Baysian Neural Network to the binary classification problem of propensity-to-pay energy bills is proposed and explored for deployment.