  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.
