Dataset for Customer Default

Rise in Energy Prices and cost of living crisis

As energy costs surge and the cost of living becomes a pressing concern, customer debt levels are on the rise. To address this, E.ON Next has tasked me with developing a machine learning model. This model will identify customers who may struggle to repay their debts, enabling us to offer timely support or explore alternative measures. I've analyzed a dataset provided by E.ON Next and developed a proof of concept model to predict the likelihood of customers repaying their debt. Here's a brief overview of my findings, the model, and recommendations:

The rising energy prices and cost of living have led to an increase in customer debt levels. E.ON Next aims to proactively identify customers who may need additional support to repay their debts. Dataset Overview:

The dataset contains information about customers, their energy usage, payment history, and debt status. Machine Learning Model:

Developed a simple machine learning model using algorithms such as logistic regression or decision trees. Trained the model on historical data to predict the likelihood of debt repayment based on various customer attributes. Findings:

The model successfully predicts customers who are less likely to repay their debts with a certain level of accuracy. Identified key factors influencing debt repayment likelihood, such as payment history, income levels, and energy usage patterns.

Utilize the model to proactively identify at-risk customers and offer tailored support or alternative repayment options. Implement strategies to improve communication with customers about their energy usage and payment plans. Explore the feasibility of offering financial assistance programs or flexible payment arrangements to alleviate debt burdens.

The machine learning model offers a valuable tool for E.ON Next to address the challenge of rising customer debt levels. By identifying at-risk customers early and offering targeted support, we can help mitigate the impact of the cost of living crisis and foster better financial well-being among our customer base. This presentation outlines our approach, findings, and recommendations for leveraging machine learning to address the issue of customer debt repayment. Thank you for your attention.