Enhancing Fraud Detection in Utility Consumption Using Neural Networks - A Comparative Study

Amidst an era of speedy technological growth, fraud is a complex challenge. This article presents an innovative analytical method that uses utility consumption data to identify probable instances of fraud in utility-based services. This study evaluates the efficacy of neural networks, specifically Artificial Neural Network (ANN) — a subset of neural networks, compared to Light Gradient Boosting Machine (LGBM), a tree-based model. It uses data from the Zindi challenge to analyze their capability to detect fraudulent consumption patterns. The article focuses on optimizing features using Pearson correlation and discusses the difficulties associated with imbalanced data sets. The LGBM model has exceptional performance, as seen by its impressive ROC AUC score of 0.878242. This number highlights its remarkable ability to differentiate fraudulent actions from ANN, thus establishing future advancements in fraud detection in utility-based services.