Promotion Abuse Fraud Detection Application Development using Risk Scoring
Shafira Naya Aprisadianti, Latifa Dwiyanti · 2023
Promotion abuse fraud is promotion abuse by duplicating accounts to gain an advantage over promotional codes fraudulently. This action inflicts a financial loss on the company. Therefore, this study aims to deal with fraud by developing an application to detect promotion abuse fraud. The application development process includes needs analysis, modeling, and application development. The dataset used for modeling comes from an e-commerce company in Indonesia. The dataset collection stage includes calculating the similarity between accounts using the Levenshtein distance similarity algorithm to get additional features, such as the number of accounts that are similar to an account. At the modeling stage, experiments were carried out using the Random Forest algorithm and a risk scoring algorithm based on machine learning, namely FasterRisk. The FasterRisk has better performance than the Random Forest algorithm shown by a higher F1 score and AUC score. The algorithm also has an advantage in terms of interpretability because it has an output in the form of a more understandable risk score model, so users can understand the factors that are fraud indicators. The FasterRisk prediction result is then deployed into a web application based on user needs.