Sistem Deteksi Fraud Menggunakan Data Mining, Data Warehouse, dan OLAP di Bank of India Indonesia

Abstract

This study aims to analyze and develop an advanced fraud detection system for bank of India Indonesia by leveraging data mining, data warehouse, and Online Analytical Processing (OLAP) technologies. fraud remains a critical challenge in the banking sector, posing significant risks to financial institutions and their customers. As transaction volumes grow and data complexity increases, conventional fraud detection methods have proven inadequate. Employing a quantitative research approach, this study utilizes data analytics techniques to identify anomalous transaction patterns. The findings demonstrate that integrating data mining and OLAP improves fraud detection accuracy by approximately 30% compared to traditional methods. The study concludes that adopting advanced information technology systems is essential for safeguarding banking operations, enhancing security, and maintaining customer trust. Furthermore, the practical implications suggest that implementing sophisticated fraud detection mechanisms can significantly enhance banking industry performance by mitigating financial losses and reinforcing customer confidence.