Artificial Intelligence and Machine Learning in Financial Industry Transformation: A Comparative Analysis of Conventional and Sharia Fintech in Indonesia

Abstract

Purpose: This study examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in transforming Indonesia’s financial industry by comparing their implementation in a conventional fintech company (JULO) and a Sharia-compliant fintech company (ALAMI). The study focuses on differences in AI adoption, value orientation, and their implications for operational efficiency and ethical compliance. Method: This research employs a qualitative comparative case study using secondary data sources, including corporate reports and relevant literature. The analysis is guided by the Technology–Organization–Environment (TOE) framework, Socio-Technical Systems Theory, and Maqasid al-Shariah. Result: The findings show that both companies utilize AI and ML to enhance decision-making and operational efficiency. However, JULO prioritizes speed and scalability, while ALAMI integrates Sharia principles such as justice, transparency, and the avoidance of riba. Implication: The study highlights the importance of aligning AI innovation with ethical and Sharia values in Islamic fintech development. Originality: This study offers a comparative, value-based analysis of AI-driven fintech from conventional and Sharia perspectives in Indonesia.