Deepfake-Driven Financial Fraud In Indonesia: A Systematic Review Of Economic Impacts And Regulatory Challenges

Muhammad Arsalan Khan; Hidayatul Khusnah; Emeralda Ayu Kusuma; Mar’a Elthaf Ilahiyah; Muhammad Zeeshan Khan

doi:10.29284/87d1j190

Authors

Muhammad Arsalan Khan Postdoctoral Fellow, Center for Data and Business Intelligence, Universitas Nahdlatul Ulama Surabaya, Indonesia
Hidayatul Khusnah Department of Accounting, Faculty of Business Economics and Digital Technology, Universitas Nahdlatul Ulama, Surabaya, Indonesia
Emeralda Ayu Kusuma Department of Management, Sekolah Tinggi Ilmu Ekonomi Indonesia
Mar’a Elthaf Ilahiyah Department of Accounting, Faculty of Economic and Business, Sekolah Tinggi Ilmu Ekonomi Indonesia
Muhammad Zeeshan Khan Department of Computer science, Islamia College Peshawar Khyber Pakhtunkhwa, Pakistan

DOI:

https://doi.org/10.29284/87d1j190

Keywords:

Deepfake, financial fraud, fintech, biometric spoofing, regulatory framework

Abstract

Deepfake technology, utilizing advanced artificial intelligence to create hyper-realistic synthetic media, poses a significant threat to Indonesia’s financial sector, exploiting the nation’s rapid digital transformation. This study synthesizes over 50 secondary sources, including academic papers, industry reports, and regulatory documents from 2020 to October 2025, to assess the economic impact and regulatory responses to deepfake-enabled financial fraud. Findings reveal losses exceeding $138.5 million, driven by a 1,550% surge in AI-driven fraud within fintech from 2022 to 2024, primarily through biometric spoofing and impersonation scams. These incidents undermine financial inclusion for 66 million unbanked Indonesians and erode trust in digital platforms. Regulatory efforts, led by the Financial Services Authority and Ministry of Communication and Digital Affairs, include ethical AI guidelines and enhanced biometric protocols, reducing fraud attempts by 20–30%. However, gaps in specific legislation and low digital literacy (42% proficiency) hinder enforcement. Limitations include underreported losses and reliance on potentially outdated secondary data. Future research should incorporate primary data and explore emerging fraud vectors. Recommendations advocate for a dedicated deepfake law, inter-agency coordination, advanced detection tools, and public education to ensure a resilient financial ecosystem.

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