Journal of Economics, Social, and Humanities

Implementation of Smart Digital Finance in Promoting Digital Financial Inclusion and Risk Management

Cindy Jasty Louisa — 2026-04-01

The development of digital finance has transformed financial systems in developing countries by enhancing efficiency, accessibility, and transparency, while simultaneously introducing new technological and institutional risks. This study analyzes the implementation of Smart Digital Finance in Indonesia and its role in promoting digital financial inclusion through the management of technological, trust, and security aspects. The study adopts a qualitative descriptive–analytical approach by systematically reviewing policy documents, institutional reports, and relevant academic literature.

The findings indicate that the development of interoperable digital payment infrastructure, particularly through QR-based payment systems, has expanded public access to formal financial services, improved operational efficiency through the utilization of digital transaction data, and supported the integration of micro and small enterprises into the formal economic system. Nevertheless, cybersecurity risks, personal data protection issues, digital fraud, and public trust remain major challenges to the sustainability of the digital financial system.

Therefore, this study emphasizes the importance of risk-sensitive regulation, strong consumer protection, secure digital identity systems, and effective institutional governance to ensure that Smart Digital Finance can promote inclusive economic growth and sustainable public financial governance in Indonesia.

Comparison of The Effectiveness of Biometric Security Technology, Encryption, Ai Fraud Detection Preventing Fraud on Online Loan Applicatons

Arinda Manar Rizqiani — 2026-04-01

The rapid growth of online lending services (peer to peer lending) in Indonesia as part of the financial technology (fintech) ecosystem has increased financial inclusion, but at the same time accompanied by a high risk of digital fraud and the rise of illegal platforms. The characteristics of fully digital services, minimal face-to-face interaction, and reliance on remote verification make online loan applications very vulnerable to various forms of fraud, such as identity theft, account abuse, synthetic identity, and loan stacking. This study aims to assess and compare the effectiveness of biometric security technology, encryption, and AI fraud detection in preventing fraud in online loan applications. The research method used is a literature review with a comparative descriptive approach to scientific articles, regulatory reports, and relevant fintech industry publications. The results of the study show that there is no single security technology that is completely effective when applied alone. Biometric technology has proven to be effective in the early stages of authentication to prevent identity misuse, encryption serves as a security foundation in protecting data confidentiality and integrity, while AI fraud detection demonstrates the most comprehensive effectiveness in detecting and preventing complex and dynamic fraud patterns. This study concludes that a multi-layer security approach that combines biometrics, encryption, and AI fraud detection is the most optimal strategy to minimize fraud risk, maintain operational efficiency, improve user comfort, and strengthen the trust and sustainability of the online lending industry in Indonesia.

Determinants of Early Financial Reporting in Indonesia’s Financial Sector: The Role of RegTech and Institutional Type

M. Daffa Fahada Lubis — 2026-04-01

This study examines the drivers of early financial reporting across Indonesia’s financial institutions. It highlights the influence of institutional type and technological adoption. While digital tools like RegTech are often promoted as solutions for timely compliance, our findings reveal that institutional characteristics, particularly being a financing institution, are far more decisive. Entities in the financing sector, not finance sector as a whole, are over 30 times more likely to report early with a probability increase of more than 50 percentage points compared to other sectors. In contrast, factors such as RegTech adoption, firm size, and IT infrastructure do not exhibit significant influence on reporting timeliness. These results suggest that organizational alignment with regulatory expectations and internal governance practices play a more critical role than digital maturity alone. Despite high levels of technology adoption, early reporting behavior remains uneven which shows reinforcing the notion that technology is not a standalone solution. The logistic regression model used in this study demonstrates strong predictive ability which emphasizes the importance of sectoral identity in shaping reporting outcomes. Policymakers are encouraged to move beyond one-size-fits-all digital mandates and instead develop targeted strategies that address the specific institutional contexts of financial entities

Algorithmic Vigilance: Implementasi Machine Learning dalam Screening Portofolio Investasi untuk Mendeteksi Manipulasi Saham (Studi Kasus PT. Asuransi Jiwasraya)

Prinody Kredo Cavaliere — 2026-04-01

The default case of PT Asuransi Jiwasraya reflects the weakness of investment supervision systems and risk management, particularly in detecting manipulative stocks. This study aims to implement algorithmic vigilance through machine learning to conduct early screening of investment portfolios. The method employed is a quantitative experimental approach with a case study of Jiwasraya using stock data from the Indonesia Stock Exchange for the period 2014–2019. The S.I.G.A.P (Smart Investment Governance & Analysis Protocol) model was developed based on Random Forest, integrating fundamental analysis, market anomalies, and corporate governance. Simulation results indicate that the model is capable of identifying high-risk stocks, such as MYRX and TRAM, prior to the occurrence of default. This research proves that machine learning is effective as a support tool for investment decision-making and for strengthening institutional risk management.

AL-AMIN (Algorithmic Learning and Automated Monitoring): Strategi Mitigasi Moral Hazard Fintech Syariah Melalui Integrasi API Point of Sales dalam Kerangka Triple Helix

Rifki Hilman Fauzi — 2026-04-01

Pertumbuhan Islamic Fintech di Indonesia menghadapi paradoks fundamental: meskipun akses pembiayaan meluas, adopsi akad berbasis bagi hasil (Mudharabah) mengalami stagnasi akibat tingginya risiko pembiayaan. Akar permasalahannya adalah defisit kepercayaan (trust deficit) yang dipicu oleh asimetri informasi, di mana mitra UMKM memiliki peluang melakukan moral hazard melalui praktik double bookkeeping (pembukuan ganda) untuk memanipulasi laporan bagi hasil. Metode mitigasi konvensional terbukti inefisien dalam memvalidasi integritas laporan keuangan UMKM secara real-time. Penelitian ini bertujuan merekonstruksi model pengawasan fintech syariah menggunakan metode kualitatif deskriptif dengan pendekatan conceptual model building. Hasil penelitian merumuskan inovasi sistem AL-AMIN (Algorithmic Learning and Automated Monitoring). Sistem ini mengintegrasikan tiga lapisan teknologi pertahanan: (1) Integrasi API Point of Sales (POS) sebagai instrumen validasi arus kas digital (single source of truth); (2) AI Anomaly Detection yang berfungsi sebagai auditor cerdas untuk mendeteksi ketidakwajaran korelasi antara data stok inventori dan volume penjualan; serta (3) Smart Contract untuk otomasi distribusi bagi hasil. Keberlanjutan sistem diperkuat melalui kerangka Triple Helix, dimana OJK berperan dalam manajemen blacklist nasional dan DSN-MUI memberikan legitimasi sanksi syariah (taqsir). Implementasi AL-AMIN diharapkan mampu mentransformasi ekosistem fintech dari trust-based menjadi data-driven trust, mewujudkan iklim investasi yang transparan, aman, dan sesuai prinsip syariah.