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All Journal Aptisi Transactions on Management Aptisi Transactions on Technopreneurship (ATT) ADI Journal on Recent Innovation (AJRI) ADI Bisnis Digital Interdisiplin (ABDI Jurnal) Jurnal MENTARI: Manajemen, Pendidikan dan Teknologi Informasi Journal of Computer Science and Technology Application
Rizky, Agung
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Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Other

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Leveraging Big Data Analytics to Strategically Expand Digital Microcredit Access for MSMEs Rizky, Agung; Ramaditya, Muhammad; Kamal, Abdullah Arif
ADI Journal on Recent Innovation Vol. 7 No. 1 (2025): September
Publisher : ADI Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/ajri.v7i1.1325

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a pivotal role in driving economic development and job creation, especially in emerging economies. However, limited access to formal credit remains a persistent challenge due to the reliance on conventional financial assessments that often exclude MSMEs with informal or incomplete financial histories. This study aims to investigate how big data analytics can be effectively leveraged to strategically expand digital microcredit access for MSMEs, offering more inclusive and accurate credit evaluation models. The research adopts a qualitative descriptive methodology, incorporating a comprehensive literature review and multiple case studies of fintech platforms that utilize alternative data sources such as e commerce transactions, mobile phone activity, utility bill payments, and social media engagement to construct alternative credit scoring systems. The findings indicate that big data enables improved risk profiling, faster loan processing, and wider financial inclusion by reaching unbanked and underbanked MSMEs. Additionally, the integration of machine learning algorithms in analyzing real time behavioral data enhances decision making precision and operational efficiency in digital lending. However, the study also raises critical issues regarding data privacy, ethical use, and transparency in automated credit decisions. In conclusion, the use of big data analytics offers transformative potential to reshape digital microcredit services, empowering MSMEs through accessible, scalable, and intelligent financial solutions that align with broader goals of sustainable economic inclusion and digital transformation.
Co-Authors Ahmad Gunawan Amsyar, Izwan Ardiansyah, Zulfadli Arif Andika Asri asri Audiah, Sipah Aulia Lindzani, Nur Bagong Suyanto Beldiq, Eiser Aaron Cristhopher, Ethan Dyatmika, Sutama Wisnu Ekawaty, Anita Harris, Ethan Hua, Chua Toh Kamal, Abdullah Arif Komara, Maulana Arif Madani, Muchlisina Maria Daeli, Ora Plane Mkhize, Thabo Mochamad Heru Riza Chakim Ndlovu, Zinhle Ninda Lutfiani Pasha, Lukita Ramadan, Ahmad Ridwan Arifin Rodriguez, Marta Santiago Ikhsan, Ramiro Setijaningrum, Erna Sukendri, Nengah Sumliyah, Sumliyah Tanjung, Azrul Tantri Wenny Sitanggang Untung Rahardja Utami, Rahayu Tri Yul Ifda Tanjung, Yul Ifda Zainarthur, Henry Zaki Firli, Muhammad