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Driving Financial Inclusion in Indonesia with Innovative Credit Scoring

Author

Listed:
  • Latif Adam

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Jiwa Sarana

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Bitra Suyatno

    (Directorate General for Financial Sector Stability and Development, Financial Policy Agency, Ministry of Finance of the Republic of Indonesia, Jakarta 10710, Indonesia)

  • Muhammad Soekarni

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Joko Suryanto

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Tuti Ermawati

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Yeni Saptia

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Septian Adityawati

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Erla Mychelisda

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

  • Yogi Pamungkas

    (Directorate General for Financial Sector Stability and Development, Financial Policy Agency, Ministry of Finance of the Republic of Indonesia, Jakarta 10710, Indonesia)

  • M. Rifqy Nurfauzan Abdillah

    (Directorate General for Financial Sector Stability and Development, Financial Policy Agency, Ministry of Finance of the Republic of Indonesia, Jakarta 10710, Indonesia)

  • Lisa Angelia

    (Directorate General for Financial Sector Stability and Development, Financial Policy Agency, Ministry of Finance of the Republic of Indonesia, Jakarta 10710, Indonesia)

  • Mahmud Thoha

    (Research Center for Macroeconomics and Finance, National Research and Innovation Agency, Jakarta 12710, Indonesia)

Abstract

Innovative Credit Scoring (ICS) holds promise for reshaping financial inclusion in Indonesia, offering a potent alternative to conventional credit assessments that often exclude underserved populations. By leveraging alternative data—from telco records to e-commerce and social media footprints—and AI/ML technologies, ICS can deliver more accurate, inclusive, and responsive credit evaluations. However, its potential is constrained by structural inefficiencies and weak regulatory frameworks. This study employs a qualitative, exploratory design based on eight focus group discussions with 36 stakeholders, including regulators, financial institutions, data providers, and academics. Thematic analysis reveals three core barriers: fragmented regulation, limited data interoperability, and algorithmic opacity. To address these challenges, the paper recommends four policy priorities: (1) enforce and expand POJK 29/2024; (2) establish interoperable, integrated MSME data systems; (3) mandate algorithm audits to reduce bias and opacity; and (4) invest in digital infrastructure to close regional access gaps. Without these systemic shifts, ICS may fall short of its inclusive promise.

Suggested Citation

  • Latif Adam & Jiwa Sarana & Bitra Suyatno & Muhammad Soekarni & Joko Suryanto & Tuti Ermawati & Yeni Saptia & Septian Adityawati & Erla Mychelisda & Yogi Pamungkas & M. Rifqy Nurfauzan Abdillah & Lisa , 2025. "Driving Financial Inclusion in Indonesia with Innovative Credit Scoring," JRFM, MDPI, vol. 18(8), pages 1-20, August.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:8:p:442-:d:1719830
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    References listed on IDEAS

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    1. Sanjiv Das & Richard Stanton & Nancy Wallace, 2023. "Algorithmic Fairness," Annual Review of Financial Economics, Annual Reviews, vol. 15(1), pages 565-593, November.
    2. World Bank, 2018. "Developing and Operationalizing a National Financial Inclusion Strategy," World Bank Publications - Reports 29953, The World Bank Group.
    3. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    4. Tatjana Miljkovic & Pei Wang, 2025. "A dimension reduction assisted credit scoring method for big data with categorical features," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-30, December.
    5. Elias Shohei Kamimura & Anderson Rogério Faia Pinto & Marcelo Seido Nagano, 2023. "A recent review on optimisation methods applied to credit scoring models," Journal of Economics, Finance and Administrative Science, Emerald Group Publishing Limited, vol. 28(56), pages 352-371, June.
    6. Ceylan Onay & Elif Öztürk, 2018. "A review of credit scoring research in the age of Big Data," Journal of Financial Regulation and Compliance, Emerald Group Publishing Limited, vol. 26(3), pages 382-405, July.
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