The role of bank and startup fintech P2P lending in supporting financial credit for Indonesian farmers

Abstract

One of the challenges faced by farmers is securing capital for the development of their agricultural businesses. Banks and peer-to-peer (P2P) lending fintech startups employ various business models to assist farmers in obtaining the necessary capital. This study investigates the credit financing schemes available to farmers through banks and P2P lending fintech startups. The research, which utilized a qualitative approach, involved collecting both primary and secondary data. Primary data were gathered through comprehensive interviews with two academic experts in the agricultural business sector and five leaders of agri-tech startup companies. Secondary data included: (1) annual financial reports from BRI, Mandiri, and BNI; (2) statistical reports on P2P lending providers from the Financial Services Authority (OJK); and (3) models of financing schemes for farmers derived from a range of empirical sources. A descriptive analysis was subsequently conducted to explore the various financing schemes available to farmers through banks and P2P lending fintech startups, as well as to assess the performance of these financing programs via data on the rate of non-performing loans (NPLs). The findings indicate that the financing schemes implemented by banks predominantly focus on economic factors to facilitate loan repayment. In contrast, P2P lending fintech startup schemes emphasize both economic and social aspects, including enhancing farmers' knowledge in implementing Good Agricultural Practices (GAP) and improving financial literacy, aiming to ensure smooth loan repayments. Furthermore, the study observed an increase in the value of Non-Performing Loans (NPL) among both banks and P2P lending fintech startups during the Covid-19 pandemic.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 8 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.