Abstract:The development of Islamic microfinance institutions requires the implementation of financing schemes that are not only compliant with Sharia principles but also capable of maintaining financing quality and sustainability.…
y. One of the most widely applied contracts is murabahah financing, particularly in supporting Micro, Small, and Medium Enterprises (MSMEs). This study aims to analyze the implementation of murabahah financing and the risk mitigation strategies applied at BMT Alif Mandiri Makassar. This research employs a qualitative approach with a descriptive-analytical method. Data were collected through field observations, in-depth interviews with management and financing officers, and documentation studies. The findings indicate that murabahah financing at BMT Alif Mandiri Makassar is implemented regularly through several stages, including application submission, feasibility analysis, financing approval, contract realization, and post-disbursement monitoring. The financing analysis emphasizes repayment capacity, members' character, and the suitability of financed goods for productive business needs. Risk mitigation strategies are conducted through careful customer character assessment, direct business verification, proportional margin determination, the use of collateral as a financing safeguard, and continuous monitoring. This study concludes that productive murabahah financing supported by integrated risk management practices is an effective and sustainable financing instrument for the development of MSMEs within Islamic microfinance institutions
Abstract:This study aims to analyze the effects of competence, reward systems, and training on the performance of executive officers in Sharia Rural Banks (BPRS), with job satisfaction as an intervening variable. The primary focus…
s is to assess the extent to which these factors influence executive performance within the context of Islamic microfinance institutions. Employing a quantitative, explanatory design, primary data were collected via a Likert-scale questionnaire and analyzed using structural equation modeling–partial least squares (SEM–PLS) with SmartPLS. The study population consists of executive officers of BPRS in Indonesia. Using purposive sampling, 121 responses were obtained from multiple BPRS across several regions. The results show that (1) competence has a positive effect on performance; (2) job satisfaction positively affects performance; (3) the reward system influences performance primarily through job satisfaction (partial/competitive mediation), while its direct effect on performance tends to be negative; and (4) training does not exhibit a significant effect on performance, and its indirect path via job satisfaction is not significant. These findings highlight the importance of strengthening competence and redesigning reward systems in alignment with executive expectations to enhance job satisfaction and performance.
Abstract:This study examines the impact of Microfinance Institutions' (MFIs) performance on economic growth in Cambodia, using annual panel data from 62 MFIs for the period 2017–2023. Employing advanced econometric techniques, the…
the findings reveal nuanced relationships between key indicators of MFI performance and GDP growth. Notably, Non-Performing Loans (NPLs) show an unexpected positive relationship with GDP growth, highlighting the Cambodian microfinance sector's resilience in mitigating adverse effects through sustained economic activity. Inflation is also positively associated with GDP growth, suggesting that moderate inflation can drive economic expansion, though careful management is necessary to avoid destabilization. Conversely, the study finds a negative relationship between the number of MFIs and GDP growth, indicating potential inefficiencies from sector oversaturation. Lastly, a positive link between Return on Equity (ROE) and GDP growth underscores the importance of profitability in ensuring financial stability and economic development. The findings emphasize the need for policy measures to manage sector growth, maintain moderate inflation, and enhance MFI profitability for sustainable economic progress in Cambodia.
Keywords: Microfinance Institutions (MFIs); Cambodia; Economic Growth.
Abstract:Abstract: Non-performing loans remain one of the main challenges faced by cooperatives, particularly when the loan eligibility assessment process is still conducted manually. This traditional approach tends to be time consuming,…
nsuming, subjective, and prone to inaccurate decisions. This study aims to develop a predictive model for borrower eligibility using the Support Vector Machine (SVM) algorithm as a more efficient and objective machine learning-based solution. A total of 1,000 loan history records were processed using RapidMiner software, taking into account variables such as salary, years of employment, loan amount, monthly installment, employment status, monthly expenses, number of dependents, housing status, age, and collateral value. The model’s performance was evaluated using a confusion matrix and classification metrics including accuracy, precision, recall, and kappa. The results indicate that the SVM model achieved an accuracy of 90.05%, precision of 90.13%, recall of 90.05%, and f1 score of 90,08%, reflecting a strong performance in classifying borrower eligibility. The application of this method makes a significant contribution to the development of data driven decision support systems within cooperative environments. This finding expands the scientific understanding in the field of microfinance and supports the implementation of artificial intelligence technologies in making decisions that are more precise, rapid, and accurate.
Keywords: cooperative; eligibility prediction; machine learning; non-performing loan; SVM
Abstrak: Kredit macet merupakan salah satu permasalahan utama yang dihadapi koperasi, terutama ketika proses penilaian kelayakan peminjam masih dilakukan secara manual. Pendekatan ini cenderung lambat, subjektif, dan berisiko menghasilkan keputusan yang kurang akurat. Penelitian ini bertujuan untuk membangun model prediksi kelayakan peminjam menggunakan algoritma Support Vector Machine (SVM) sebagai solusi berbasis machine learning yang lebih efisien dan objektif. Sebanyak 1.000 data riwayat pinjaman diolah menggunakan tools RapidMiner dengan mempertimbangkan variabel: gaji, lama bekerja, besar pinjaman, angsuran per bulan, status pegawai, pengeluaran bulanan, jumlah tanggungan, status rumah, umur, dan nilai jaminan. Evaluasi model dilakukan menggunakan confusion matrix dan metrik klasifikasi seperti akurasi, presisi, recall, dan kappa. Hasil menunjukkan bahwa model SVM mencapai akurasi 90,05%, presisi 90,13%, recall 90,05%, dan f1 score 90,08%, yang mencerminkan performa model yang sangat baik dalam mengklasifikasikan kelayakan peminjam. Penerapan metode ini memberikan kontribusi penting dalam pengembangan sistem pendukung keputusan berbasis data di lingkungan koperasi. Temuan ini memperluas wawasan keilmuan di bidang keuangan mikro dan mendukung penerapan teknologi kecerdasan buatan dalam pengambilan keputusan yang lebih tepat, cepat, dan akurat.
Kata Kunci: koperasi; kredit macet; machine learning; prediksi kelayakan; SVM