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"Penelitian ini mencoba menjawab pertanyaan tentang apakah data trade credit termasuk data yang dikelola oleh biro kredit mengingat pentingnya peranan trade credit dalam perekonomian. Dalam menjawab pertanyaan tersebut, penelitian yuridis-normatif ini meninjau praktik-praktik biro kredit di beberapa negara di dunia, terutama dari segi kerangka hukum, untuk melihat tentang keberadaan pengelolaan data trade credit (utang dagang) oleh biro kredit di negara-negara tersebut. Selain tentang pengelolaan data trade credit, penelitian ini juga mempelajari ketentuan-ketentuan hukum yang memberikan perlindungan terhadap data kredit yang dikelola.Data trade credit ternyata adalah data yang telah dikelola oleh biro kredit di negara-negara tersebut. Mereka bahkan telah memasukkan unsur trade credit secara eksplisit dalam definisi kredit pada umumnya, tidak seperti definisi kredit di Indonesia yang hanya terbatas pada kredit oleh lembaga keuangan. Negara-negara tersebut juga memiliki kerangka hukum terhadap perlindungan data yang lebih komprehensif daripada yang ditemui di Indonesia. Penelitian ini juga mendapatkan beberapa pembelajaran dari negara-negara tersebut tentang struktur biro kredit yang dapat mengatasi kendala-kendala yang berpotensi muncul dalam pengelolaan informasi trade credit di Indonesia.

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This research attempts to answer the question on whether trade credit is processed by credit bureaus, owing to the important role trade credit plays in the economy. In the quest for the answer, this legal-normative research looks at the practices of credit bureau in several countries in the world, especially at their legal and regulatory frameworks, to observe the existence of such trade credit processing by the credit bureaus in those countries. Aside from the trade credit processing, this research also learns about the various legal rules providing for the protection of the credit data processed by the credit bureaus.

The processing of trade credit is already part of the operation of the credit bureaus in those countries. They have also explicitly included the trade credit elements in their definition of credit, unlike the credit definition in Indonesia which is still largely limited to the credit by financial intermediaries. Those countries also employ more comprehensive legal frameworks of data protection than the one found in Indonesia. This research also benefits from some learning on the credit bureau structures in those countries, which can be used to address the constraints potentially arising in the trade credit processing in Indonesia."

"Penelitian ini membahas kemampuan model Nelson-Siegel mengestimasi credit spread obligasi korporasi dengan rating AAA, AA dan A di Indonesia. Hasil dari estimasi yield curve Nelson-Siegel digunakan sebagai penentu estimasi credit spread. Selanjutnya hasil estimasi dibandingkan dengan credit spread IBPA untuk melihat tingkat akurasi terhadap credit spread aktual di pasar. Proyeksi credit spread juga dilakukan dengan metode otoregresi pada data beta, yield dan credit spread hasil estimasi model Nelson-Siegel. Credit spread proyeksi ketiga variabel tersebut kemudian dibandingkan dengan credit spread aktual dan credit spread IBPA untuk menentukan metode proyeksi yang paling akurat. Estimasi dan proyeksi credit spread dapat berguna bagi investor dalam menghitung nilai wajar obligasi serta menentukan waktu yang tepat dalam berinvestasi pada obligasi korporasi yang memberikan yield lebih tinggi dari obligasi pemerintah.

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This research discusses the Nelson-Siegel model capabilities to estimate the credit spread of corporate bonds with a rating of AAA, AA and A in Indonesia. The results of the yield curve estimated using the Nelson-Siegel models are used as a determinant of the estimated credit spread. Furthermore, the results compared with the credit spread estimated by IBPA to look at the accuracy toward the actual credit spread in the market. Credit spread projection is also performed using the autoregression in beta, yield and credit spread resulted from NelsonSiegel model estimation. Credit spreads projection of these three variables were then compared with the actual credit spread and IBPA credit spread to determine the most accurate projection method. Credit spread estimation and projection can be useful for investors in calculating the fair value of bonds and determining the right time to invest in corporate bonds that yielding higher than government bonds."

"Pandemi Covid-19 telah mendisrupsi aktivitas global di semua sector. Sebagai upaya menekan penyebaran virus, pembatasan aktivitas berdampak pada merosotnya aktivitas ekonomi. Untuk memenuhi kebutuhan likuiditas akibat keterbatasan kreditor, perbankan dapat meningkatkan non-core liabilities dari perbankan yang tidak leluasa menyalurkan kredit karena menurunnya permintaan kredit. Aktivitas ini meningkatkan interkonektivitas perbankan yang berasal dari neraca. Penelitian ini bertujuan untuk mengetahui tren interkonektivitas perbankan selama pandemi Covid-19 dan pengaruhnya terhadap kontribusi risiko sistemik setiap individual bank di Indonesia. Penelitian ini menggunakan the generalized method-of-moments (GMM) estimator yang dikembangkan untuk dynamic models of panel data. Hasil penelitian menunjukkan adanya tren peningkatan interkonektivitas selama pandemi dan mendapatkan pengaruh positif dan signifikan interkonektivitas terhadap kontribusi risiko sistemik setiap individual bank pada periode sebelum pandemi Covid-19, sementara selama pandemi didapatkan pengaruh yang negatif. Temuan ini menyiratkan bahwa pengaruh interkonektivitas terhadap kontribusi risiko sistemik setiap individual bank bervariasi waktu dan ukuran bank memainkan peran penting dalam menentukan kontribusi risiko sistemik setiap individual bank.

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The Covid-19 pandemic has disrupted global activity in all sectors. In an effort to minimize the the spread of the virus, restrictions on activities have led to a decline in economic activity. This has led some banks to meet their funding needs by increasing non-core liabilities. At the same time, other banking groups may no longer have the flexibility to extend credit due to less credit demand. This activity may increase banking interconnectedness from the balance sheet. This study aims to describes the trend of banking interconnectedness during the Covid-19 pandemic and its impact on the systemic risk contribution of the Indonesian banking industry. This study used the generalized method-of-moments (GMM) estimator developed for dynamic models of panel data. This study found an increasing trend in bank interconnectedness during the Covid-19 pandemic. In addition, this study also discovered a positive effect of bank interconnectedness on systemic risk contributions in the period before the Covid-19 pandemic, while a negative effect on systemic risk contributions was found during the pandemic. These findings imply that the effect of interconnectedness on the banking systemic risk contribution varies over time and the size of the bank plays an important role in determining the contribution of systemic risk."

"Penelitian ini bertujuan untuk menganalisis peningkatan pengendalian internal yang dapat dilakukan PT X terkait pengelolaan piutang dan sewa pasca penerapan PSAK 71 dan PSAK 73. Analisis dilakukan dengan membandingkan antara pengendalian internal existing dengan Kerangka Pengendalian Internal COSO untuk mengidentifikasi area yang membutuhkan peningkatan. Data diperoleh melalui tinjauan dokumen dan wawancara dengan lima narasumber. Hasil analisis menunjukkan bahwa PT X sudah mengidentifikasi risiko terkait penerapan PSAK 71 dan 73 serta meningkatkan pengendalian internalnya untuk memitigasi risiko. Tetapi tingkat penyisihan penurunan nilai piutang perusahaan terus meningkat dan rasio solvabilitas perlu dijaga agar tidak melanggar ketentuan debt covenants. Penelitian merekomendasikan agar PT X menerapkan kebijakan kredit yang ketat dengan seleksi pelanggan, penerapan batasan kredit dan jaminan yang berkualitas serta menerapkan software perhitungan expected credit loss untuk meningkatkan pengelolaan piutang. Penelitian juga merekomendasikan agar PT X melakukan analisis strategi pengadaan aset dan membenahi administrasi kontrak untuk meningkatkan pengelolaan sewa.

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This research aims to analyze the improvement of internal control that can be done by PT X related to the management of accounts receivable and leases after the implementation of PSAK 71 and PSAK 73. The analysis is carried out by comparing existing internal controls with COSO's Internal Control-Integrated Framework to identify areas that need improvement. Data were obtained through document review and interviews. The results of the analysis show that PT X had identified risks related to the implementation of PSAK 71 and 73 and improved its internal control accordingly. However, the level of loan loss provisioning for trade receivables continues to increase and the solvency ratio needs to be maintained to comply with debt covenants. Research recommends that PT X implement a strict credit policy with customer selection, credit limits, and quality guarantees, and also implement an expected credit loss calculation software to improve account receivables management. Research also recommends that PT X conduct an analysis of asset procurement strategies and improve contract administration to improve lease management."

"Penelitian ini bertujuan mengidentifikasikan dampak Program BPD Regional Champion terhadap tingkat NPL BPD yang merupakan proxy risiko kredit. Dengan menggunakan analisis data panel, model terpilih yaitu Random Effects Model mengidentifikasikan determinan NPL dari faktor internal dan eksternal bank. Model tersebut kemudian disimulasikan dengan given condition berupa target indikatif keberhasilan program yaitu pertumbuhan kredit minimum 20% dan porsi kredit produktif minimum 40%. Hasil penelitian menunjukkan bahwa tingkat NPL estimasi selama periode 2008-2011 bagi 12 bank akan lebih tinggi dibandingkan NPL riil sementara bagi 13 bank lain sebaliknya.

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Focus of this research is to identify the impact of BPD Regional Champion Program to each bank's NPL. Using a panel data analysis for the observation period of 2008 - 2011, the selected random effects model identifies the determinant of NPL from bank specific variables and external factors. The Model is then simulated by the indicative targets of minimum loan growth of 20% and minimum portion of productive loan of 40%. This research reveals that the Program may cause higher estimated NPL for 12 banks while for the other 13 banks the estimated NPL will be lower than the real NPL."