Journal articles on the topic 'Bank loans Data processing'
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Harimurti, Cundo, Pandoyo Pandoyo, and Mohammad Sofyan. "FACTORS AFFECTING NON-PERFORMING LOANS IN STATE-OWNED BANKING." International Journal of Economics, Business and Accounting Research (IJEBAR) 6, no. 2 (June 27, 2022): 958. http://dx.doi.org/10.29040/ijebar.v6i2.5273.
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This research aims to analyze the influence of macroeconomic factors projected by inflation and bank-specific factors used, namely Return on Asset, Equity to Asset Ratio, and Bank Size on Non-Performing Loan of State-Owned Banking for the period 2017-2021. This type of research is a causal associative study because it was conducted to determine the effect of Return on Assets, Equity to Asset Ratio, Inflation, and Bank Size on Non-performing State-owned banking Loans for the period 2017-2021. This research data analysis method uses data panel analysis as a data processing tool using EViews version 10. Return on Assets has a significant negative effect on Non-Performing Loans. Equity to Asset Ratio and bank size have a significant positive effect on Non-Performing Loans. Whereas inflation has a positive effect on Non-Performing Loans
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Rulinda Hijjas, Zakina, and Rini Agustiya. "The Impact Of Monetary Policy On Indonesian Bank Loans." ASIAN Economic and Business Development 5, no. 1 (October 30, 2022): 17–26. http://dx.doi.org/10.54204/aebd/vol5no1october2022002.
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This article discusse monetary policy's impact on bank borrowing. The data selected are Indonesian state data and the annual research period for 13 years from 2008 - 2020 with secondary data from the world bank. This study investigates wide money as a percentage of Gdp, Interest payment percent of expense, Domestic credit to private sector by banks percent of GDP. This study uses a quantitative method with an autoregressive vector model with the results of data processing showing that there is no reciprocal or two-way relationship between the three variables. This study found that monetary policy's impact can have macroeconomic action by increasing or limiting the supply of bank loans. This is evidenced by the different magnitudes of growth in lending in various sectors reflecting the growing effects of monetary policy.
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Hernando, Alben, Elvaretta Miranda, Lauw Luvena Aileen Theodora, Yohanes B. Kadarusma, and Glisera Agri Ariyan. "Dampak Faktor Makroekonomi terhadap Non Performing Loan pada Kredit Produktif Bank Umum di Indonesia." Studi Akuntansi dan Keuangan Indonesia 3, no. 1 (June 15, 2020): 1–28. http://dx.doi.org/10.21632/saki.3.1.1-28.
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This study aims to find out the factors that affect non-performing loans of productive loans of commercial banks registeredin oritas Jasa Keuangan. This study examined the macroeconomic variables that affect productive credit NPLs. The sample used is a commercial bank in Indonesia, which is registered in OJK. Productive credit NPL data are taken quarterly from 2003 to 2017,and 56 observationsare obtained. The regression model used in this study is ARDL, with the help of processing Eviews 10 software. The macroeconomic variables tested in this research consist of GDP growth, loan interest rates growth, and IDR/ USD exchangerate growth. GDP growth and exchange rate growth has a negative relationship, while loan interest rates growth does not have a significant relationship with the dependent variable.
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Rosmita Rasyid, Vanesha Valentina,. "Faktor-Faktor Yang Mempengaruhi Kinerja Keuangan Perbankan." Jurnal Paradigma Akuntansi 4, no. 1 (January 20, 2022): 424. http://dx.doi.org/10.24912/jpa.v4i1.17562.
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The purpose of this study is to find out and analyze the influence of capital adequacy ratio, non performing loan, liquidity, operational efficiency, and bank size on financial performance of go public banks listed on Indonesian Stock Exchange during 2017-2019. The research method used was purposive sampling with sample size of 40 banks in accordance with the criteria. The data processing technique uses multiple linear regression analysis with the EViews 11 program. The results showed that capital adequacy ratio, operational efficiency, and bank size have a significant effect on banking financial performance, while non-performing loans and liquidity did not have a significant effect on banking financial performance.
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Wiranegara, Muhammad Andhika. "Pengaruh Penyaluran Kredit Usaha Rakyat, Non Performing Loan,Tingkat Suku Bunga Bank Indonesia dan CAR Terhadap Profitabilitas (Studi Kasus Pada PT Bank Rakyat Indonesia (PERSERO) TBK Periode 2010-2017)." JAF- Journal of Accounting and Finance 3, no. 1 (August 6, 2019): 24. http://dx.doi.org/10.25124/jaf.v3i1.2109.
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The purpose of this study was to determine whether the level of People's Business Credit distribution, non-performing loans, Bank Indonesia interest rates and CAR can affect the level of profitability (Return On Asset) of PT Bank Rakyat Indonesia (Persero) Tbk, this study using secondary data sourced from the quarterly financial statements in the period 2010-2017. In managing the data that is owned, the author uses the SPSS version 20 data processing application. The data analysis technique used is multiple linear regression and to test the hypotheses of this study using t-statistical tests to test hypotheses partially and f-statistical tests to test hypothetically simultaneous. From the results of the tests that have been carried out in the Business Credit distribution, the interest rates of Bank Indonesia and CAR do not partially affect Return On Assets, while the non-performing loans affect Return On Assets. Simultaneously, the variable of People's Business Credit distribution, non-performing loans, Bank Indonesia interest rates and CAR has an effect on Return On Asset of 71.4 percent and the other is influenced by variables other than those studied.
Key notes : Kredit Usaha Rakyat, Non Performing Loan, tingkat suku bunga Bank Indonesia, Capital Adequacy Ratio, Return On Asset.
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Yunawati, Sri. "Analisis Perbedaan Kinerja Keuangan Bank Umum Milik Negara Konvensional dan Bank Umum Syariah di Indonesia." Al-Buhuts 15, no. 2 (December 31, 2019): 121–30. http://dx.doi.org/10.30603/ab.v15i2.1104.
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This study aims to to see what there are differences between the financial performance of BUMNN conventional and bank syariah, if reflected in the following the ratio of the CAR , non-performing loans to outstanding loans , ROE , ROA , BOPO, and LDR .In this research put it through statistical analysis of the ratio of descriptive financial used with describing the results of the highest value , the lowest score , the average, and the outcomes of the against a standard deviation, of a variable researched .In this research in conducting statistical testing data processing using kruskal walls, who was one of the probe in statistics non-parametrik who frequently used to test some samples who are not in charge. Based on tests carried out so the results showed that the financial performance of BUMN conventional and Bank Syariah seen from ratio car slightly went up , non-performing loans to outstanding loans, ROE , ROA , BOPO , and LDR there are significant differences.
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Widyaningsih, Novita, and Hersugondo Hersugondo. "INKLUSI KEUANGAN DAN PROFITABILITAS BANK DI INDONESIA." Jurnal Ilmu Manajemen dan Akuntansi Terapan (JIMAT) 12, no. 2 (August 15, 2021): 175. http://dx.doi.org/10.36694/jimat.v12i2.327.
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This study was conducted to examine the effect of financial inclusion and bank profitability. The data test method is done manually, which is obtained from the Bloomberg database and the annual reports of banks listed on the Indonesia Stock Exchange (IDX) in the 2017-2019 period with a total sample of 17 banks. The data from this study are included in the type of panel data and the data processing technique used is in the form of Least Square Analysis (OLS) using SPSS version 23. The results show that the amount of loans and the number of automated teller machines (ATMs) have a negative and significant effect on bank profitability. meanwhile, the number of bank branches has a positive and significant impact on the profitability of banks in Indonesia.
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Safitri, Julia, and Yuridistya Primadhita. "The Role of Credit Risk as Mediating the Effect of Liquidity on Sharia Banking Performance." Perisai : Islamic Banking and Finance Journal 6, no. 1 (March 19, 2022): 40–50. http://dx.doi.org/10.21070/perisai.v6i1.1580.
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This study aims to examine and analyze the relationship of the influence of liquidity on bank performance mediated by credit risk. Using data on Islamic banking companies listed on the IDX in 2013-2019. The methodology of this research was carried out to achieve the objectives of this study, namely how the influence of liquidity on the performance of Islamic banking companies in Indonesia which is mediated by credit risk. By processing data from data collected from the pre-pandemic and during the pandemic, this research can prove the proposed hypothesis. The analytical tool used is SEM-PLS with WarpPLS 7.0 application. The results of this study indicate that credit risk can partially mediate the relationship between the influence of liquidity on bank performance. This study succeeded in proving that the influence of liquidity on bank performance is acceptable and can be mediated by credit risk. This is in line with the Commercial Loan Theory which explains that providing loans to short-term and productive customers can minimize customer defaults, so that the company's performance will be maintained. During the current pandemic, it is one of the things that makes companies careful in managing liquidity as well as in distributing credit. Banks must be really selective in choosing loans submitted by customers, in order to avoid defaults that cause a decline in bank performance.
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Al-qerem, Ahmad, Ghazi Al-Naymat, Mays Alhasan, and Mutaz Al-Debei. "Default Prediction Model: The Significant Role of Data Engineering in the Quality of Outcomes." International Arab Journal of Information Technology 17, no. 4A (July 31, 2020): 635–44. http://dx.doi.org/10.34028/iajit/17/4a/8.
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For financial institutions and the banking industry, it is very crucial to have predictive models for their core financial activities, and especially those activities which play major roles in risk management. Predicting loan default is one of the critical issues that banks and financial institutions focus on, as huge revenue loss could be prevented by predicting customer’s ability not only to pay back, but also to be able to do that on time. Customer loan default prediction is a task of proactively identifying customers who are most probably to stop paying back their loans. This is usually done by dynamically analyzing customers’ relevant information and behaviors. This is significant so as the bank or the financial institution can estimate the borrowers’ risk. Many different machine learning classification models and algorithms have been used to predict customers’ ability to pay back loans. In this paper, three different classification methods (Naïve Bayes, Decision Tree, and Random Forest) are used for prediction, comprehensive different pre-processing techniques are being applied on the dataset in order to gain better data through fixing some of the main data issues like missing values and imbalanced data, and three different feature extractions algorithms are used to enhance the accuracy and the performance. Results of the competing models were varied after applying data preprocessing techniques and features selections. The results were compared using F1 accuracy measure. The best model achieved an improvement of about 40%, whilst the least performing model achieved an improvement of 3% only. This implies the significance and importance of data engineering (e.g., data preprocessing techniques and features selections) course of action in machine learning exercises
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Hadianto, Nur, Hafifah Bella Novitasari, and Ami Rahmawati. "KLASIFIKASI PEMINJAMAN NASABAH BANK MENGGUNAKAN METODE NEURAL NETWORK." Jurnal Pilar Nusa Mandiri 15, no. 2 (September 5, 2019): 163–70. http://dx.doi.org/10.33480/pilar.v15i2.658.
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Payment of loans that experience difficulties in repayment or often called bad credit is a very detrimental thing for the bank, with the occurrence of bad credit the bank does not have the maximum ability to make money for investment. Choosing the right customer must go through the right analysis because the decision to approve or disagree with the loan is the main point that determines the possibility of bad credit. This study aims to classify eligible customers to obtain loans by taking into account existing parameters such as age, total income, number of families, monthly expenditure average, education level and others. This study uses a data mining classification method with a neural network model, to assess the accuracy of data processing using rapid miners then proceed with measurements using confusion matrix, ROC curve. The results of the neural network algorithm after going through confusion matrix testing, the ROC curve shows a very high accuracy value, and the dominant value of AUC and algorithm. The accuracy value is 98.24% with AUC of 0.979
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Satria, Indra, and Iha Haryani Hatta. "PENGARUH KINERJA KEUANGAN TERHADAP HARGA SAHAM 10 BANK TERKEMUKA DI INDONESIA." Jurnal Akuntansi 19, no. 2 (March 6, 2017): 179. http://dx.doi.org/10.24912/ja.v19i2.93.
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Penelitian ini bertujuan untuk mengetahui dampak rasio keuangan terhadap harga saham sepuluh bank terkemuka di Indonesia. Penelitian ini menggunakan metode purposive sampling untuk sepuluh bank yang go public di Bursa Efek Indonesia periode 2013-2014 dengan kriteria berikut : (1) memiliki aset dengan jumlah terbesar pada tahun 2013-2014 (2) memiliki informasi rasio keuangan pada tahun 2013-2014 (3) tidak terjadi pemecahan saham pada tahun 2013-2014 (4) hasil pengolahan data statistiknya memenuhi uji asumsi klasik. Berdasarkan kriteria itu, maka jumlah bank yang terpilih adalah Bank Central Asia Tbk, Bank Negara Indonesia (Persero) Tbk, Bank Mandiri (Persero) Tbk, Bank Danamon Indonesia Tbk, Bank Rakyat Indonesia (Persero) Tbk, Bank Permata Tbk, Bank Pan Indonesia Tbk, Bank CIMB Niaga Tbk, Bank Tabungan Negara (Persero) Tbk dan Bank International Indonesia Tbk. Variabel tidak bebas dalam penelitian ini adalah harga saham, sementara variabel terikat adalah Loan to Deposit Ratio (LDR), Non Performing Loans (NPL), Capital Adequacy Ratio (CAR) and Return on Equity (ROE). Data dianalisis dengan menggunakan analisa regresi linier berganda. Hasil penelitian menunjukkan bahwa variabel bebas (LDR, NPL, CAR, and ROE) secara simultan berpengaruh signifikan terhadap harga saham. Secara parsial, LDR, CAR dan ROE berpengaruh signifikan terhadap harga saham. Sementara, NPL tidak berpengaruh terhadap harga saham.This research is to determine the impact of financial ratios on the stock price of ten leading banks in Indonesia. This research using a purposive sampling method for the ten banks that listed on the Indonesian Stock Exchange in the years 2013-2014 with the following criteria : (1) has assets with the largest number in the years 2013-2014 (2) has information about financial ratios in the years 2013-2014 (3) a stock split does not occur in the years 2013-2014 (4) the results of the processing of statistical data meets classical assumption. Based on the criteria, the then banks selected are Bank Central Asia Tbk, Bank Negara Indonesia (Persero) Tbk, Bank Mandiri (Persero) Tbk, Bank Danamon Indonesia Tbk, Bank Rakyat Indonesia (Persero) Tbk, Bank Permata Tbk, Bank Pan Indonesia Tbk, Bank CIMB Niaga Tbk, Bank Negara Indonesia (Persero) Tbk and Bank International Indonesia Tbk. The dependent variable in this research is the stock price, while the dependent variable are Loan to Deposit Ratio (LDR), Non Performing Loans (NPL), Capital Adequacy Ratio (CAR) and Return on Equity (ROE). Data were analyzed using multiple linear regression analysis. The results showed that the independent variables (LDR, NPL, CAR, and ROE) simultaneously significant effect on the stock price. Partially, LDR, CAR and ROE have a significant effect on the stock price. Meanwhile, NPL has no effect on the stock price.
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., Suryanto. "ANALISIS KREDIT USAHA RAKYAT PADA BANK RAKYAT INDONESIA." AdBispreneur 4, no. 2 (January 21, 2020): 113. http://dx.doi.org/10.24198/adbispreneur.v4i2.22488.
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This study aims to analyze the procedure for granting people's business credit (KUR) to Bank Rakyat Indonesia (BRI). The research method used in this study is a qualitative method with a type of descriptive research. The data obtained in this study uses research instruments, namely through observation, in-depth interviews, and documentation studies. Interviews were conducted with the Micro and Small Director of Bank BRI, debtor representatives and prospective debtors, as well as Bank Indonesia Representative Credit Section Bandung. Data collected from several sources carried out credibility testing with data triangulation — data analysis techniques used with data reduction, data presentation, and conclusions. The results of the study indicate that the implementation of the provision of people's business loans to Bank BRI through several stages, namely the stages of credit administration, credit documentation, credit approval, credit processing. The level of Non-Performing Loans (NPL) of BRI KUR shows a fairly large number of 2.31%, but among other KUR executing banks BRI's NPL is the smallest. The high level of NPL is because there are several factors, including: (1) wrong perceptions among Micro, Small and Medium Enterprises (MSME) actors regarding the source of funds in KUR distribution; (2) changes in the character of the debtor; and (3) the account officer section conducts a subjective analysis because there are certain relationships with prospective debtors Penelitian ini bertujuan untuk menganalisis prosedur pemberian kredit usaha rakyat (KUR) pada Bank Rakyat Indonesia (BRI). Metode penelitian yang digunakan dalam penelitian ini adalah metode kualitatif dengan jenis penelitian deskriptif. Data yang diperoleh pada penelitian ini menggunakan instrumen penelitian yaitu melalui observasi, wawancara mendalam dan studi dokumentasi. Wawancara dilakukan dengan Direktur Mikro dan Kecil Bank BRI, perwakilan debitur dan calon debitur, serta Bagian kredit Bank Indonesia Perwakilan Bandung. Data yang terkumpul dari beberapa sumber dilakukan pengujian kredibilitas dengan triangulasi data. Teknik analisis data yang digunakan dengan reduksi data, penyajian data, dan kesimpulan. Hasil penelitian menunjukkan bahwa pelaksanaan pemberian kredit usaha rakyat pada Bank BRI melalui beberapa tahapan yaitu tahap credit administration, credit documentation, credit approval, credit processing. Tingkat Non Performing Loan (NPL) KUR BRI memperlihatkan angka yang cukup besar yaitu 2,31%, tetapi diantara bank pelaksana KUR yang lainnya NPL Bank BRI yang paling kecil. Tingginya tingkat NPL dikarenakan adalah ada beberapa faktor, antara lain: (1) persepsi yang salah dikalangan pelaku Usaha Mikro Kecil dan Menengah (UMKM) mengenai sumber dana dalam penyaluran KUR; (2) adanya perubahan karakter debitur; dan (3) bagian account officer melakukan analisis secara subjektif karena ada hubungan tertentu dengan calon debitur.
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Chen, Herui. "Prediction and Analysis of Financial Default Loan Behavior Based on Machine Learning Model." Computational Intelligence and Neuroscience 2022 (September 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/7907210.
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In recent years, the increase of customer loan risk and the aggravation of the epidemic have led to the increase of customer default risk. Therefore, identifying high-risk customers has become an important research hotspot for banks. The customer’s credit is the standard to evaluate the loan amount and interest rate, and the ability to quickly identify customer information has become a research hotspot. Based on the bank credit application scenario, this paper realizes function extraction and data processing for customer basic attribute data and download transaction data. Then, a linear regression model with penalty and a neural network prediction model are proposed to improve the accuracy of bankruptcy assessment and achieve local optimization. In this way, the implicit risk prediction and control of customer credit are improved, and the default risk of bank loans is significantly reduced. According to the characteristics of the collected sample data, the most appropriate penalty linear regression prediction algorithm is selected and the experimental analysis is carried out to improve the risk management level of banks. The experimental results show that the improved logistic regression and neural network model has obvious advantages in the prediction effect for four models.
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Berisha, Fahredin. "Funding Challenges of Small and Medium Enterprises in Transition Countries: Kosovo Case Study." Journal of Economics and Management Sciences 3, no. 2 (June 20, 2020): p71. http://dx.doi.org/10.30560/jems.v3n2p71.
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SMEs play a very important role in the development of economies of different countries and they are now considered as a key factor of economic development. They affect unemployment, promote social welfare and can be treated as a promoter of economic growth. The paper addresses the role and importance of financing SMEs in transition countries including Kosovo. The study examines the key factors affecting the increase of SME financing from external sources, namely bank lending since other external sources of financing in Kosovo are scarce and almost non-existent. For the purposes of this paper, data from 215 SMEs surveyed in Kosovo were used, randomly distributed across manufacturing, services and commerce sectors. Data collection was done in the period January-April 2016, and their processing was carried out with SPSS (Social Package for Social Science). In order to have more consistent information during data processing, certain models were used in the paper: Paried-Samples T Test, which was used to investigate the difference between two sets of averages, which indicates that the business plan for the enterprise is relevant to bank loan access. The One Way Anova model was used to test the differences between two or more averages, and through this model is proved that high-profit enterprises have achieved easier access to bank loans. Also following the One Way Anova and Post Hoc LSD test, there were found differences between groups of enterprise by their types, activity and age. The research shows that enterprises with older ages have been able to obtain more easily bank loans. The One Way Anova and Welch-Brown-Forthyse test was used to deal with the level of education of business owners, whereby it was found that owners with a high level of education had easier access to bank loans. Through the Indepedent Samples T Test technique it was found that there is a significant difference between the age groups of the owners based on the mean and standard deviation.
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Hutahaean, Popy Sandra Tesalonica, Diharpi Herli Setyowati, and Endang Hatma Juniwati. "Pengaruh Dana Pihak Ketiga dan Non-Performing Loan terhadap Penyaluran Kredit pada Bank yang Terdaftar di BEI." Indonesian Journal of Economics and Management 1, no. 1 (November 30, 2020): 163–73. http://dx.doi.org/10.35313/ijem.v1i1.2426.
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The purpose in this research was to find the effect of third party funds and non-performing loans on the amount of lending to banking sector listed on the Indonesia Stock Exchange for the period 2014 to 2018. The research method used is quantitative descriptive method. The data used are secondary data in the form of annual financial reports, and the data analysis technique uses multiple linear regression analysis. The data processing in this study used a statistical tool, namely E-views 9. The results of this study indicate that partially third party funds have a positive and significant effect on the amount of lending and non-performing loans have a negative and significant effect on the amount of credit disbursement. Simultaneously or together, third party funds and non-performing loans have a significant effect on the amount of lending.
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Khatirina, Yuyun, Luk Luk Fuadah, and Azwardi Azwardi. "The analysis of the effects of Bank Soundness Rate, Inflation and Indonesian Bank Rate on the Profit Growth of Regional Development Banks." Accounting and Finance, no. 2(92) (2021): 95–106. http://dx.doi.org/10.33146/2307-9878-2021-2(92)-95-106.
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Regional Development Banks (BPD in Indonesian) are a type of bank in Indonesia that is established by the local provincial government. Its purpose is to boost regional development and provide initial capital to the province that private banks would not risk giving, as well as giving basic financial services for the general provincial population. RDBs support not only the economic growth in their respective regions but also Indonesia's macroeconomic growth. The purpose of this study is to provide empirical evidence on the impact of the bank soundness rate, inflation and Indonesian Bank rate (BI Rate) on the profit growth of Regional Development Banks. In this study, the authors use data for 2014-2019. The sample of the study is represented by 26 regional development banks in Indonesia, which are registered with the Bank Indonesia and the Financial Services Authority. The authors identified five regions of Indonesia that are being analyzed: Java (including Bali), Sumatra, Kalimantan, Sulawesi and Irian Jaya (including Nusa Tenggara). The authors use for analysis the secondary data obtained from quarterly and annual financial statements of banks. Hypothesis testing was performed using multiple regression analysis, data processing was performed in the SPSS Statistics program. It was found that the components of bank soundness (Capital Adequacy Ratio (CAR), Net Interest Margin (NIM), Non-Performing Loans (NPL), Loan to Deposit Ratio (LDR), Good Corporate Governance (GCG)), inflation and the BI Rate do not affect the profits growth of regional development banks. However, such a variable as the Operational Efficiency (known in Indonesia as BOPO) has little effect on the profits growth of regional development banks in Sumatra. For other regions, such an effect is not observed.
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Muplihah, Novia, Dadang Hermawan, and Destian Arshad Darulmalshah Tamara. "Return on Assets Bank Pembangunan Daerah di Pulau Jawa: Studi pada Efisiensi sebagai Determinan." Indonesian Journal of Economics and Management 2, no. 3 (July 24, 2022): 486–96. http://dx.doi.org/10.35313/ijem.v2i3.3775.
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This study aims to analyze the effect of Non Performing Loan (NPL) and Operating Income Operating Expenses (BOPO) on Return On Assets (ROA) at Regional Development Banks in Java for the period 2011-2020. This title was chosen because during 2020 BPD has not been efficient in disbursing funds because it does not apply the principles of prudence and bank efficiency so that non-performing loans increase. The research method used is associative research with a quantitative method approach. The sampling technique uses a saturated sample so that the total population and sample are the same, namely five Regional Development Banks on the island of Java for the 2011-2020 period. Data processing was carried out using the IBM SPPS Statistics 25 application. The results of the study stated that partially NPL had no effect on ROA, while BOPO had a negative effect on ROA. Meanwhile, simultaneously NPL and BOPO affect ROA.
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Biasmara, Hanif Artafani. "Pengaruh Tingkat Suku Bunga, Inflasi, dan Kredit Bermasalah terhadap Penyaluran Kredit UMKM di Indonesia." Jurnal Manajemen Bisnis dan Kewirausahaan 6, no. 1 (January 30, 2022): 95. http://dx.doi.org/10.24912/jmbk.v6i1.11438.
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Banks as intermediary institutions have a role in extending credit to the public. One of the loans provided by banks to the public is credit for Micro, Small, and Medium Enterprises (MSMEs). This research was conducted to analyze several factors consisting of Bank Indonesia (BI) interest rates or BI Rate, inflation, and bad credit or NPL that would affect MSME credit distribution. The research period is from 2011 to 2020. While the object sample consists of four banking groups, namely State-Owned Banks, Regional Development Banks, Foreign Exchange Commercial Banks, and Joint Venture Banks and Foreign Owned Banks. Where this research is a quantitative study using secondary data. Data processing using Panel Data Regression through Stata 16. The results obtained indicate that bad credit has a significant positive effect on MSME credit. Meanwhile, interest rates and inflation did not have a significant effect on MSME credit. Bank sebagai lembaga perantara memiliki peranan dalam menyalurkan kredit kepada masyarakat. Salah satu kredit yang diberikan oleh bank kepada masyarakat adalah kredit Usaha Mikro Kecil Menengah (UMKM). Penelitian ini dilakukan untuk menganalisis beberapa faktor yang terdiri atas tingkat suku bunga Bank Indonesia (BI) atau BI Rate, inflasi, dan kredit bermasalah atau NPL, akan pengaruhnya terhadap penyaluran kredit UMKM. Periode penelitian yaitu tahun 2011 hingga tahun 2020. Sedangkan sampel objek terdiri atas empat kelompok bank yaitu Bank Persero, Bank Pembangunan Daerah, Bank Swasta Nasional, dan Bank Asing atau Campuran. Dimana penelitian ini merupakan penelitian kuantitatif dengan menggunakan data sekunder. Data diolah dengan menggunakan metode Regresi Data Panel melalui perangkat lunak Stata 16. Hasil yang diperoleh yaitu menunjukkan bahwa kredit bermasalah memiliki pengaruh signifikan positif terhadap kredit UMKM. Sedangkan tingkat suku bunga dan inflasi berpengaruh namun tidak signifikan terhadap kredit UMKM.
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Mathur, Anjali, K. Vinitha, R. Shubham, and K. Gowtham. "Effect of SBI Bank Merger on Education Loan, a Comparative Study Using Big Data Analytics." International Journal of Engineering & Technology 7, no. 2.32 (May 31, 2018): 452. http://dx.doi.org/10.14419/ijet.v7i2.32.15739.
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A bank merger is a situation in which two banks or all branches of a bank join together to become one bank. The bank merger of State Bank of India was implemented on 1stApril 2017 in India. The bank merger is a good idea to centralize the customer’s data from nationwide. However, it is a difficult task for administrators and technologists. Some high level techniques are required to collect the data from the branches, of the bank present at nationwide, and merge them accordingly. For this huge data Big-Data Analysis techniques can be used to manage and access the data. The big data analytics provides algorithms to compare, classify and cluster the data at local and global level. This research paper proposes big data analytics for education loan provided by State Bank of India. The loan granting process becomes centralized after merger. It affects the processing of granting a loan, as earlier it was according to branches only. The proposed work is for comparative study of the impact of bank merger on education loan provided by State Bank of India.
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Safitri, Anggi Windu, and Rimi Gusliana Mais. "Analysis of Factors Affecting Murabahah Financing on Sharia Commercial Banks in Indonesia 2012–2018." Indonesian Journal of Business, Accounting and Management 2, no. 01 (June 10, 2019): 29–37. http://dx.doi.org/10.36406/ijbam.v2i2.583.
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Abstract—This study aims to determine the effect of Capital Adequacy Ratio (CAR), Third Party Funds (DPK), Financing to Deposit Ratio (FDR), Non Performing Financing (NPF), and Return On Ratio (ROA) to Financing Murabaha at Islamic Commercial Banks in Indonesia for the 2012-2018 Period. There are ten samples in this study that meet the research criteria, namely BCA Syariah Bank, BRI Syariah Bank, BNI Syariah Bank, Mandiri Syariah Bank, Syariah Bukopin Bank, Panin Indonesia Syariah Bank, Jabar Banten Syariah Bank, Mega Syariah Bank, Muamalat Bank, Victoria Bank Sharia. This research method is quantitative with data processing tools usingEviews 9 and the analysis tool used was panel data regression analysis. The selected model is the modelFixed Effect which was tested by F test and t test, with a significance of 5%. The results of the study show that the ratio of Third Party Funds has a positive and significant effect on financing Murabaha which means that no matter how big the deposited DPK will affect any amount of financing Murabaha. Capital Adequacy Ratio, Financing to Deposit Ratio, Net Performing Financing, and Return on Assets does not affect financing Murabaha, which means that no matter how big the CAR, FDR, NPF, and ROA will not affect the distribution of capital adequacy, distribution of total loans, nonperforming financing and investment profits to Financing Murabaha.
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Chen, Mingkeng, and Xiaoyun Ma. "An Optimized BP Neural Network Model and Its Application in the Credit Evaluation of Venture Loans." Computational Intelligence and Neuroscience 2022 (May 2, 2022): 1–9. http://dx.doi.org/10.1155/2022/8791968.
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With the rapid development of entrepreneurship loans in China, the construction of a credit evaluation system of risk loans has become an important financial safeguard measure. This paper mainly studies the following three aspects. Firstly, in view of the subjective factors in the approval process of venture loans, based on the credit evaluation system of commercial banks and the data characteristics of venture loans, a credit evaluation system based on venture loans is constructed. Secondly, the randomized uniform design method is used to improve the population initialization method to realize the uniform distribution of the individual population. Finally, aiming at the problem of low efficiency of venture loan audit, this paper proposes an optimized BP neural network to evaluate the venture loan. Especially, through data processing, a credit index system is constructed, and then the optimized BP neural network model is determined in parameters. The model contains 15 input nodes, 1 hidden layer, and 2 output layers. Finally, the simulation shows that the optimized BP neural network model has obvious advantages in the loan evaluation. This paper includes the development status of credit evaluation of venture loans is empirically studied by using an optimized BP neural network model of nonexpected output.
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Supriatini, Kadek Ayu, and Ni Luh Gede Erni Sulindawati. "Non Performing Loan, Loan to Deposit Ratio, Good Corporate Governance, Net Interest Margin, Return on Assets, Capital Adequacy Ratio dan Economic Value Added Terhadap Harga Saham." Ekuitas: Jurnal Pendidikan Ekonomi 9, no. 1 (June 29, 2021): 50. http://dx.doi.org/10.23887/ekuitas.v9i1.26756.
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Banking is one of the companies that have the role to support the economy. Conceptually the purpose of the research is to observe financial performance. To find out the health of banks, financial ratios are used through the Risk Based Bank Rating (RBBR) approach and through the performance value tool with the Ecconomic Value Added approach. Thus the results of this study are intended to determine the effect of Non Performing Loan, Loan to Deposit Ratio, Good Coorporate Governance, Net Interest Margin, Return On Asset, Capital Adequacy Ratio and Economic Value Added on the Bank's Stock Price. This type of research is to use quantitative because the use of data is in the form of numbers. Data acquisition is secondary in the financial statements. population use, namely overall listing on the Indonesia Stock Exchange specifically for the period 2014-2018. Sampling by purposive sampling through certain criteria. The number of samples produced was 23 banks in five years. Data processing using multiple linear regression techniques through SPSS version 20. The results showed that partially there were negative influences of Non-Performing Loans, Loan To Deposit Ratio, Good Corporate Governance variables on Stock Prices, while Net Interest Margin, Return On Assets, and Economic Value Added variables had positive effects on Stock Prices. While the Capital Adequacy Ratio has no effect on the Share Price.
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Nasution, Yuslinda. "ANALISIS LOAN TO DEPOSIT RATIO (LDR) DAN CAPITAL ADEQUANCY RATIO (CAR) TERHADAP RENTABILITAS PADA PT. BANK TABUNGAN NEGARA (PERSERO) Tbk." Jurnal Manajemen 1, no. 1 (August 1, 2016): 48–58. http://dx.doi.org/10.54964/manajemen.v1i1.173.
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This study aims to identify, analyze and be able to explain financial ratios, especially the level of liquidity that is calculated by using the ratio of loan to deposit ratio (LDR), calculating the solvency level by using the ratio of Capital Adequacy Ratio (CAR), and calculate profitability by using the ratio Retun On Assets (ROA), Return On Investment (ROI) and Net Interest Margin (NIM) In PT Bank Tabungan Negara (Persero) Tbk, which is one of the State Owned Enterprises in the field of banking services primarily in credit financing.To obtain the necessary data, the authors use data collection techniques, the type of data used in the form of primary data and secondary data.Based on the results of data processing by the author, it is known that there are three conclusions. The First. of the results of the liquidity ratio LDR decline in 2014 due to an increase of 1.66% higher than the savings credit, and an increase back in 2015 of 3.52% for loans greater than the savings. Second, the results of the solvency ratio increased in 2014 compared to 2013 of 2.66% is due to an increase of capital by the end of 2013. And a decline back in 2015 of 2.07% is due to the decline in capital activities over the previous year. Third, the results of Profitability ratios using ROA calculation decreased by 0.08% in 2014 and decreased by 0.09% return this happens because the increase in net income is followed by an increase in the number of significant assets annually. While using the ROE also decreased by 0.75% in 2014 and decreased by 0.12% return is due to the growth of the share capital. And calculations using the NIM in 2014 increased by 0.07% due to significant asset growth, but a decline back in 2015 by 0.29% due to an increase in interest rates as a result of the BI rate.Can be seen from the calculation of financial ratios and earnings at PT Bank Tabungan Negara (Persero) Tbk showed good conditions and positive, although LDR exceed the provisions of Bank Indonesia, is because BTN is a bank loan portfolio for the long term.
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Ekong, Rebecca Etim, Kolawole Gabriel Akintola, and Bamidele Moses Kuboye. "Development Of Credit Scoring Model For Borrowers Using Machine Learning Techniques." PERSPEKTIF 11, no. 3 (June 11, 2022): 829–38. http://dx.doi.org/10.31289/perspektif.v11i3.7180.
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Financial organizations such as banks have experienced an increase in demand for loans from borrowers over the years. These organizations are highly interested in knowing whether a borrower can pay back if granted the loan requested. Granting loans to defaulters can cripple the business, hence, these financial organizations are compelled to evaluate credit worthiness of clients using the vast volume of historical data related to financial position of borrowers. Like other prediction models, credit scoring is a technique used in predicting the probability that a loan applicant, existing borrower, or counterparty will default. Machine learning technique has ability to solve these challenges faced by credit analyst by automating the processing and extraction of knowledge from data. This research focuses on the development of a credit scoring model using Rough Set Theory (RST) and Multi-Layer Perceptron (MLP) Neural Network. RST was used for feature selection while ANN trained with backpropagation was used for classification. This research used two credit scoring datasets; Australian and German credit dataset. Data pre-processing and machine learning were performed using the Anaconda software. This research compares the result obtained from the RST and MLP with Decision Tree, Logistic Regression, Random Forest, Support Vector Machine, Nayes Bayes, K-Nearest Neighbour and ANN using standard evaluation metric to ascertain its performance on the two datasets and conclude the major findings. This research contributes a credit scoring model with improved performance while saving the computational costs.
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Dinah Purnamasari and Fatchan Achyani. "Analysis of the Effect of Credit Expansion, Operational Efficiency Rate, Lending Interest Rate, NPL of the Previous Period and Capital Adequacy Ratio (CAR) on Non-Performing Loans Based on the Generalized Method of Moment." Quantitative Economics and Management Studies 3, no. 2 (April 28, 2022): 256–64. http://dx.doi.org/10.35877/454ri.qems919.
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This study aims to determine the effect of credit expansion, operational efficiency, lending interest rate, NPL in the previous period and Capital Adequacy Ratio (CAR) on Non-Performing Loans (NPL). This type of research was quantitative and used secondary data from annual reports. This study used a sample of 20 conventional commercial banks registered on the IDX in 2017-2019 with sample determination using the purposive sampling method. The technique for analyzing data in this study used the Generalized Method of Moment (GMM) with data processing using the help of the Eviews 9 application. Based on the Generalized Method of Moment (GMM) analysis, it was obtained that credit expansion as measured by loan to deposit ratio and lending interest rate did not significantly affect non-performing loans. The level of operational efficiency as measured by Operating Expenses on Operating Income (BOPO), NPL of the previous period, and Capital Adequacy Ratio (CAR) significantly affected non-performing loans.
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Jindrich, Spicka, Naglova Zdenka, and Gurtler Martin. "Effects of the investment support in the Czech meat processing industry." Agricultural Economics (Zemědělská ekonomika) 63, No. 8 (August 4, 2017): 356–69. http://dx.doi.org/10.17221/367/2015-agricecon.
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The goal of the paper is to quantify and evaluate the effects of investment subsidies in the Czech meat processing industry. The investment subsidies should enhance the economic results of the supported companies and increase their competitiveness. The analysis is based on the fixed-effect modelling of balanced panel data of 130 meat processors in the period 2008–2013. It quantifies the impact of investment subsidies from the Rural Development Programme (RDP) and the national support programme (Decree of MoA) on profitability, labour productivity, credit debt ratio and the efficiency of production consumption. The conclusions can be generalized for medium-sized and large companies. The results show that investment subsidies from the RDP had not such a significant effect as expected. Investment subsidies from the RDP affected only the labour productivity of large meat processors and the ROA of non-family companies. However, they should preferably help small and medium-sized companies to be more competitive. Subsidies from the national programme increased the profitability of family-owned and medium-sized companies and changed the capital structure of the supported companies which used more bank loans for upgrading the technology.
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Suradi, Didi, Hermanto Siregar, and Bagus Sartono. "Non-Performing Loan Determinants during COVID-19 Pandemic (Case Study at Bank XYZ)." International Journal of Research and Review 8, no. 12 (December 16, 2021): 301–10. http://dx.doi.org/10.52403/ijrr.20211237.
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Non-Performing Loans (NPL) is a financial ratio that shows the credit risk faced as a result of granting credit and investment funds on different portfolios. This study aimed to analyze the determinants of Non-Performing Loans (NPL) before and during the Covid 19 pandemic at Bank XYZ. NPL can be caused by internal or external factors from Bank XYZ. The analytical method used is the Mixed method which combines quantitative and qualitative analysis. Data analysis used multiple linear regression method using time series data for the 1st quarter of 2013- 4th quarter of 2020. The analysis method used multiple linear regression to see the influence of internal factors are total credit, Return on Equity, Loan to Deposit Ratio, Net Interest Margin, total assets, BOPO, condition dummy before after transformation and external factors are Benchmark Interest Rate, exchange rate (exchange rate), Inflation, Industry Production Index, dummy conditions before and during the Covid 19 pandemic on Bank XYZ's NPL. The estimated regressions are the overall NPL, the Small Medium Enterprise (SME) Business Segment NPL, the Small Medium Enterprise (SME) Business Segment for the wholesales business sector, and the Small Medium Enterprise (SME) Business Segment NPL for the Retailer business sector. Data processing using E-views software version 9.0. The result of this research are factors that affect the overall NPL: Dummy Transformation, Net Interest Margin, and total assets, for the NPL for the Small and Medium Enterprises (SME) segment: total assets, dummy transformation and Net Interest Margin (NIM), NPL for the Small and Medium Enterprises (SME) business sector Wholesales: BOPO NIM, Lending growth, total assets, ROE and Dummy Transformation, for the NPL segment of Small and Medium Enterprises (SMEs) Retailer business sector: Net Interest Margin (NIM) and total assets. The impact of the COVID-19 pandemic on NPLs was most felt by the NPL all, SME business segment credit and the wholesales business sector. Keywords: covid-19 pandemic, NPL, NPL SME, retailer, wholesales.
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Alamsyah, Setiawan, and Nuryasman MN. "Analisis Kinerja Keuangan Bank Konvensional dan Bank Syariah yang Terdaftar di BEI." Jurnal Manajerial Dan Kewirausahaan 4, no. 3 (August 16, 2022): 806–15. http://dx.doi.org/10.24912/jmk.v4i3.19775.
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Tujuan penelitian ini adalah untuk menganalisis kinerja keuangan bank konvensional dan bank syariah yang terdaftar di bursa efek indonesia (BEI) dengan menggunakan Return on Assets (ROA) sebagai indikatornya yang dipengaruhi oleh Capital Adequacy Ratio (CAR), Loan to Deposit Ratio (LDR), dan Growth Domestic Product (GDP). Sampel penelitian ini adalah sebanyak 40 sampel yang dipilih melalui metode purposive sampling. Data perusahaan untuk pemilihan sampel diambil dari website resmi Bursa Efek Indonesia, yaitu idx.co.id, investing.com dan idnfinancials.com. Pengolahan data dilakukan dengan menggunakan software Eviews9 dan SPSS25. Hasil penelitian ini menunjukkan bahwa Capital Adequacy Ratio (CAR) memiliki pengaruh terhadap Return on Assets (ROA) bank konvensional tetapi tidak memiliki pengaruh terhadap Return on Assets (ROA) bank syariah, Loan to Deposit Ratio (LDR) tidak memiliki pengaruh terhadap Return on Assets (ROA) bank konvensional dan bank syariah, Growth Domestic Product (GDP) tidak memiliki pengaruh terhadap Return on Assets (ROA) bank konvensional dan bank syariah, dan terdapat perbedaan kinerja keuangan antara bank konvensional dan bank syariah berdasarkan Return on Assets (ROA)
The purpose of this study is to analyze the financial performance of conventional banks and Islamic banks listed on the Indonesia Stock Exchange (IDX) using Return on Assets (ROA) as an indicator which is influenced by the Capital Adequacy Ratio (CAR), Loan to Deposit Ratio (LDR), and Growth Domestic Product (GDP). The sample of this research was 40 samples which were selected through purposive sampling method. Company data for sample selection was taken from the official website of the Indonesia Stock Exchange, namely idx.co.id, investing.com and idnfinancials.com. Data processing is done using Eviews9 and SPSS25 software. The results of this study indicate that the Capital Adequacy Ratio (CAR) has an influence on the Return on Assets (ROA) of conventional banks but has no effect on the Return on Assets (ROA) of Islamic banks, Loan to Deposit Ratio (LDR) has no effect on the Return on Assets. (ROA) of conventional banks and Islamic banks, Growth Domestic Product (GDP) has no effect on the Return on Assets (ROA) of conventional banks and Islamic banks, and there are differences in financial performance between conventional banks and Islamic banks based on Return on Assets (ROA).
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Suswadi, Diego Hermacho, Agung Prasetyo, and Kusriani Prasetyowati. "Agrotourism Development Strategy in Wonogiri." JURNAL ILMIAH AGRINECA 22, no. 2 (July 28, 2022): 40–48. http://dx.doi.org/10.36728/afp.v22i1.2031.
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This study aims to analyze and identify internal and external environmental factors, describe marketing strategies, and describe the priority strategies used by Soko Langit Agrotourism. The data processing and analysis method uses the IFE (Internal Factor Evaluation), EFE (External Factor Evaluation) matrix, the IE (Internal-External) matrix, the SWOT matrix (Strengths, Weaknesses, Opportunities, Threats), and QSPM (Quantitative Strategic Planning Matrix). The study results were obtained from five internal and five external factors, which resulted in eight alternative strategies in the marketing of Soko Langit Agrotourism. This study resulted in three strategic priorities related to visitor satisfaction, including a) improving the quality of facility maintenance; b) adding and maintaining rides in order to compete with competitors; and c) taking advantage of capital opportunities from the village government to reduce the number of loans from banks. Recommendations for strategies that can be implemented include: allocating capital opportunities from the village government for facility maintenance costs; adding rides with manageable maintenance levels; improving facilities that accommodate extreme weather changes; increasing the efficiency of financing from bank loans; and increasing the efficiency of facilities to accommodate extreme weather.
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Kou, Gang, Yi Peng, and Chen Lu. "MCDM APPROACH TO EVALUATING BANK LOAN DEFAULT MODELS." Technological and Economic Development of Economy 20, no. 2 (June 27, 2014): 292–311. http://dx.doi.org/10.3846/20294913.2014.913275.
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Banks and financial institutions rely on loan default prediction models in credit risk management. An important yet challenging task in developing and applying default classification models is model evaluation and selection. This study proposes an evaluation approach for bank loan default classification models based on multiple criteria decision making (MCDM) methods. A large real-life Chinese bank loan dataset is used to validate the proposed approach. Specifically, a set of performance metrics is utilized to measure a selection of statistical and machine-learning default models. The technique for order preference by similarity to ideal solution (TOPSIS), a MCDM method, takes the performances of default classification models on multiple performance metrics as inputs to generate a ranking of default risk models. In addition, feature selection and sampling techniques are applied to the data pre-processing step to handle high dimensionality and class unbalancedness of bank loan default data. The results show that K-Nearest Neighbor algorithm has a good potential in bank loan default prediction.
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Sitanaya, Meliske. "ANALISIS TINGKAT PROFITABILITAS BANK DENGAN METODE RISK BASED BANK RATING." Jurnal Ekonomi dan Bisnis 22, no. 2 (June 1, 2018): 69–82. http://dx.doi.org/10.24123/jeb.v22i2.1648.
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This research aims to analyze whether the, Non-Performing Loan (NPL), Loan to Deposit Ratio (LDR), Net Interest Margin (NIM),) and Capital Adequacy Ratio (CAR) have significantinfluencesimultaneously and partially toward Return On Asset (ROA). This research classifiedthe verifiation research. The population is conventional commercial bank period 2006-2015. Sample was determined by the higher bank asset, a total of ten companies. The secondary data were taken such as from financialreport of Banks started from 2006 until 2015. The technique of data analysis in this research using panel regression analysis. ROA as a dependent variable, NPL, LDR, NIM and CAR as independent variables. Data processing using E-views 6. The result provides evidence that NPL and CAR have significantinfluencesimultaneously toward ROA, while NIM and LDR are not significantinfluencesimultaneously toward ROA. NPL partially have negative significantinfluencetoward ROA, LDR and NIM partially positive are not significantinfluencetoward ROA, and CAR partially have positive significant influence towd ROA.
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Soerip, S. Riauwanto. "Pengaruh Kinerja Dan Kepedulian Manajemen Bank Pembangunan Daerah Di Indonesia Terhadap Porsi Penyaluran Kredit Pengembangan Sektor UMKM." Kajian Bisnis STIE Widya Wiwaha 23, no. 1 (March 21, 2017): 14–39. http://dx.doi.org/10.32477/jkb.v23i1.201.
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This study investigates the effect of performance management and care of BPD in Indonesia against the share of SME sector development lending.The loan portfolio BPD in Indonesia is dominated employee loans (consumer) compared with the productive sectors especially the MSME sector.The inequality shows the composition distribution disitermediari very sharp.Though SME sector contribute greatly to the economy of Indonesia.But the “concern” BPD in Indonesia management is less interested in developing SME loans larger portion of consumer credit (employees).This study uses secondary data sources from Financial Statements 26 BPD in Indonesia period 2005-2009 for which data are derived from the website of Bank Indonesia.Cross-section data and time series of data obtained are arranged into panels totaling 130 sets of data.The data of this research is the dependent variable PK (MSME sector credit portion), while the independent variables consist of CAR, NPL, EQI, ROA, LIQRR, LDR, IRISK, SIZE and SHM.Data management factors described by the factor of “concern” managers and owners (EQI and SHM).The model was tested using Hausman equation to obtain the most appropriate model.The results using fixed effects approach to data processing.Methods of data using multiple regression analysis (multiple regression model), while the data processing using statistical program eviews 6. The study found that the factors “concern” the development of the SME sector is still weak, because the portions are relatively much smaller when compared to consumer credit.In addition to a weak level of concern, caring influence too weak manager and owner of the credit portion of the development of SMEs.This is due to liquid assets tend to be directed and invested in the reserve requerment and not optimized for using the development of SMEs.Another finding is that the overall performance of BPD has no effect on the development of SME lending portion, except NPL factors and SIZE bank.NPL showed a negative effect, while SIZE positive effect on bank lending portion of the SME sector development. Thus, this study found and concluded that the performance of BPD in Indonesia in managing assets - liability to the development of the SME sector is not optimal and efficient.The portion of the SME sector has not made backbound loan portfolio, although a major contribution to the Indonesian economy.Inequality composition of the loan portfolio between consumptive portion and the SME sector credit shows that there has been disintermediari on bank performance.Therefore, profits or revenues (achievement) is now obtained the credit market has yet to show real potential, because lounable support fund owned by the BPD in Indonesia is still very large.
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Ulaga Priya, K., S. Pushpa, K. Kalaivani, and A. Sartiha. "Exploratory analysis on prediction of loan privilege for customers using random forest." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 339. http://dx.doi.org/10.14419/ijet.v7i2.21.12399.
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In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.
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Rianto, Muhammad, Rudi Rusdiah, and Hidayatul Ichwan. "Penerapan Data Mining Dengan Metode Naïve Bayes Dan Learning Vector Quantization Credit Rating Dalam Memprediksi Kelayakan Pemberian Kredit Oleh PT. BPR Lebak Sejahtera." Respati 17, no. 1 (February 10, 2022): 69. http://dx.doi.org/10.35842/jtir.v17i1.443.
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INTISARIPihak bank dalam memberikan pinjaman perlu melaksanakan credit analisis evaluasi approval terlebih dahulu supaya resiko yang timbul dari pemberian kredit kepada calon debitur tidak terlalu besar. Data mining merupakan teknik yang memanfaatkan data dengan jumlah yang besar untuk mendapatkan informasi atau data yang berharga untuk mengambil keputusan yang penting. Data mining juga telah terbukti digunakan dalam perbankan yang mengklasifikasikan data yang berguna dan berukuran besar dalam melakukan big data dan analysis. Dalam penelitian ini studikasus yang dilakukan pada data debitur Bank PT. BPR Lebak Sejahtera Kabupaten Lebak dengan menggunakan model Naive Bayes (NBC) & Learning Vector Quantization. Dengan menggunakan teknologi di bidang data mining yang mengoptimasi proses pencarian informasi prediksi dalam basis data yang besar, serta menemukan pola-pola yang tidak diketahui sebelumnya. Naïve Bayes memprediksi probabilitas di masa depan berdasarkan pengalaman di masa sebelumnya dengan mempelajari korelasi hipotesis yang merupakan label kelas yang menjadi target pemetaan dalam klasifikasi dan evidence yang merupakan fitur-fitur yang menjadi masukan dalam model klasifikasi. Pengolahan data berbasis data mining tersebut diharapkan dapat digunakan sebagai alat bantu dalam memprediksikan kelayakan kredit yang memperkirakan layak atau tidaknya pemohon atau nasabah untuk diberikan kredit..Kata kunci— Data Mining, Naïve Bayes, Learning Vector Quantization , Bank, Kredit. ABSTRACTThe bank in providing loans needs to predict the feasibility of applying for credit in advance so that the risks arising from lending to prospective debtors are not too great. Data mining is a technique that utilizes a large amount of data to obtain valuable information or data to make important decisions. Data mining has also been shown to be used in banks that classify useful and large-sized data. In this study, the case study was conducted on the data of the bank debtors. PT. BPR Lebak Sejahtera District using Naive Bayes (NBC) Learning Vector Quantization model. By using technology in the field of data mining that optimizes the process of searching for predictive information in large databases, as well as finding previously unknown patterns. Naive Bayes predicts future probabilities based on previous experience by studying the correlation of hypotheses that are class labels that are the target of mapping in classification and evidence which are features that are input in the classification model. Data mining-based data processing is expected to be used as a tool in predicting creditworthiness that estimates whether or not the applicant or customer is eligible for credit. Keywords— Data Mining, Naïve Bayes, Learning Vector Quantization, Bank, Credit.
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Firdaus, Jemmy, Fakhry Zamzam, and Harsi Romli. "PENGARUH DETERMINASI PENYALURAN KREDIT TERHADAP PROFITABILITAS BANK UMUM TERDAFTAR DI BURSA EFEK INDONESIA (BEI)." Ekonomica Sharia: Jurnal Pemikiran dan Pengembangan Perbankan Syariah 6, no. 2 (February 11, 2021): 137–54. http://dx.doi.org/10.36908/esha.v6i2.205.
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This research aims to determine the influence of credit distribution determinations of Spread of Interest Rate (SIR), Loan to Deposit Ratio (LDR) and Non Perfoming Loan (NPL) against the profitability of commercial banks. The focus of research is on the banking industry listed on the Indonesia Stock Exchange (IDX). The population used in this research is a public bank listed on the Indonesia Stock Exchange in 2010-2018. This study uses multiple regression analysis methods with data processing techniques using 8 eviews. The test results proved that: 1) The increase in SIR has no effect on the profitability of the national commercial Bank located in IDX period 2010-2018. ; 2) The increase of LDR negatively affects the profitability of the national commercial Bank located in IDX period 2010-2018; 3) The NPL increase affects the increase in profitability of the national commercial Bank located in the IDX period 2010-2018.
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Nguyen Thi, Canh, and Khoa Pham Chi. "An Application of KMV Model to Forecast the Credit Risk of Corporate Customers andBank’s Expected Losses." Journal of Asian Business and Economic Studies 22, no. 1 (January 1, 2015): 62–81. http://dx.doi.org/10.24311/jabes/2015.22.1.07.
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The research aims to apply KMV-Merton model to calculate and forecast default probability (DP) among corporate customers of Vietcombank. Analyzing data from financial statements of 6,398 corporate customers in the years 2008–2012/2013, the research shows that the DP of the whole customer portfolio is 2.6%, equaling a loss of VND6,319 billion, or 3.8% of outstanding loans to the portfolio. The results also show that small-sized companies have smaller DP as compared to larger ones. Regarding industries, the lowest DP is found in road and waterway transport business, and the highest is in electricity (including production, transmission and distribution), production of other kinds of power, and seafood processing business. Industries with high DP and outstanding loans may cause the greatest damage to banks. The research concludes that large-sized companies and seafood processing enterprises cause the greatest losses to banks.
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Tipa, Handra, and Mortigor Afrizal Purba. "ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENGAMBILAN KREDIT OLEH PELAKU USAHA PROPERTY DI KOTA BATAM." JURNAL AKUNTANSI BARELANG 3, no. 1 (December 6, 2019): 49. http://dx.doi.org/10.33884/jab.v3i1.1612.
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Factors that are one aspect of the failure of property businesses in accessing Working Capital Loans and Investment Loans are misunderstandings of business actors who refer to what factors are important assessments of banks in lending. This study aims to analyze the factors that influence credit taking by property businesses in Batam City. in particular, because land ownership status in Batam is different from other regions in Indonesia. Based on data processing In partial interest rates have a significant effect on the decision to take credit by property businesses in Batam City with a significant value = 0,000 <0.05, Ha is accepted and Ho is rejected. From these calculations the interest rate has a significant effect on Credit Decisions, influential collateral significant to the decision to take credit by property business actors in Batam City with a significance = 0.017 <0.05 then Ha is accepted and Ho is rejected. From these calculations, the guarantee has a significant effect on Credit Decisions. Credit nominal has a significant effect on the decision to take credit by property businesses in Batam City with a significant value = 0,000 <0,05 so Ha is accepted and Ho is rejected. Likewise, the variable bank service has a significant effect on the decision to take credit by property businesses in Batam City. Simultaneously the influence of interest rates, guarantees, nominal credit, and bank services have a significant effect on the decision in credit disbursement for property entrepreneurs in the city of Batam. From the results of this study entrepreneurs must be wiser in making credit decisions and be better able to see better opportunities in the future so that the business undertaken can grow much more rapidly.
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-, Irawati Junaeni. "How Big The Role of Credit Risk, Liquidity Risk and Capital Have an Effect On The Profitability of The 10 Largestt Bank in Indonesia." International Journal of Science, Technology & Management 2, no. 1 (January 27, 2021): 179–89. http://dx.doi.org/10.46729/ijstm.v2i1.146.
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The purpose of this research is to analyze how the effect of credit risk, liquidity risk, bank capital, on profitability. The ratio used to measure credit risk using the Non Performing Loan (NPL), liquidity risk using the Loan to Funding Ratio ( LFR) and bank capital using the Capital Adequacy Ratio (CAR). The sample in this study were the 10 largest banks in Indonesia based on total assets. The analysis technique used in this research is panel data regression with fixed effects. The data processing tool used in this study is the Eviews 10 program. The partial test results show that the variables of credit risk and bank capital have an effect on profitabilityas measured by Return on Assets (ROA). Credit risk shows a negative and significant effect on profitability. And bank capital has a positive and significant effect on profitability. Meanwhile, liquidity risk has no significant effect on profitability. Simultaneously, the variables of credit risk, liquidity risk and capital have an effect of 90.17% on profitability. The remaining 9.83% was influenced by other factors not examined in this study
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Juniardana, I. Gede Arya, and Desak Putu Dewi Kasih. "URGENSI REGULASI FINANCIAL TECHNOLOGY (FINTECH) PINJAMAN ONLINE MELALUI PEMBAYARAN PERBANKAN." Kertha Semaya : Journal Ilmu Hukum 10, no. 10 (August 6, 2022): 2305. http://dx.doi.org/10.24843/ks.2022.v10.i10.p09.
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Penelitian ini bertujuan untuk mengetahui serta menganalisa bagaimana pengakuan dari financial technology (FinTech) di Indonesia serta tujuan lainnya untuk mengenalisis bentuk transaksi serta pinjaman online yang dimana penyaluran dan penerimaan pembayaran pinjamannya melalui perbankan. Metode penelitian yang digunakan yakni penelitian yuridis normatif dengan pendekatan konseptual (conseptual approach) serta perundang–undangan (statue approach). Metode yang digunakan terkait dengan metode yuridis normative yakni suatu metode dengan mengacu pada mengkaji lebih dalam terkait dengan peraturan perundang-undangan yang berlaku dan juga mengacu terhadap suatu studi kepustakaan dengan memanfaatkan data sekunder baik yakni sebagai bahan hukum primer maupun bahan hukum sekunder. Sifat penelitian ini berisfat deskriptif dengan menjelaskan suatu permasalahan dari isu hukum terjadi secara mendetail dengan menarik kesimpulan sehingga dapat menemukan jawaban dari suatu permasalahan yang terjadi. Hasil dari penelitian ini merupakan perjanjian pinjaman online berbasis Financial Technology (FinTech) di Indonesia mempunyai regulasi yakni Peraturan Otoritas Jasa Keuangan Nomor 13/POJK.02/2018 mengenai Inovasi Keuangan Digital pada Sektor Jasa Keuangan menjadi pengaturan industri FinTech (Financial Ttechnology) & ketentuan yang memayungi pengawasan. Selain itu Bank Indonesia juga mengatur mengenai regulasi tersebut yang terdapat pada Peraturan Bank Indonesia Nomor. 19/12/PBI/2017 mengenai Penyelenggaraan Teknologi Finansial. Sistem pembayarn di Indonesia dalam penyelenggaraan FinTech tertuang di Peraturan Bank Indonesia Nomor. 18/40/PBI/2016 mengenai Penyelenggaraan Pemrosesan Transaksi Pembayaran, Surat Edaran Bank Indonesia Nomor. 18/22/DKSP mengenai Penyelenggaraan Layanan Keuangan Digital, Peraturan Bank Indonesia No. 18/17/PBI/2016 mengenai Uang Elektronik. Hadirnya fintech secara tidak langsung memberikan solusi pembentukan inovasi keuangan serta transaksi non tunai. Tujuan fintech yakni memudahkan konsumen mendapatkan layanan keuangan yang prima serta mempermudah transaksi finansial.
This purpose of study to determine and analyze how the recognition of financial technology (FinTech) in Indonesia and other purposes to identify forms of transactions and online loans in which the distribution and receipt of loan payments through banks. The research method used is normative juridical research with a conceptual approach (conceptual approach) and legislation (statue approach). The method used is related to the normative juridical method, which is a method with reference to a deeper study of the applicable laws and regulations and also refers to a literature study by utilizing secondary data, namely as primary legal materials and secondary legal materials. The nature of this research is descriptive by explaining a problem from a legal issue that occurs in detail by drawing conclusions so that it can find answers to a problem that occurs. The results of this study are online loan agreements based on Financial Technology (FinTech) in Indonesia which have regulations, namely the Financial Services Authority Regulation Number 13/POJK.02/2018 concerning Digital Financial Innovation in the Financial Services Sector to regulate the FinTech industry (Financial Ttechnology) & the provisions that apply. cover supervision. In addition, Bank Indonesia also regulates the regulation contained in Bank Indonesia Regulation Number. 19/12/PBI/2017 regarding the Implementation of Financial Technology. The payment system in Indonesia in implementing FinTech is stated in Bank Indonesia Regulation Number. 18/40/PBI/2016 concerning the Implementation of Payment Transaction Processing, Bank Indonesia Circular Letter Number. 18/22/DKSP regarding the Implementation of Digital Financial Services, Bank Indonesia Regulation No. 18/17/PBI/2016 regarding Electronic Money. The presence of fintech indirectly provides solutions for the formation of financial innovations and non-cash transactions. The purpose of fintech is to make it easier for consumers to get excellent financial services and facilitate financial transactions.
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Jaya, Gladis Kusuma. "ANALISIS PENGARUH ROA, ROE, NPL, DAN LDR TERHADAP CAR DI PERBANKAN INDONESIA PERIODE 2004-2015." Jurnal Ekonomi dan Bisnis 21, no. 1 (November 1, 2016): 21–29. http://dx.doi.org/10.24123/jeb.v21i1.1633.
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This research aims to analyze whether the Return On Asset (ROA), Return On Equity (ROE), Non Performing Loan (NPL) and Loan to Deposit Ratio (LDR) have significantinfluencesimultaneously and partially toward Capital Adequacy Ratio (CAR). This research classifiedthe verificativeresearch. The population is the national private commercial bank period 2004-2015. Sample was determined by the higher bank asset, a total of fivecompanies. The secondary data were taken such as from financialreport of Banks started from 2004 until 2013. The technique of data analysis in this research using panel regresion analysis. CAR as a dependent variable, ROA, ROE, NPL and LDR as independent variables. Data processing using Eviews 6. The result provides evidance that ROA, ROE, NPL, and LDR have significantinfluencesimultaneously toward CAR. ROA and NPL partially have positive significantinfluencetoward CAR. ROE and LDR partially have negative significantinfluencetoward CAR.
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Sunaryo, Deni. "THE EFFECT OF WORKING CAPITAL, RETURN ON ASSETS AND COMPANY SIZES ON THE CREDIT AMOUNT OF SMALL AND MEDIUM MICRO BUSINESSES IN NATIONAL BANKS IN INDONESIA PRE COVID-19." Dinasti International Journal of Economics, Finance & Accounting 1, no. 3 (August 14, 2020): 501–14. http://dx.doi.org/10.38035/dijefa.v1i3.450.
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The research of "Effect of Working Capital, Return on Assets and Company Size on the Amount of Micro and Small Medium Enterprises Loans at National Banks in Indonesia in Pra COVID-19" was conducted using Multiple Linear Regression analysis tools using the help of SPSS 25 data processing applications. This research is the influence of Working Capital variable on the distribution of MSME loans with t arithmetic> t table (4.992> 2.048) with a significance value of 0.000 <0.05. The Return on Assets (ROA) variable does not affect the distribution of MSME loans to national banks in Indonesia in 2014-2018 with t count <t table (0.025 <2.048) with a significance value of 0.980> 0.05. The company size variable has a significant effect with the value of t count> t table (3.026> 2.048) with a significance value of 0.006 <0.05. Based on a simultaneous study of working capital, Return on Assets (ROA), and company size influence the distribution of MSME loans to national banks in Indonesia in 2014-2018 with a F table of 2.98 and a significance level of 0.05. Then F count> F table (12.041> 2.98) and sig. <0.05 (0,000 <0.05).
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Prasanth, S., and S. Sudhamathi. "Analysis of Indian Bank Customer’s Attitude towards E-Banking." Shanlax International Journal of Management 8, no. 4 (April 1, 2021): 82–89. http://dx.doi.org/10.34293/management.v8i4.3661.
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The banks acquired the capacity to efficiently cut costs through the introduction of E-banking technologies, to extend its reach to manage its everyday banking needs by way of the use of online banking platforms. This paper analyses Indian bank customer’s attitudes towards e-banking. A big part of why people tend to do banking at home or the ATM is that they feel like they should go at a time that is more convenient for them, rather than coming to the branch. This paper found how the impression of banking facilities that students get is affected by choice of banks they use. The researcher has used a proportionate stratified random sampling method, which is more effective for correctly selecting respondents. The data were obtained 120 individuals applying for a bank loan in India through online banking. In this category of 120, only those who replied arecited by the report. Then the data processing was performed using the Multivariate Analysis Test, the NSQ, and the Kolmogorov-Smirnov analyses. Through this research, it was determined that youth, ethnicity, and income play a major role in online banking.
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Kurniasari, Agri, and Dyah Fitriani. "KEMAMPUAN RASIO KEUANGAN DALAM MEMPREDIKSI PERUBAHAN LABA PADA BANK-BANK BADAN USAHA MILIK NEGARA YANG TERDAFTAR DI BURSA EFEK INDONESIA." Jurnal Fokus Manajemen Bisnis 3, no. 1 (March 31, 2013): 18. http://dx.doi.org/10.12928/fokus.v3i1.1327.
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This Research purposed to analyze any influence of Loan to Deposit Ratio (LDR), Capital Adequacy Ratio (CAR), Return on Equity (ROE), and BOPO Ratio toward Earning Changes at State Owned Bank registered in Indonesia Stock Exchange (IDX) during 2008-2012. The population in this research are all of State Owned Bank registered on Indonesian Stock Exchange (IDX) during 2008-2012, and the samples in this research are all of populations. They are Bank Mandiri (Persero) Tbk, Bank Negara Indonesia (Persero) Tbk, Bank Rakyat Indonesia (persero) Tbk, and Bank Tabungan Negara (Persero) Tbk. The data can be found from quartely financial statements publicated on Indonesia banking directory and processed using panel data processing and partial (t) test. The result of this research showed that Loan to Deposit Ratio (LDR) partialy not significance influental toward Earning Changes. Significancy value 0.5003 more than alpha 5%. Capital Adequacy Ratio (CAR) partialy not significance influental toward Earning Changes. Significancy value 0.8248 more than alpha 5%. Return on Equity (ROE) partialy not significance influental toward Earning Changes. Significancy value 0.0784 more than alpha 5%. BOPO Ratio partialy not significance influental toward Earning Changes. Significancy value 0.6577 more than alpha 5%
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Ekawati, Nurtika, Unggul Purwohedi, and Ari Warokka. "The Influence of Risk Management, Third-Party Funds and Capital Structure on Banking Sector Financial Performance in Indonesia and Thailand with Corporate Governance as Moderating Variable in 2015-2019." Oblik i finansi, no. 4(94) (2021): 71–80. http://dx.doi.org/10.33146/2307-9878-2021-4(94)-71-80.
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The banking sector plays an important role in the country's economic growth. International experience shows that a weak banking sector not only threatens the long-term stability of a country's economy. It can also cause a financial crisis which can lead to economic crisis. Therefore, it is important to identify and investigate the factors on which the financial performance of banks depends. The purpose of this study is to analyze the influence of risk management, third-party funds and capital structure on banking sector financial performance in Indonesia and Thailand with corporate governance as moderating variable. The authors use return on assets (ROA) as the key indicator of bank efficiency. The data used in this study are secondary data, including nonperforming loan (NPL), loan-to-deposit ratio (LDR), operating expenses to operating income (BOPO), net interest margin (NIM), third party funds (TPF), debt-to-equity ratio (DER), return on assets (ROA), corporate governance. The data was obtained from the official website of the Indonesia Stock Exchange (www.idx.co.id) and the Thai Stock Exchange (www.set.or.th). The sample used in this study is 20 conventional banks listed on the Indonesia and Thailand Stock Exchange from 2015-2019. The methodological basis of this study is the use of the Structural Equation Model (SEM) with Partial Least Square (PLS). Data processing was performed in the WarpPLS 7.0 software. The study results show that NPL and LDR have a negative and significant influence on the financial performance of banks. At the same time, the BOPO and DER do not affect the financial performance of banks. The NIM and TPF have a significant and positive influence on the bank's financial performance. In addition, corporate governance does not moderate risk management relationship to the bank's financial performance. The results of this study can benefit bank shareholders and customers, and bank management.
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Zuhroh, Idah, and Frinda Pramesti Regitara Cahyani. "Exploring the role of financial ratio and interest rate on banking credit channelling: Data from Indonesia." Jurnal Inovasi Ekonomi 6, no. 03 (October 29, 2021): 117–22. http://dx.doi.org/10.22219/jiko.v6i03.17510.
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This study explores the effect of financial ratio and benchmark interest rate on the determinants of credit channelling in Indonesia. The data in this study uses quarterly data during the 2010-2019 period; the analysis technique in data processing uses panel data regression. The results of this study indicate that the model is simultaneous significant. Third-party funds and loan to funding ratio partially have a significant positive effect. Operating expenses and operating income have a significant negative impact. The benchmark interest rate has not substantially influenced the determinants of bank lending in Indonesia.
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Gregorius Ken and Linda Santioso. "Determinan Profitabilitas Perbankan yang Terdaftar di BEI Periode 2018-2020." Jurnal Ekonomi 27, no. 03 (March 4, 2022): 358–278. http://dx.doi.org/10.24912/je.v27i03.881.
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This study was conducted with the aim of measuring the effect of capital adequacy ratio, net interest margin, non-performing loan, loan to deposit ratio, operational efficiency ratio on the profitability of banking companies listed on the IDX during the 2018-2020 period using firm size as a control variable. Sample selection is done by using purposive sampling technique. Data processing of 32 banking companies use samples carried out using SPSS Statistics 17 software. The results show that net interest margin has a significant effect on bank profitability, operational efficiency ratio has a significant negative effect on bank profitability, but the ratio of capital adequacy, non-performing loan, loan to deposit ratio does not have a significant effect on the profitability of banking companies.
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Khanchel, Hanen. "Banking Risk Analysis in Tunisia: A Case Study of BTE Bank." Business and Management Research 8, no. 4 (January 16, 2020): 8. http://dx.doi.org/10.5430/bmr.v8n4p8.
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The banking activity represents a strategic sector of sustainable economic development in Tunisia. Hence, Tunisian banks have the status of financial institutions that earn profits by providing financial services to customers by dealing with risks. Therefore, lending decisions for these establishments are strategic as they can avoid the risk of loan recourse. However, the assessment of borrowing sanctions in Tunisian banks is based on credit rating models. Consequently, it is important to assess the riskiness of the banking sector in Tunisia. Indeed, Tunisian banks have kept voluminous data concerning their clienteles which can be considered as critical knowledge assets which can be processed via underwritten credit management tools. This tools denote a recent development of statistical techniques and promising tools of data mining and data processing. The current study attempts to develop the rating model as a decision support system to credit approval evaluation at Tunisian banks based on applicant’s characteristics; the proposed model is mainly based on quantitative and qualitative criteria can be used to help credit officers make better decisions when evaluating future loan applications. A real-world credit application of cases of both granted and rejected applications from BTE bank was employed to develop the rating model. The experimental outcomes showed that this approach area promising addition to the existing classification methods. It therefore requires a high responsibility and commitment of managers in the process of evaluation and decision-making to reduce both the risk of default and the risk of debt distress.
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Sopini, Pupu. "Tingkat Kesehatan Bank Berdasarkan Analisis RGEC Pada Bank BNI 46." EKONOMIS : Journal of Economics and Business 2, no. 2 (October 4, 2018): 194. http://dx.doi.org/10.33087/ekonomis.v2i2.44.
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This research aims to know the level of health of Bank BNI 46 using RGEC (Risk Profile, Good Corporate Governance, Earning, Capital). Assessment of the health of the bank rate is used to determine whether the bank is in a very healthy condition, healthy, reasonably healthy, less healthy, or unhealthy. A healthy bank is a bank that is able to keep and maintain the trust of the community, can perform the function of intermediary, can help smooth the payment traffic and can be used by the Government in implementing the various policies relating to, especially monetary policy. This type of research uses descriptive analysis methods with quantitative approach that aims to describe systematically and factual about the facts as well as the relationships between variables are investigated by means of collecting data, processing menginterprestasikan, analyze, and secondary data from the financial statements of Bank BNI 46. The results showed that the Risk profiles of the components of the Non Performing Loan (NPL) average value below 2% which means that bank BNI 46 are at a very healthy state, means the bank can control the risk of going bad credit happens. As for the components of the Loan to Deposit Ratio (LDR) bank BNI 46 from 2012-2016 experience fluctuating growth declines and are in the position well enough. This suggests that the ability of the lower liquidity of bank BNI 46. The results of the rating component of Good Corporate Governance (GCG) is at rank 2 which means in a State of healthy, so the bank has good corporate governance. Assessment of Earnings/earning ratios seen from the value of the Return on Equity (ROE) above 15% very good circumstances, it means that the bank maintains consistent gains its profits. NET Interest Margin (NIM)) bank BNI 46 in 2007-2016 have a rating above 3% NIM means the bank is in very good condition. Value-based capital components Capital Adequacy Ratio (CAR) have a rating above 11% which means it is in very good condition. This means bank BNI 46 have capital adequacy to fulfill obligations that are owned, both in its business activities as well as funding to cover the risk in the future.
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Sunarmi Sunarmi. "PKM IMPLEMENTASI SIMPAN PINJAM PADA BANK PERKRIDITAN RAKYAT BERBASIS CLIENT SERVER." JURNAL PENGABDIAN MASYARAKAT INDONESIA 1, no. 1 (February 16, 2022): 22–33. http://dx.doi.org/10.55606/jpmi.v1i1.76.
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Bima Credit and Savings Cooperative Credit Union is a financial institution that provides transaction services to store and borrow money to its members. Bima Savings and Loan Cooperative needs to have an information system to facilitate data processing, carry out tasks appropriately, and minimize errors in recording transactions and financial calculations. To meet the above needs, it is necessary to design a savings and loan information system. The system development method used is SDLC (System Development Life Cycle) which stands for several stages, namely the planning stage, the analysis phase, the design stage, the implementation stage and the maintenance phase, but the maintenance stage is not included in the development of this system. This design uses a programming language that is C # in its creation and mysql as its database. The results of this study are to provide convenience for officers in managing savings and loan data. Desktop-based Savings and Loan Information System, can help to manage, search, make savings and loan reports quickly and easily, and can make savings and loan arrangements and calculate interest directly through the application without being done manually.
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Abdul Kara, Katon. "ANALISA PERANCANGAN SIMPAN PINJAM PADA BANK PERKRIDITAN RAKYAT BERBASIS CLIENT SERVER." Jurnal Pengabdian Pada Masyarakat Indonesia 1, no. 1 (February 23, 2022): 31–41. http://dx.doi.org/10.55542/jppmi.v1i1.178.
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Bima Credit and Savings Cooperative Credit Union is a financial institution that provides transaction services to store and borrow money to its members. Bima Savings and Loan Cooperative needs to have an information system to facilitate data processing, carry out tasks appropriately, and minimize errors in recording transactions and financial calculations. To meet the above needs, it is necessary to design a savings and loan information system. The system development method used is SDLC (System Development Life Cycle) which stands for several stages, namely the planning stage, the analysis phase, the design stage, the implementation stage and the maintenance phase, but the maintenance stage is not included in the development of this system. This design uses a programming language that is C # in its creation and mysql as its database. The results of this study are to provide convenience for officers in managing savings and loan data. Desktop-based Savings and Loan Information System, can help to manage, search, make savings and loan reports quickly and easily, and can make savings and loan arrangements and calculate interest directly through the application without being done manually.