scholarly journals Implementasi Algoritma Decision Tree_J48 untuk Memprediksi Resiko Kredit pada Koperasi Simpan Pinjam (Studi Kasus : Kofipindo Lubuk Pakam)

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Kristin Sinaga ◽  
Efori Buulolo ◽  
Berto Nadeak
Keyword(s):  
Decision Tree ◽  
Credit Risk ◽  
Risk Prediction ◽  
Financial Institutions ◽  
Information Gain ◽  
Prediction Algorithm ◽  
Management Problem ◽  
Processing Data ◽  
Savings And Loan ◽  
Modern Era

The development of the modern era has made many people more influenced in managing business, especially in financial institutions, one of which is credit at the Kofipindo cooperative. Credit is the most important thing in a savings and loan cooperative and it has an effect on the health of the cooperative. However, if there is a management problem that carelessly provides credit, it will be risky for the company, namely there will be a loss of customers and not collectible. To overcome the problems above, a method is needed. The technique that can be utilized in the process of processing data is the classification with the J48 algorithm. J48 algorithm is a classification algorithm applied to decision tree techniques that are processed using information gain. In selecting attributes for object breakers in several classes, the attribute that produces the greatest information gain with the highest information gain value will be chosen as the parent of the next node.Keywords: Credit risk prediction, Algorithm J48

Extreme Learning Machine for Credit Risk Analysis

2018 ◽  
Vol 29 (1) ◽  
pp. 640-652
Author(s):  
Mais Haj Qasem ◽  
Loai Nemer
Keyword(s):  
Risk Analysis ◽  
Decision Tree ◽  
Credit Risk ◽  
Extreme Learning Machine ◽  
Financial Institutions ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Statistical Measures ◽  
Credit Risk Analysis ◽  
Learning Machine

Abstract Credit risk analysis is important for financial institutions that provide loans to businesses and individuals. Banks and other financial institutions generally face risks that are mostly of financial nature; hence, such institutions must balance risks and returns. Analyzing or determining risk levels involved in credits, finances, and loans can be performed through predictive analytic techniques, such as an extreme learning machine (ELM). In this work, we empirically evaluated the performance of an ELM for credit risk problems and compared it to naive Bayes, decision tree, and multi-layer perceptron (MLP). The comparison was conducted on the basis of a German credit risk dataset. The simulation results of statistical measures of performance corroborated that the ELM outperforms naive Bayes, decision tree, and MLP classifiers by 1.8248%, 16.6346%, and 5.8934%, respectively.

The prediction algorithm of credit risk of science and technology finance based on cloud computing

2021 ◽  
pp. 1-17
Author(s):  
Guiping Li
Keyword(s):  
Cloud Computing ◽  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Risk Prediction ◽  
Logistic Regression Model ◽  
Science And Technology ◽  
Principal Component ◽  
Prediction Algorithm ◽  
Financial Indicators

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.

scholarly journals Analisis Dengan Metode Klasifikasi Menggunakan Decission Tree Untuk Memprediksi Penentuan Resiko kredit Bank

JURTEKSI ◽  
2017 ◽  
Vol 4 (1) ◽  
pp. 101-106
Author(s):  
Afdhal Syafnur
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Credit Risk ◽  
Risk Prediction ◽  
Data Mining Techniques ◽  
Analysis Process ◽  
Time And Energy ◽  
Number Of Customers ◽  
Excel Format ◽  
Existing Data

Abstract: There are several facilities in distributing funds to the customer which is owned by Bank Syariah Bukopin. One of them is Kredit Pemilikan Rumah / Housing Loan (mortgage), so far the bank when provides mortgages to customers still uses risk prediction manually in giving credit to customers which is taking up a lot of time and energy especially when the customer reports is further analyzed by the Bank. One technique that can help in predicting the Bank's credit risk determination is Decision Tree which is a technique that is a part of Data Mining techniques to take a decision in the form of a tree. With Decision Tree techniques, it is expected to help the bank to allow faster and easier in predicting the data and getting a conclusion from existing data. One of the ways to predict the data is using Dtreg software. This software only uses data that is in the format of "csv (comma delimited)�, if it is not using the format" csv (comma delimited)", so that the data can not be processed by Dtreg software. When the excel format has been converted to the "csv (comma delimited)" format, the analysis process can be done. Dtreg can generate decision tree, one of them is the result of risk decision from the number of mortgages based on the number of customers. Keywords: data mining, decision tree Abstrak: Ada beberapa fasilitas dalam penyaluran dana ke nasabah yang di miliki Bank Syariah Bukopin. Salah satunya Kredit Pemilikan Rumah (KPR), selama ini pihak Bank memberikan KPR ke nasabah masih menggunakan prediksi resiko secara manual dalam meberikan kredit kepada nasabah yang banyak menyita waktu dan tenaga apalagi pada saat laporan nasabah dianalisa lebih lanjut oleh pihak Bank. Salah satu teknik yang dapat membantu pihak Bank dalam memprediksi Penentuan resiko kredit adalah teknik Decision Tree yang merupakan bagian dari teknik Data Mining untuk mengambil suatu keputusan dalam bentuk pohon. Dengan teknik Decision Tree diharapkan dapat membantu pihak bank agar lebih cepat dan mudah dalam memprediksi data dan menarik suatu kesimpulan dari data yang ada.Salah satu cara memprediksi data tersebut dengan menggunakan software Dtreg. Pada software ini data yang digunakan hanya bisa dalam bentuk format �csv (comma delimited), jika tidak menggunakan format �csv (comma delimited)� maka data tersebut tidak bisa diproses oleh software Dtreg dan selanjutnya jika format excel yang telah dirubah ke format �csv (comma delimited)�, maka akan dapat dilakukan proses analisa. Dtreg dapat menghasilkan pohon keputusan, salah satu nya yaitu hasil keputusan resiko dari jumlah kredit pemilikan rumah berdasarkan jumlah nasabah. Kata kunci: data mining, decision tree

Financial institutions mergers: a strategy choice of wealth maximisation and economic value

2020 ◽  
Vol 12 (4) ◽  
pp. 495-529
Author(s):  
Mohamad Hassan ◽  
Evangelos Giouvris
Keyword(s):  
Credit Risk ◽  
Event Study ◽  
Financial Institutions ◽  
Value Creation ◽  
Economic Value ◽  
Abnormal Returns ◽  
Study Analysis ◽  
Event Window ◽  
Content Type ◽  
Merger Announcement

Purpose This study Investigates Shareholders' value adjustment in response to financial institutions (FIs) merger announcements in the immediate event window and in the extended event window. This study also investigates accounting measures performance, comparison of post-merger to pre-merger, including several cash flow measures and not just profitability measures, as the empirical literature review suggests. Finally, the authors examine FIs mergers orientations of diversification and focus create more value for shareholders (in the immediate announcement window and several months afterward) and/or generates better cash flows, profitability and less credit risk. Design/methodology/approach This study examines FIs merger effect on bidders’ shareholder’s value and on their observed performance. This examination deploys three techniques simultaneously: a) an event study analysis, to estimate and calculate abnormal returns (ARs) and cumulative abnormal returns (CARs) in the narrow windows of the merger announcement, b) buy and hold event study analysis, to estimate ARs in the wider window of the event, +50 to +230 days after the merger announcement and c) an observed performance analysis, of financial and capital efficiency measures before and after the merger announcement; return on equity, liquidity, cost to income ratio, capital to total assets ratio, net loans to total loans, credit risk, loans to deposits ratio, other expenses and total assets, economic value addition, weighted average cost of capital and return on invested capital. Deal criteria of value, mega-deals, strategic orientation (as in Ansoff (1980) growth strategies), acquiring bank size and payment method are set as individually as control variables. Findings Results show that FIs mergers destroy share value for the bidding firms pursuing a market penetration strategy. Market development and product development strategies enable shareholders’ value creation in short and long horizons. Diversification strategies do not influence bidding shareholders’ value. Local bank to bank mergers create shareholders’ value and enhance liquidity and economic value in the short run. Bank to bank cross border mergers create value for bidders’ in the long term but are associated with high costs and higher risks. Originality/value A significant advancement over the current literature is in assessing mergers, not only for bank bidders but also for the three pillars FIs of the financial sector; banks, real-estate companies and investment companies mergers. It is an improvement over current finance literature because it deploys two different strategies in the analysis. At a univariate level, shareholder value creation and market reaction to merger announcements are examined over short (−5 or +5 days) and long (+230 days) windows of the event. Followed by regressing, the resultant CARs and BHARs over financial performance variables at the multivariate level.

Research on Online Scene Teaching Mode of Tobacco Picking Decision Tree Construction Process Integrating Deep Learning

2021 ◽  
Vol 7 (5) ◽  
pp. 3076-3086
Author(s):  
Zhang Shuili ◽  
Zhao Yi ◽  
Zheng Kexin ◽  
Zhang Jun ◽  
Zheng Fuchun
Keyword(s):  
Deep Learning ◽  
Decision Tree ◽  
Online Teaching ◽  
Information Gain ◽  
Teaching Evaluation ◽  
Teaching Process ◽  
Teaching Mode ◽  
Teaching Interaction ◽  
Tree Construction ◽  
Information And Communication

Objectives: In view of the characteristics of online teaching during the coronavirus pandemic and the importance of practical teaching in training students’ skills in the process of graduate education, this paper proposes an online scene teaching mode that takes projects as the carrier and integrates with deep learning. In order to meet the demand for information and communication engineering professionals in the big data context, the whole teaching process is divided into four stages: Topic selection, Teaching project setting, online teaching interaction and teaching evaluation. In the teaching process of Python Data Analysis Foundations, the project “establishment process of tobacco picking decision tree based on information gain” is taken as the teaching case. Prior knowledge and references are pushed through the cloud platform before class, and The scene of tobacco picking affected by the weather is set in the online classroom to guide students to seek solutions to problems, and the results are presented with graphics to assist students to summarize, and then reset the scene to promote knowledge transfer, so as to integrate deep learning into the teaching process, and modify the corresponding stages according to the teaching evaluation results. The content of the scene is gradually increased from easy to difficult, from simple to complex, and from least to most, gradually increasing the difficulty, which enhances students’ learning interest and sense of achievement. Meanwhile, students’ initiative to participate in curriculum research further strengthens the effectiveness of the course in serving scientific research, which has a certain value of popularization and application.

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