The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans

Ghulam, Yaseen; Dhruva, Kamini; Naseem, Sana; Hill, Sophie

doi:10.3390/risks6030101

Cited by 7 publications

(7 citation statements)

References 28 publications

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“…Another method that banks can use to reduce the level of non-performing loans when they want to disburse credit, especially to types of credit that have a high level of sensitivity, is by applying the principle of prudence (selectiveness in choosing debtors) through an assessment of the profile of prospective debtors [19] including the value of the collateral held precisely and accurately [14].…”

Section: Literature Reviewmentioning

confidence: 99%

The Internal Model of Default Credit for Rural Banks in Indonesia

Monalisa¹,

Sugiarto²

2022

IJSDP

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The urgency of the internal model of default credit for the rural bank in Indonesia is increasing due to the recent acceleration of the increase in Non-Performing Loans at rural banks. This research will formulate an internal model of default credit that can reduce the level of default risk in rural banks based on the explanatory sequential design of mixed research method. The findings of quantitative analysis integration by Chi-Square Analysis, Discriminant Analysis, and Logistics Regression analysis at BPR BKK Pekalongan Regency in 2021 will be explored further by qualitative research. Using nine variables that affect debtors' failure, this study finds that the interest rate is a variable that consistently affects the status of default loans using the integration of 3 analyses and qualitative analysis. The study results indicate that rural banks need to pay more attention to determining credit interest rates when prospective debtors apply for credit. The determination of interest rates is related to compensation for risks faced by rural banks in connection with asymmetric information about the debtor's ability to pay while considering the interest rate determined by the IDIC.

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Section: Literature Reviewmentioning

confidence: 99%

The Internal Model of Default Credit for Rural Banks in Indonesia

Monalisa¹,

Sugiarto²

2022

IJSDP

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“…Peningkatan rasio pinjaman terhadap nilai agunan (LTV) meningkatkan kemungkinan gagal bayar menurut Agarwal et al (2008) yang didukung hasil penelitian Adrianda (2011), Itoo et al (2013), dan Lusian (2015). Pengaruh LTV terhadap kegagalan bayar juga diteliti oleh Ghulam et al (2018) Berdasarkan Tabel 4, variabel tingkat inflasi yang berpengaruh nyata terhadap NPF dengan nilai p-value 0,000. Setiap ada kenaikan inflasi sebesar satu persen (%) pada saat penyaluran kredit ke debitur, maka NPF cenderung akan naik sebanyak 1,094 kali sesuai odds rasio sebesar 1,094.…”

Section: Analisis Faktor-faktor Yang Memengaruhi Besaran Kredit (Totaunclassified

Analisis Faktor-Faktor yang Memengaruhi Non Performing Financing di PT TAF dan Pengendaliannya

Wahyudi¹,

Harianto²,

Sahara³

2020

JMO

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PT TAF is one of multifinance companies in Indonesia. The increase in low segment sales from 2013-2017 with a high credit amount led to an increase in the quality of PT TAF's receivables categorized as Non Performing Financing (NPF). This increase in NPF was also accompanied by an increase in PT TAF's net loss in 2017 and affected the company's profits. Therefore an analysis is needed to find out factors that influence the amount of credit and NPF, in order to formulate the strategy to manage it. This study uses secondary data of PT TAF debtors which related to the amount of credit, debtor delays and NPF from 2013 to 2017. The approach of this study is a case study in PT TAF about the level of NPF from 2013 -2017. The method used in this study is descriptive analysis and statistical tests. Multiple linear regression is performed to determine the factors that influence the amount of credit, while binary logistic regression is carried out to determine the factors that influence NPF. The results of the analysis will be used to formulate a strategy proposal for controlling NPF at PT TAF. Factors that significantly influence the amount of the credit are vehicle price, age, loan interest, installment amount, income, down payment, type of first installment, type of vehicle, education, gender, credit package, and credit tenor. The factors that influence NPF are the amount of credit, Loan to Value (LTV), the residence area of the debtor and inflation. The proposed strategy formulations are: to consider factors which affecting amount of credit in granting credit, to increase the debtor portfolio with LTV less than or equal to 80 percent, avoid low down payment and long tenor products, strengthen collection organizational structures and increase team collection capabilities, and utilize the Financial Information Service System from OJK to obtain information about the debtors.

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“…Peer to peer lending [3], [5], [7], [11], [12], commercial banking [2], [4], [13]- [15], insurance [6], agriculture [16], mortgage [17], and small and medium enterprises (SMEs) [8], [12] are different application areas of loan default prediction studies. However, because of certain specific problems and different available dataset, the studies employed different machine learning models.…”

Section: A) Application Areas and The Problemmentioning

confidence: 99%

“…This, as shown, suggests the type of applicable machine learning model, especially where the size of the available dataset determines the performance. The data features used in building the models reviewed range from eight (8) [2], seventeen (17) [4] to twenty-four (24) [13]. Studies with considerable large datasets are Bagherpour [18] of about 20 million loan observations between 2001 -2006, and Rivet [6] which used 1 million loans data.…”

Section: B) Data Features and The Machine Learning Modelsmentioning

confidence: 99%

“…In these, K-Nearest Neighbors [18], Decision Tree [4], [6], [18], Random Forest [18], logistic regression [5], [6], Support Vector Machine [18], Artificial Neural Network [3], [13], [19], and Naïve Bayes [2], [11] are the recorded machine learning models. Asides from studies that did comparative study for loan default prediction performances [2], [5], [18], others employed single [4], [17] and hybrid/ensemble models [3], [6], [8], [9]. Hybrid and ensemble are mostly used.…”

Section: B) Data Features and The Machine Learning Modelsmentioning

confidence: 99%

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A Boosted Decision Tree Model for Predicting Loan Default in P2P Lending Communities

Akanmu,

Gilal

2019

IJEAT

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Loan Default Prediction For Social Lending Is An Emerging Area Of Research In Predictive Analytics. The Need For Large Amount Of Data And Few Available Studies In The Current Loan Default Prediction Models For Social Lending Suggest That Other Viable And Easily Implementable Models Should Be Investigated And Developed. In View Of This, This Study Developed A Data Mining Model For Predicting Loan Default Among Social Lending Patrons, Specifically The Small Business Owners, Using Boosted Decision Tree Model. The United States Small Business Administration (Usba) PubliclyAvailable Loan Administration Dataset Of 27 Features And 899164 Data Instances Was Used In 80:20 Ratios For The Training And Testing Of The Model. 16 Data Features Were Finally Used As Predictors After Data Cleaning And Feature Engineering. The Gradient Boosting Decision Tree Classifier Recorded 99% Accuracy Compared To The Basic Decision Tree Classifier Of 98%. The Model Is Further Evaluated With (A) Receiver Operating Characteristics (Roc) And Area Under Curve (Auc), (B) Cumulative Accuracy Profile (Cap), And (C) Cumulative Accuracy Profile (Cap) Under Auc. Each Of These Model Performance Evaluation Metrics, Especially Roc-Auc, Showed The Relationship Between The True Positives And False Positives That Implies The Model Is A Good Fit.

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The Interaction of Borrower and Loan Characteristics in Predicting Risks of Subprime Automobile Loans

Cited by 7 publications

References 28 publications

The Internal Model of Default Credit for Rural Banks in Indonesia

The Internal Model of Default Credit for Rural Banks in Indonesia

Analisis Faktor-Faktor yang Memengaruhi Non Performing Financing di PT TAF dan Pengendaliannya

A Boosted Decision Tree Model for Predicting Loan Default in P2P Lending Communities

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