scholarly journals Predicting Loan Defaults Using Logistic Regression

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Selena Zhao ◽  
Jiying Zou
Keyword(s):  
Logistic Regression ◽  
Exploratory Data Analysis ◽  
Predictor Variables ◽  
Loan Default ◽  
Stratified Sample ◽  
Anonymized Data ◽  
Exploratory Data ◽  
Loan Defaults ◽  
The Interest Rate ◽  
Made In

We used anonymized data from a loan company to analyze correlations between loan defaults and other characteristics of loans or borrowers of loans. We performed an exploratory data analysis of the different factors and how they correlated with loan defaults. Using observations made in the EDA, we proceeded to use logistic regression to predict the odds of loan defaults with several loan characteristics as predictor variables. Different models were evaluated and cross-validated using AIC, AUC, and predicted accuracy. Weighted accuracy was also measured because the loan dataset was a stratified sample. We concluded that the interest rate most accurately predicted the odds of a loan default and that the most useful model was both simplistic and accurate. Research was limited by the variables that were not analyzed during EDA, the limited variables the loan dataset contained, and the modeling technique used.

scholarly journals Logistic Regression Analysis Of Predictors Of Loan Defaults By Customers Of Non-Traditional Banks In Ghana

2016 ◽  
Vol 12 (1) ◽  
pp. 175 ◽  
Author(s):  
Edinam Agbemava ◽  
Israel Kofi Nyarko ◽  
Thomas Clarkson Adade ◽  
Albert K. Bediako
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Internal Control ◽  
Default Risk ◽  
Risk Model ◽  
Control Measure ◽  
Microfinance Institutions ◽  
Loan Default ◽  
Loan Defaults

The objective of this research is to identify the risk factors that influence loan defaults by customers in the microfinance sector and to develop a model that links these factors to credit default by customers in the sector. Data from a microfinance institution based in Accra Ghana was used. A binomial logistic regression analysis was fitted to a data of 548 customers who were granted credit from January 2013 to December 2014. The results of the study revealed that six factors: X3 (Marital Status); X7 (Dependents); X11 (Type of Collateral or Security); X13(Assessment); X15 (Duration); and X16 (Loan Type) were statistically significant in the prediction of loan default payment with a predicted default rate of 86.67%. It is therefore suggested that microfinance institutions adopt among others, the default risk model to ascertain the level of risk since it’s relatively efficient and cost effective. There should also be up to date training for loan officers of microfinance institutions in order to improve on their assessment skills and methodology. The supervising body of microfinance institutions (Bank of Ghana) should also consider enacting laws that will ensure that all such institutions in Ghana are roped into centralized database to check multiple borrowing and also serve as an internal control measure for the sustainability of these institutions.

scholarly journals Evaluating Default Risk and Loan Performance in UK Peer-to-Peer Lending: Evidence from Funding Circle

2021 ◽  
Vol 25 (5) ◽  
pp. 530-538
Author(s):  
Boyu Xu ◽  
◽  
Zhifang Su ◽  
Jan Celler
Keyword(s):  
Interest Rate ◽  
Default Risk ◽  
Binary Logistic Regression ◽  
The United States ◽  
Peer To Peer ◽  
Loan Default ◽  
P2p Lending ◽  
Loan Defaults ◽  
The Interest Rate ◽  
The Uk

The United Kingdom is the third-largest peer-to-peer (P2P) lending market in the world, which is surpassed only by the two dominant forces in P2P investing, China and the United States of America. As an innovative financial market in the UK, P2P lending brings not only many opportunities but also many risks, especially the loan default risk. In this context, this paper uses binary logistic regression and survival analysis to evaluate default risk and loan performance in UK P2P lending. The empirical results indicate that credit group, loan purpose for capital needs, sector type, loan amount, interest rate, loan term, and the age of the company all have a significant impact on the probability of loan default. Among them, the interest rate, loan term, and loan purpose for capital needs are the three most important determinants of the probability of loan defaults and survival time of loans.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Covid-19 Cases in India: A Visual Exploratory Data Analysis Model (Preprint)

2020 ◽  
Author(s):  
Jayesh S
Keyword(s):  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Case Fatality ◽  
Public Health Emergency ◽  
Virus Spread ◽  
Analysis Model ◽  
Case Fatality Ratio ◽  
First Case ◽  
Exploratory Data ◽  
The Government

UNSTRUCTURED Covid-19 outbreak was first reported in Wuhan, China. The deadly virus spread not just the disease, but fear around the globe. On January 2020, WHO declared COVID-19 as a Public Health Emergency of International Concern (PHEIC). First case of Covid-19 in India was reported on January 30, 2020. By the time, India was prepared in fighting against the virus. India has taken various measures to tackle the situation. In this paper, an exploratory data analysis of Covid-19 cases in India is carried out. Data namely number of cases, testing done, Case Fatality ratio, Number of deaths, change in visits stringency index and measures taken by the government is used for modelling and visual exploratory data analysis.

scholarly journals Multivariate Statistical Approach for Nephrines in Women with Obesity

Molecules ◽  
2021 ◽  
Vol 26 (5) ◽  
pp. 1393
Author(s):  
Ralitsa Robeva ◽  
Miroslava Nedyalkova ◽  
Georgi Kirilov ◽  
Atanaska Elenkova ◽  
Sabina Zacharieva ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Metabolic Syndrome ◽  
Data Analysis ◽  
Stress Response ◽  
Exploratory Data Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Obese Women ◽  
Exploratory Data

Catecholamines are physiological regulators of carbohydrate and lipid metabolism during stress, but their chronic influence on metabolic changes in obese patients is still not clarified. The present study aimed to establish the associations between the catecholamine metabolites and metabolic syndrome (MS) components in obese women as well as to reveal the possible hidden subgroups of patients through hierarchical cluster analysis and principal component analysis. The 24-h urine excretion of metanephrine and normetanephrine was investigated in 150 obese women (54 non diabetic without MS, 70 non-diabetic with MS and 26 with type 2 diabetes). The interrelations between carbohydrate disturbances, metabolic syndrome components and stress response hormones were studied. Exploratory data analysis was used to determine different patterns of similarities among the patients. Normetanephrine concentrations were significantly increased in postmenopausal patients and in women with morbid obesity, type 2 diabetes, and hypertension but not with prediabetes. Both metanephrine and normetanephrine levels were positively associated with glucose concentrations one hour after glucose load irrespectively of the insulin levels. The exploratory data analysis showed different risk subgroups among the investigated obese women. The development of predictive tools that include not only traditional metabolic risk factors, but also markers of stress response systems might help for specific risk estimation in obesity patients.

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