Dissertations / Theses on the topic 'Prediction of loan default'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Prediction of loan default.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
Granström, Daria, and Johan Abrahamsson. "Loan Default Prediction using Supervised Machine Learning Algorithms." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252312.
Full textDet är nödvändigt för en bank att ha en bra uppskattning på hur stor risk den bär med avseende på kunders fallissemang. Olika statistiska metoder har använts för att estimera denna risk, men med den nuvarande utvecklingen inom maskininlärningsområdet har det väckt ett intesse att utforska om maskininlärningsmetoder kan förbättra kvaliteten på riskuppskattningen. Syftet med denna avhandling är att undersöka vilken metod av de implementerade maskininlärningsmetoderna presterar bäst för modellering av fallissemangprediktion med avseende på valda modelvaldieringsparametrar. De implementerade metoderna var Logistisk Regression, Random Forest, Decision Tree, AdaBoost, XGBoost, Artificiella neurala nätverk och Stödvektormaskin. En översamplingsteknik, SMOTE, användes för att behandla obalansen i klassfördelningen för svarsvariabeln. Resultatet blev följande: XGBoost utan implementering av SMOTE visade bäst resultat med avseende på den valda metriken.
Stone, Devon. "An exploration of alternative features in micro-finance loan default prediction models." Master's thesis, Faculty of Science, 2020. http://hdl.handle.net/11427/32377.
Full textShan, Chenyu, and 陜晨煜. "Credit default swaps (CDS) and loan financing." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2013. http://hub.hku.hk/bib/B5089965X.
Full textpublished_or_final_version
Economics and Finance
Doctoral
Doctor of Philosophy
Tian, Shaonan. "Essays on Corporate Default Prediction." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1352403546.
Full textOlsen, Hunter. "An Analysis of Post Great Recession Student Loan Default." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/cmc_theses/1960.
Full textLeow, Mindy. "Credit risk models for mortgage loan loss given default." Thesis, University of Southampton, 2010. https://eprints.soton.ac.uk/170515/.
Full textNgufor, Patrick. "Quantitative study of perceptions of business owners and loan officers on loan delinquency and default." Thesis, University of Phoenix, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3578584.
Full textThis research seeks to document if differences in perceptions of small business creditworthiness and lending practices of credit union and commercial lenders exist. This study applied a quantitative method to answer five questions: (1)How do small business owners perceive commercial lenders? (2)How do small business owners perceive credit union lenders? (3)How do commercial banks perceive small businesses? (4)How do credit unions perceive small businesses? (5)What are the differences in the perceptions of small businesses, commercial banks, and credit unions? The study used a quantitative survey instrument to gather data and the data was compared and contrasted among groups (Fitzgerald & Rumrill, 2004). The chi-squared test of differences in probabilities and the goodness of fit test were applied (Figure 2A) to determine if there were differences in probabilities between answers.
The results of this study are significant to small business and banking leaders by helping to define how lenders’ and small businesses’ perceptions affect the differences in loan delinquency rates between commercial lenders and credit unions lenders and by offering new insight into how loan delinquency rates can be reduced. The results also pointed to inherent perceptions of small business owners and lenders that might contribute to the root causes of loan defaults and delinquencies. The results provided information upon which small business owners and financial institution loan officers might act in order to understand how to better manage loans and to reduce the rate of loan delinquency.
Aguilera, Nelson A. "Credit rationing and loan default in formal rural credit markets." Connect to resource, 1990. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1232115721.
Full textMphaka, Patrick. "Strategies for Reducing Microfinance Loan Default in Low-Income Markets." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4391.
Full textWang, Yangzhengxuan. "Corporate default prediction : models, drivers and measurements." Thesis, University of Exeter, 2011. http://hdl.handle.net/10036/3457.
Full textEmenike, Obioma. "Business loan default in Nigerian commercial banks : from causes to remedies." Thesis, Stellenbosch : Stellenbosch University, 2011. http://hdl.handle.net/10019.1/97167.
Full textENGLISH ABSTRACT: A sound and favourable financial climate is necessary for any forward-looking economy to thrive. This, amongst others, includes the extent to which the commercial banks are able to discharge their intermediating role in the demand and supply of credit necessary to sustain commercial businesses. Indeed, in the last decade, the Nigerian banking industry has witnessed swings with the attendant effects on the business community. One of the downsides has been the incidence of loan default which led to many banks recording astronomical levels of bad loans in their 2008 financial reports. The drastic measures taken by the Central Bank of Nigeria of relieving eight CEOs of their jobs in September 2009 further highlights the import of this subject matter. This paper gives an overview of the concept of loan default in Nigerian commercial banks ranging from the causes to the remedies currently in place to checkmate it. A field survey on loan officers, credit analysts and credit risk managers in some select banks was carried out. The findings reveal that the banks have a rather cautious approach to lending with certain classes of loans classified. Causal factors leading to loan delinquencies categorised into environmental, bank specific and borrower specific factors were analysed to have contributed equally to causing loan default in Nigeria. Lastly, the regression results indicated that there was a significant relationship between measures adopted by the banks in the face of increa
Kelley, Samuel Hanson. "Factors Affecting Student Loan Default in Proprietary Non-Degree Granting Colleges." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/3898.
Full textBlack, Kevin. "Determining capital adequacy for a community bank's agricultural loan portfolio." Thesis, Kansas State University, 2015. http://hdl.handle.net/2097/35221.
Full textDepartment of Agricultural Economics
Brian C. Briggeman
As the recent financial crisis brought to light, the ability of commercial banks to quantify and better manage risk in their loan portfolios is paramount to their continued success and viability. Assessing, managing, and retaining capital is now a larger issue than ever given this event as well as the advent of the Basel III Accord. Pinnacle Bancorp is a community banking organization headquartered in Omaha, Nebraska with roughly $8.6 billion in assets. The company is also one of the largest agricultural lenders in the country and the largest agricultural lender among traditional community banks. Given the ominous outlook heading into 2016 for agricultural producers from lower projected net incomes and increased borrowing costs following Federal Reserve action on the Fed Funds Rate, many banks worry about the increased likelihood of default for agricultural producers. The objective of this thesis is to determine the adequacy of Pinnacle Bank’s equity capital relative to the agricultural loan portfolio. This process begins by employing binary logit regression in an effort to determine the probability of default for the bank’s agricultural loan portfolio. With default likelihood quantified, efforts are then made to determine the bank’s credit value-at-risk at various solvency levels. These figures are then compared to current capital levels in order to determine the adequacy of bank capital as measured by five key regulatory ratios ultimately imposed by Basel III. Finally, recommendations are made to management as to the adequacy of bank capital relative to the agricultural loan portfolio and any future efforts that need to be made in order to determine and ensure the adequacy of bank capital for the entire loan portfolio.
Lai, Hengwa. "Agency risk in CMBS default resolution : a case study of the Peter Cooper Village - Stuyvesant Town mortgage loan default." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62055.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 54-56).
Between 2010 and 2018, approximately $410 billion of maturing CMBS loans are expected not to able to refinance; that is, they are in high risk of default. The current real estate downturn has not only pushed delinquencies to a historic high but has also inflicted losses to bondholders. When losses are realized through foreclosure, junior bondholders can have the face amount of their investment significantly reduced with no cash payment, while the senior bondholders receive partial repayment of their investment at par. Alternatively, loan modifications, or workouts, yield different outcomes which are more favorable to the junior bondholders. The rising tide of loan defaults and loan workouts will certainly exacerbate the ongoing "tranche war" among the CMBS bondholders. Consequently, it is imperative to understand how the CMBS servicing structure governs default resolution and loan workouts. By analyzing the recent default of Peter Cooper Village-Stuyvesant Town, this study will examine the case of the largest commercial real estate default in the US history as a real life example to illustrate whether the overlapping role of B-Piece buyer and Special Servicer adversely affects workout prudence. Through interviews with industry professionals and a review of the Pooling and Servicing Agreement (PSA), and a review of the transcript of the CMBS Investment Grade Bondholder Forum in June, 2010, the study proposes structural changes that could potentially mitigate agency risk inherent in the current servicing structure..
by Hengwa Lai.
S.M.in Real Estate Development
Chan, Yuen-yee Emily. "A Study of mortgage transaction goverance in Hong Kong with particular reference to mortgage default." Click to view the E-thesis via HKU Scholars Hub, 2004. http://lookup.lib.hku.hk/lookup/bib/B37905284.
Full textBetz, Jennifer [Verfasser], and Daniel [Akademischer Betreuer] Rösch. "Resolution of defaulted loan contracts - An empirical analysis of default resolution time and loss given default / Jennifer Betz ; Betreuer: Daniel Rösch." Regensburg : Universitätsbibliothek Regensburg, 2018. http://d-nb.info/1164765604/34.
Full textSustersic, Jennifer Lynn. "Do traded credit default swaps impact lenders' monitoring activities? Evidence from private loan agreements." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1339512002.
Full textChou, Hsin-Yi, and 周欣怡. "Mortgage Loan Default Prediction-The Application of Survival Analysis Model." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/67031033284360611934.
Full text真理大學
財經研究所
96
Mortgage loan is one of the main earnings of the Banking, and residential mortgage loan takes the major part of consumer loan business. In recent years, there are competing intensely between the banks. In order to have an occupation in the market, banks forsake their credit principles. Due to the loan quality became worse and worse, and non-performing loan ratio remains high. Such as dual-card storm (credit cards and cash cards) in 2005 and American sub-prime mortgages in 2007, all of they have serious influence in financial market. So as to diminish non-performing loan problem, banks should to construct an effective housing loan credit scoring system. Therefore banks can grasp most correct background information of creditor, and reduce the default risk in the future. This paper based on a local commercial bank’s housing loan databank from November 1, 1995 to November 30, 2005. We apply Survival Analysis-Cox regression Model, Logistic Regression Model, CHAID decision tree model and CART decision tree model to construct the Mortgage Loan Scoring model. Results show that age, occupation...variables have an significance to normal and default loans. The empirical results also show that Cox model can outperform the Logit, CHAID and CART model in percentage of correct rate in out-sample. This represent the Cox model has a high performance than Logit, CHAID and CART model. In the last, to compare the performance of out-sample, we consider the ROC ratio. The results also reveal that Cox model can still beat the entire classified model.
CHIU, CHING-HUAN, and 邱敬桓. "An Application of Feature Selection to P2P Loan Default Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/y7ym8q.
Full text輔仁大學
金融與國際企業學系金融碩士班
107
Along with the trend of the development of the financial technology,online peer to peer is growing rapidly,the necessity of the risk control on P2P is more and more important issue,hence this study uses the machine learning to analyze the data of the Lending Club,which is the biggest P2P platform in America and to predict P2P loan default. Feature selection is a pretty crucial procedure before analyzing the data,because it not only can reduce the complexity of the data construction,but avoid the occurrence of overfitting. This study chooses the random forest、extreme gradient boosting、logistic regression and support vector machine as the feature selection model. Finally, this study gets the intersection according to the result of the four models,so eight features have been chosen,such as the ratio of total current balance to high credit、debt to income、funded amount、grade、installment、home ownership、purpose and total high credit/credit limit. Then, we choose the extreme gradient boosting as our classification model,the result shows that the power of prediction has been improved,and this study selects four indicators to stand for the greater prediction,such as “Accuracy”、“Precision”、“F1 score” and “G means”. For the P2P platform,the result of this study not only can reduce the cost when collecting the lending factors,but lower the situation of default.
Chen, Jie-Awei, and 陳皆回. "The Performance Comparison of Various Default Prediction Models for Mortgage Loan." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/09010428489538842045.
Full text朝陽科技大學
財務金融系碩士班
99
The mortgage loan is the highest proportion of various consumptive loan services for Taiwan financial institutions, includes domestic banks, the local branches of foreign banks and medium business banks. The revenues of the Taiwan financial institutions is significantly related to the proportion of overdue mortgages. With gradual opening of financial policy, financial system heads for liberalization and globalization. Local banks, private or public, intend to increase market share so that the crediting principles are somewhat loose and quality lowers as well. As a result, the bad debt rate is on the increase year by year. This study attempts to Classification tree model, Bayesian classification and the rough set model as a supplementary tool to explore the predictive value, and to identify more representative decision-making model. The purpose of this study is to build a fair and objective default prediction models for mortgage loan. The findings of this study can provide useful information to banks for making decision about credit policy of personal mortgage in the future. Besides, it can increase the correctness from sieve of default customer and decrease the loss of banks. The empirical results show that Classification tree model has the highest overall accuracy rate reached 85.3%.
Yeh, Tse-Ying, and 葉澤瑩. "An Application of Survival Analysis to P2P Lending Loan Default Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/3ajc5c.
Full text輔仁大學
金融與國際企業學系金融碩士班
107
P2P Lending is at higher default risk than traditional financial institutions most of the time. In this research, we use Cox proportional hazards model in survival analysis to study P2P lending loan default and analyze effects of personal financial conditions and environmental fluctuation by 10 individual and 5 macroeconomic factors. We hope to predict the timing of default on P2P lending. In this research, we use loan data on Lending Club in 2012~2015. The observation period is from Jan, 2012 to Oct, 2018. We define "default" as payments which are 120 days overdue, and "survival period" as months from lending to default. The results show that the lower the default cutoff point is, the higher the accuracy of loan status and the higher the missed loan ratio (numbers of loans predicted normal but actually default / numbers of loans predicted normal) is. We find that a shorter predicted period outperform a longer period. Such results are not sensitive to the choices of cutoff points for the predicted period of one and a half to two years. For survival period prediction, the higher the cutoff point is, the better the early warning effect is. Also, the missed loan ratio and survival period predictability will be better when including macroeconomic factors. Besides, the survival curves of the following factors have high sensitivity: interest rate, annual income, debt to income, inquiries in past 6 months, grade, home ownership, and purpose.
Ma, Jo-Ya, and 馬若雅. "The Influence of Open Data on the P2P Loan Default Prediction." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/36r7hy.
Full text輔仁大學
金融與國際企業學系金融碩士班
107
P2P lending industry is gradually emerging in Taiwan. Due to the lack of traditional analytical data, it is necessary to collect new data to assist in the analysis. The open data is relatively easy to collect, and the collection cost is low. The study will test the influence of open data on the p2p loan default prediction. We used the Prosper platform as the research object. During the study period, from 2010 to 2014, the regression prediction model was established using logistic regression and random forest. In addition to the control variables, the explanatory variables are added to the control variable model by public data, and the other part is used as the basis for segmentation data, and the predicted default rate after grouping is compared with the default rate of the parent. From the empirical results, it can be known that the data will be higher than the maternal default rate, with a rate of about 60%, and the predicted default rate can be increased by up to 10%. Therefore, the study believes that the open data variable can improve the existing default forecast.
Yi, Jessica Moonhee. "Identification of relevant predictors of loan default using the Elastic Net model." Thesis, 2017. http://hdl.handle.net/2440/114273.
Full textThesis (Ph.D.) -- University of Adelaide, Business School, 2018
Chuang, Ming-Ter Morris, and 莊明德. "A Study of e-loan Default Prediction Modeling with Neural Network Applications." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/15263358316410250727.
Full text元智大學
管理研究所
97
The purpose of this study is going to set up the financial report early alarm model with Neural Network methodology. The forecast model is used to detect the financial crisis in few years latter happening in a company. Most Enterprises encounter the financial crisis can often be offered from the financial ratio from the financial report. This research will combine each financial ratio from the Taiwan Economic Journal database, through the analytic approach that combines the Self-Organizing Feature Map (SOFM) to find out the similar group, and using K-means looking for fitness clustering. In the process, we then choose the six majors industries in Taiwan, and pick up the 89 companies, which including the listed companies or was in the listed companies to do analysis. Finally, we asked an expert to help us recognizing the company with our clustering levels, and hope the model that is purposed by this research is able to an analysis tool in the practice.
YEH, HSIN-YI, and 葉昕宜. "Data Science for Loan Default Probability Prediction in Online Peer-to-Peer Lending." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/ur7f37.
Full text輔仁大學
資訊管理學系碩士班
107
In recent years, P2P lending had become a global trend because of the development of financial technology. P2P lending is a way of crossfunding from the lenders through the Internet, and then loaning the collected funds to the borrower. As an example of Lending Club, the world's largest online P2P lending market. Since 2011, the number of loans and the amount of loans were increase year over year. In 2017, there were 750,000 loans, and the loan amount was reach up to 8.9 billion. In traditional financial institutions, there will be strict review criteria and access to important credit score of the borrower. The P2P platform was designed to eliminate the cumbersome lending process of financial institutions from the huge amount of data collected in the past, then analyzed historical through data exploration. This study will build a credit scoring model through machine learning to eliminate guesswork in financial decisions. This study proposes a data science framework to solve the probability of default in P2P lending based on machine learning. This process included data preprocessing, imbalanced data processing, feature selection, learning algorithm, optimization model hyperparameter, evaluation method and feature importance ranking. In the case of Lending Club, using different imbalance methods to sample features like personal characteristics, credit data, and platform, then use the LASSO algorithm to select the important features. This study created multiple models like logistic regression, neural network, random forest and XGBoost, then find the best hyperparameters for each model using the particle swarm optimization algorithm. Finally, using different metrics to evaluate those models, and find the important features to predict the probability of default of the borrower. This study will demonstrate the feasibility and effectiveness of the P2P credit risk model.
Li, Chia-sheng, and 李嘉昇. "The Research of Loan Default Predicting Model for Enterprise." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/08894523089433339897.
Full text淡江大學
財務金融學系碩士在職專班
99
This thesis applies event study to investigate possibility of enterprise default risk. We exam the financial variable and non- financial variable by Z-Score model and Logistic Model from local bank in Taiwan. Corporate lending by banks is also an important source of income for businesses to thrive and growing partner. But the bank''s decision whether to grant business credit line, you must refer to a number of indicators, including the financial dimension and non-financial dimension of the variables. Interest income earned on loans to banks and to avoid loss of corporate defaults,.So how banks in the balance between risk and return, it is a very important issue. This study is based on a domestic commercial bank 97 to 99 years of corporate defaults as the research object households, the use of financial and non-financial dimensions of the variables to do an empirical analysis. Using Altman''s Z-Score value and logistic models Logistic Model to analyze the companies in breach of the annual report is likely to show a default situation, And use normal household financial ratios and default account for comparison to confirm the applicability of the model in the bank''s credit policy.
Liou, Tsung-Je, and 劉宗哲. "A Comparative Study of Predictive Models for Mortgage Loan Default." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/95925511748631559903.
Full text國立高雄第一科技大學
金融營運所
90
A Comparative Study of Predictive Models for Mortgage Loan Default Student: Tsung-Je Liou Advisors: Chia-Hsing Huang Department of Financial Operations National Kaohsiung First University of Science and Technology Abstract The debtors’ default on mortgage loan is an important issue for local banks. It does not only deteriorate the capital structure of banks but also damage their profit margin especially during financial crisis or economic depression. Therefore, a default prediction model for banks to improve the mortgage loan decisions and/or to reduce their potential losses is highly needed. This study tries to establish a better prediction model of the debtors’ default for bank’s mortgage loan decision by searching a better combination of predicting variables. Using two statistical techniques (Binary Logistic regression and discriminant analysis), this study analyzes the accuracy rate of prediction and the goodness of fit for two models built in this study and five other models (four from past research and one from the “individual credit evaluation form” of sample bank and then compares their performance with these indicators. In addition, the abilities for all models to minimize misclassification costs are also compared. The result shows that the combination of predicting variables formulated by this study is the dominant model either in the performance indicators or in the ability to minimize the misclassification costs.
Chan, Feng-Hua, and 詹鳳華. "Machine Learning Application to Loan Default Comparison and Prediction - A case study in USA and China." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/epykr2.
Full text國立中山大學
國際經營管理碩士學程
106
Online Peer-to-Peer (P2P) lending has been prevailing in the West countries such as USA, UK. The first company to offer peer-to-peer loans in the world was Zopa in UK which provide platform for borrowers can obtain small loan from lenders. In this study, we use machine learning algorithms to predict borrowers’ default risk and discover factors that impact on the rate of loan default with example and data from LendingClub.com. We use logistic regression and random forest to do analysis and identify what is the most influential factor will affect P2P lending. Finally, we compare the difference of P2P lending market between USA and China.
CHENG, KAI-TIAN, and 鄭凱天. "Dimension reduction and the performance of peer to peer loan default prediction-Evidence from Lending Club." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/5s74u9.
Full textLiu, Chiung-Wen, and 劉瓊文. "A Comparative Study of Predictive Models for Bank Mortgage Loan Default." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/32948830571648911916.
Full text輔仁大學
應用統計學研究所
96
In general, the house mortgage loan is one of the main businesses of banks and the most important income in our banking industry. Therefore, the risk of the banking businesses and the robust management of bank really have the pivotal influence. In 2006, the US sub-prime mortgage crisis has lead to credit crunch. Because the property thriving in Taiwan in the last few years and the domestic banking industry intensely competes, the bank has the loose policy in order to increase banking businesses. The Financial Supervisory Commission, Executive Yuan, announced the uncollected accounts receivable to the cover ratio of Non-Performing Loans was 65.15% at the end of January in 2008. So it is important to build an optional predictive model for the bank. The object of this research is the customers of the house mortgage loan in a bank. The ratio of default is 2.41%. In order to build the model, this study uses the default data and the undefault data to construct the model with the rate 1:2. It repeats 10 times sampling in 1:2 proportion and builds models with Logistic Regression and Cox Regression. Finally, this study builds an optional predictive model for the house mortgage loan. The conclusion is as following: 1. In the method of the study, the optional predictive model is Logistic Regression because the predict index of Logistic Regression is better than Cox Regression. 2. In the analysis of Logistic Regression, the Ensemble model is better than any one of the 10 models. Besides, the optional point cut off the ninth percentile. The accuracy rate is 92.56%, the recall rate is 81.88%, true negative rate is 92.82%, and the precision rate is 21.94%.
LIN, FONG SHU, and 馮淑鈴. "The Research of Loan Default Predicting Model for Middle and Small Business." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/70487685411546191557.
Full text中華大學
科技管理學系(所)
96
The middle and small business always play important roles in the track of our country’s development. Because of government policy to facilitate accommodation of middle and small business, the amount of bank loans to middle and small business appears to be increasing. Therefore, for the purpose to enhance bank asset quality, to improve business performance, and to decrease the possibility of middle and small business loan default, it is an important issue to set up a loan default predicting model for banking industry. The object of this research is the middle and small business loan customers of a commercial bank’s branches located in HsinChu and MiaoLio, first we adopt both the financial and non-financial factors to implement an empirical research , and by means of logistic regression to create a predicting model for middle and small business loan risk therewith. According to the result of this research, we find not only the related financial ratios of the default cases vary deeply from year to year, relatively, the related financial ratios of the normal cases are stable, but also some financial ratios show opposite result versus normal status. It reveals the possibility that the default loan customer dresses its financial statement. In terms of non-financial factors, the ratio of default varies from the elements of different industries、period of establishment、collateral、relationship with banks、 age of the person in charge, and whether the person in charge of uses the credit line of cash card or revolving line or not, moreover the difference of default ratio is quite apparent. Finally, this research makes use of the non-financial factors which have discriminating applicability to be the forecasting factors and creates a loan default precaution model for middle and small business thereby, the accurate ratio of the forecast is more than 85%. Hope the result of this research can help financial institutions to set up their middle and small business loan risk evaluation model and adopt some key reference factors therein.
Tsai, Shih-Ting, and 蔡仕廷. "The Study on the Optimum Ratio of Oversampling in the Prediction Models of Default Probability of Small Amount Personal Credit Loan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/37440312343796382859.
Full text輔仁大學
應用統計學研究所
96
The chief revenue of the banks market is the crediting business. The standard of the crediting business may affect the stability of the banks’ operation. Therefore, the banks have to construct the predicting model of default to control the crises. Compared to non-default events, the default events are the rare event. Hence, the process of constructing the default model, the patterns of the default model is always hidden. This study allocates the dataset through the oversampling in order to improve the previous problem. The study uses the small amount personal credit loan (SAPCL) data in a bank to make the research of oversampling. The default rate of the population database is 16.30%. This study uses the sample allocated proportional to the population database to construct the predicting model. On the other hand, oversampling by the different rate of default events and non-default events, which are 1:1, 1:2, 1:3, 1:4, 1:5, are also used. And in order to increase the correct predicting rate, this study also tries two additional rates, which are 3:1 and 2:1. This study evaluates the powers of predictions among 7 different oversampling rates by comparing the results of the predicting models. The conclusions of the study are as follows: In different business strategies, the index which the banks focus on is different. If the bank concerns the true negative rate (TNR), we can use the rate 1:2 to construct the model. If the bank concerns the recall rate (RR), we can use the rate 3:1 to construct the model. If the bank concerns the accuracy rate (AR) and precision rate (RR), we can use the rate 1:5 to construct the model. Because the rate 1:5 is the same as the population database, this study suggests that it does not need to use the oversampling techniques. When considering the cost/profit in the model, the 1;5 oversampling model has the best performance. This means that the oversampling model does not have better performance when considering cost/profit.
Lin, Yan-Ping, and 林燕萍. "The Model of Prediction Default Behavior in Consumer Loan—A Comparison of DEA-DA, Neural Networks, Logistic Regression and Discriminant Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/34226371046996944406.
Full text中華大學
經營管理研究所
95
As the banks focus on the loan to transfer from commercial loan to consumer loan gradually, the banking of consumer loan made boththe banks and the economy growth to bring the effect directly. However increasing the banking of consumer loan simultaneously, it also betrays to the critical problems what the borrower default on loan gradually. Therefore this paper main discussion influence of money attitude toward consumer loan default behavior, and combined the money attitudes and demographic of borrower to establish the pre-warning model for Discriminant analysis, Logistic regression, Neural networks and DEA-DA , developed a set suitable to the pre-warning model for consumer loan defaulted behavior in Taiwan. This study collected the consumer loan data from financial institutions in the Taiwan; we found the money attitudes would affect default behavior throuht the experimental analysis, specially money of attitudes for anxious and maintenance — retention of borrorwer. Moreover, using the basic attributes and money attitudes for immediate information constructed the models, reached above 75% correct rate in four kinds of models, and has the better forecast ability by DEA-DA and neural network model, the sensitivity may reach 80% also the forecast hit rate reaches as high as 94%, this for this article obtained effective pre-warning model. These findings may provide a correct and immediate efficiency pre-warning model for financial industry, also may let the borrower and financial industry understand the importance of money attitudes, raise correct money attitudes improvement expense custom, reduces occurrence the default behavior
Yu-ChangHsueh and 薛佑忠. "The Analysis of Credict Risk Elements in Predicting Loan Default for Middle and Small Businesses." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/72246623246711077237.
Full text國立成功大學
財務金融研究所碩士在職專班
103
Based on Altman’s (1968) Z-score model, this study develop a credit risk detecting model for small-to-medium-sized business (SMB) firms by using 163 bank references of 57 SMB firms collected from the loan customers of Tainan branches of a commercial bank during 2011-2013. This study depicts that, in general, when a bank makes a credit assessment of a SMB firm, besides the firm’s financial factors, non-financial factors play an important role in the assessment process as well. While four financial factors appear to be the most frequent used warning indicators, two non-financial factors, “the owner’s educational background” and “the borrower’s and guarantor’s records of repayment of credit card debt or cash card debt”, should be treated as the significant explanatory warning factors. Disturbed by the lack of transparency in the financial reports of SMB firms, this research targets especially on how the non-financial factors enhances bank officers’ understanding of a company’s financial distress to improve the predictive capacity.
Wang, Wen-Chun, and 王文崇. "Early Default-warning and the Risk-prediction System of the Bank for SMEs Loans." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/28737385112269070890.
Full text朝陽科技大學
財務金融系碩士班
97
This research mainly applies the factor analytic method and the Logistic regression to construct the bank’s “early default-warning and the risk-prediction system of potential lenders in view of the small and medium-sized enterprises”. Considering the financial structure and management characteristic of “small and medium-sized enterprise”, we design an empirical model which includes 11 explanation variables as well as 4 dummy variables, and carry on the empirical analysis. Again by the Proportional Chance, the Maximum Chance and Press the Q value, we evaluate the accuracy of this system prediction. Furthermore, such as “Bayesian Classification and Classification Tree Model”, to conduct robustness test. In order to help the bank to give the loan reference of the risk profile, the loaning out monitoring and the management before the small and medium-sized enterprise to be the new loan household.
Lin, Hsiu-Chuan, and 林秀娟. "Modeling Loss Given Default of Corporate Loan." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/59628343447116972521.
Full text國立清華大學
科技管理研究所
94
The purpose of this article is to construct a loss given default (LGD) estimation model which can discriminate LGD from different characteristics of obligation and collateral, and to offer a reference model for bank that will adopt Advance Internal Rating Based Approach to determine capital requirement of credit risk. This article is based on structured model to construct three different kinds of LGD estimation model which including the loan has no collateral, collateral value is constant and collateral value is stochastic. Specific to the model that concern collateral value is stochastic can take into account the correlation between collateral value and firm’s value, the volatility of collateral value and the volatility of assets value. In empirical analysis we find that the correlation between collateral value and firm’s assets value has significant effect on LGD. Under the same PD, higher correlation between collateral value and firm’s assets value resulting higher LGD. The liquidity of collateral is also an important factor that effect LGD. If the liquidity of collateral is low, it is more difficult to sell collateral to third party and will increase LGD.
Wu, Jenq-I., and 吳政毅. "Corporate Governance, Distance from Default, and Loan." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/75275072575725487977.
Full text輔仁大學
會計學系碩士班
95
In this few years, there have been several financial fraud and crisis events occurring, such as the emptying of Procomp Company and Infodisc Company ,the prettified financial statements of Enron and falsely increased yield of World.com. These events lead governments enforced The New Basel Capital Accord in 2006 and corporate governance and risk management becomes hot issues again. Public consider that managers in pursuing personal interests may transfer the interests of the company and shareholders. Therefore, the purpose of the study is going to figure out how to protect the benefits of the shareholders and the relevant parties involved and to ensure the company's operating performance. This study incorporates KMV default risk of development with corporate governance factors. We wish to observe the relation of DD value (Distance of Default), corporate governance, and loan. The results show that these two factors have positive relation with loan amonts. We therefore have a warning suggest for financial demander and supplier that risk management and corporate governance factors will influence performance and finance structure of a company.
Liu, Chia-Yi, and 劉家儀. "Time Series Analysis of Loan Default Rate." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/393g2s.
Full text國立交通大學
統計學研究所
107
The use of time series analysis in order to provide decision makers a reference information about future possible changes. The single-dimensional ARIMA (Autoregressive Integrated Moving Average; Box & Jenkins, 1976) model, the GARCH (Generalized Autoregressive Conditional Heteroscedasticity; Bollerslev, 1986) model, and the multidimensional VAR (Vector Autoregressive; Hatemi, 2004) model of time series analysis are mainly fitted the default rate of US loan data set which is expected to achieve good prediction results and comparing the results of different model prediction analysis. Take the default rate of the states of the US loan data from 2008 to 2015 as times series. There are two problems in the data needed to be adjusted. (1) Use differential technique in order to smooth non-stationary sequences. (2) Use GARCH model for solving data which contained heterogeneous variation and different scale characteristics. After the sequence consisting with the time series model assumptions, the prediction models of single-dimensional and multi-dimensional models are constructed. Finally, the prediction abilities of each state's default rate in different modes are compared. The single-dimensional ARIMA model is established under the independence of each state which estimates the variables of the state itself. The multidimensional VAR model considers the relationship between states. At last, observing the prediction efficiency and impact of the single-dimensional and multi-dimensional time series analysis mode. We found a better predictive performance by aggregating information through different states.
Hoque, Mohammad Ziaul. "Industrial loan default: the case of Bangladesh." Thesis, 1999. https://vuir.vu.edu.au/15347/.
Full textChang, Kai-Yuan, and 張凱原. "A Study of Default Risk of Consumer Loan - A Case of Vehicle Loan." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/53173110765968622405.
Full textChuang, Tzu-Meo, and 莊子妙. "The Default Risk Analysis of Personal Fiduciary Loan." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/10827439252827929428.
Full text國立高雄應用科技大學
金融資訊研究所
101
Domestic financial institutions industry in the consumer finance business of personal Fiduciary loans increasingly competitive environment, to improve the operational order for the output to reduce audit working time, and then seek opportunities aging, provides a simple formula or standardized audit model is definitely necessary . And objective scientific assessment methods to make more credit, credit information obtained quickly and overall system management and analysis, supplemented by loans for effective decision-making judgment. Therefore, financial institutions should establish a set of objective and impartial credit assessment mode, thereby assisting auditors approved quickly and rigorously standardized lending cases and achieve job security possessed the goal. The study of a random sample of 2,000 Bank personal Fiduciary loans as the research object, with loan bid for all the information provided by the borrower to Logistic Regression to analyze the differences between credit point and then consider each loan approval Xiang empirical variables, a systematic analysis and discussion, which found that the most affecting credit risk and occurrence of overdue principal factors for the credit sector in policy making reference. The results of this study show that the borrower marital status, occupation type and whether the holding of real estate three variables on micro-credit loans business credit quality is indeed a high level of explanatory power. Financial institution or credit to this competent to assess the credit risk of loans household level, improving loan quality and reduce operating negligence, risk management is also more accurate and effective.
Sun, Jui-tai, and 孫瑞黛. "Unsecured loan default model:Structure change after credit crunch?." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/58739308509490036044.
Full text世新大學
財務金融學研究所(含碩專班)
97
The consumer financial markets are rising after the 1997 Asian Financial Crisis, especially the unsecured personal loans grow rapidly due to high user demand. In 1999 Cosmos Bank is the first bank to launch cash card product and make a big boom. The cash card boom attract all the Taiwan banks launch their products and lastly reach the peak in 2003, and finally cause credit crunch (double-card debt storm). After that, due to our government’s strictly financial supervision and the banks’ self-disciplined, credit crunch are temporarily settled down. But how to control the incident, has become our government’s and the banks most important lesson. Our research study on a Taiwan bank’s unsecured personal loans which loaned two years before and after 2005 credit crunch. We choose 4 most representative samples of small credit and cash card products to explore the impact of customer default risk factors by SPSS statistical software package. 13 covariates include sex (X1), age (X2), education (X3), marital status (X4), home ownership patterns (X5), residence time (X6 ), seniority (X7), the number of dependents (X8), annual income (X9), pieces of credit card (X10), number of recent bank queries (X11), other bank loans (X12), the total number of bank loans (X13) are analyzed for Logistic Regression analysis. From the study analysis result, we find that the 3 common significant covariates of all 4 samples are education (X3), pieces of credit card (X10), number of recent bank queries (X11). We also found that the significant effects of variables of basic customer attributes (X1 ~ X8) are the same. About the cash card sample before credit crunch, all the variables except education (X3) are insignificant, which is different from small credit, but the cash card sample’s trend go in line with the small credit after the credit crunch.
Li, Kuei-Jung, and 李桂榮. "Default factors of the owning home mortgage loan." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/60696196361768570042.
Full text國立高雄第一科技大學
金融營運所
91
The past research on mortgage loan default did not separate the mortgages of owning and invested properties. In fact, the purposes and financial burden of these two types of properties are different. The owning properties are used by the owners and are more willing to pay the mortgages. This research only studied the factors that affect the owning home mortgage loan. Data of this research are taken from a branch of bank in southern Taiwan. From the year of 1997 to 2001, in the 426 mortgage loans 136 are default loans and 290 are not default loans. This research used the Logistic regression model to analyze the default factors. It is found that in the eighteen factors, education, housing condition, interest rate, professional, and guarantor have significant impact on default. The higher the education background the less the default rate. New houses have lower default rate. The higher the interest rate the higher the default rate. In terms of professional position, government and school employee have lower default rate. Managers have lower default rate. Those who have guarantors have lower default rate.
CHUNG, TUAN HUA, and 段華忠. "Default factors of the investing home mortgage loan." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/84963501014972740646.
Full text國立臺北大學
企業管理學系
93
The past research on mortgage loan default did not separate the mortgages of owning and invested properties. In fact, the purposes and financial burden of these two types of properties are different. The owning properties are used by the owners and are more willing to pay the mortgages. This research only studied the factors that affect the investing home mortgage loan. Data of this research are taken from a branch of bank in southern Taiwan. From the year of 1999 to 2003, in the 500 mortgage loans 50 are default loans and 450 are not default loans. This research used the Logistic regression model to analyze the default factors. It is found that in the eighteen factors, education, housing condition, interest rate, professional, and guarantor have significant impact on default. The higher the education background the less the default rate. New houses have lower default rate. The higher the interest rate the higher the default rate. In terms of professional position, government and school employee have lower default rate. Managers have lower default rate. Those who have guarantors have lower default rate.
Hao, Duong Ngoc, and 陽玉豪. "A STUDY OF DEFAULT FACTORS ON PERSONAL LOAN." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/10302565742315587705.
Full text樹德科技大學
金融與風險管理系碩士班
99
In the world, the large bank with more experience, much capital that can be threated to go to bankruptcy because of low credit quality. That reasons are valuable studies for Vietnam banking system. In Viet Nam, 60-70 percentage profits of Vietnam banking system are collected from credit income results. So credit risk is higher than others. Improving credit quality, risk reduction, it’s imperative for them. Although, there are some reports on the credit quality, my thesis is focused on default factors on personal loan. The study applies the method of non-probability sampling, specifically convenience sampling method. Linear regression is used to answer research questions. Result shows that marital status, occupation, age, education, gender and income are statistically significant with significance level of 5%. Thus marital status, occupation, age, education, gender and income are related to the credit quality. In addition, the reasons that make personal loans overdue were presented.
Huang, Yu-Hsiang, and 黃郁翔. "A Study in Default Factors of Auto Loan." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/s98xzv.
Full text國立臺灣大學
經濟學研究所
107
A decade after the U.S. subprime crisis in 2007-08, much ink has been spilled on the risks of U.S. subprime lending again. However, the subjects has been switched from house to automobile. In 2016, the subprime auto loans has reached US260 billions, surpassing the level before the global financial crisis. While in Taiwan, the auto sales, as well as auto loan’s share in consumer finance, has been increased year by year since 2009. A vehicle, as opposed to a house, loses its value over time. Therefore, the purpose of this study was to discuss the risk management in financial institutions and to address its influence on expected income. We collected 37,534 auto loans from a domestic financial institution as research samples and employed Logistic regression model for testing the significance of variables. From the empirical results, the loan characteristics with forecasting power include term, loan-to-value ratios, rate, educational level of borrowing applicants, seniority, income and guarantor. The loan interest rate and the delinquency and default rates on auto financing loans are negatively correlated, distinct from that on new and used car loans. The difference may arise from the borrowing purpose, as most subprime borrowers tend to have financing needs rather than those who borrow to buy a car.
Rodrigues, Felipe Augusto Silva. "Determinants of Loan Default in Peer-to-Peer Lending at Different Loan Risk Levels." Master's thesis, 2020. https://hdl.handle.net/10216/139323.
Full textRodrigues, Felipe Augusto Silva. "Determinants of Loan Default in Peer-to-Peer Lending at Different Loan Risk Levels." Dissertação, 2020. https://hdl.handle.net/10216/133139.
Full textTsai, Wei-jen, and 蔡偉仁. "A Study on Loss Given Default for Corporate Loan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/q382jd.
Full text東吳大學
國際經營與貿易學系
96
This study is extracted from a local commercial bank that has sold non-performing loans to the asset management company by choosing 135 cases of corporate loans from 1992 to 2002 as samples. In this article the main direction of this research is to discuss on the influence of the variable factors with LGD (loss given default) and the construction of a LGD model, so as to reduce the non-performing loans of a bank and to improve its competitiveness. First of all, we use the multiple linear regression analysis to forecast the variable factors and to discuss which one can influence the LGD. The experimental evidence showed that the enterprise period of service, loan rate, mortgage, house or stock take for collateral, the economical growth rate and the unemployment rate and the remaining seven variables, have a remarkable influence on the LGD. Secondly, we attempt to construct a LGD estimation model which can discriminate the LGD based on different characteristics of the economy. After this demonstration, its calculation and the prime number only differ by 3.78%, whereby the error is not big. Therefore this model is useful for the LGD examination when the lender applies for loan from financial institution.
Hua, Hsueh Cheng, and 華學誠. "The Analysis of the Default Factors on Mortgage Loan." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/22196240783106004869.
Full text世新大學
經濟學研究所(含碩專班)
98
Mortgage Loan is one of important services in the bank, in which housing loans have been accounted for a great portion. However the regulation relaxed and allowing the new banks to launch in Taiwan, the competition among banks became competitive. In order to pursue the higher market share the banks adopted the loosened credit policy or lower price policy to promote products and attract customers. The competition result in the worse credit quality and the more serious overdue loans. Besides, the current economic downturn affected the financial market. In order to analyze the default factors on mortgage loan, the study selects 382 cases of mortgage loan in a domestic commercial bank from 2003 to 2007 after deducting the loan settled. The study applies the Logistic Regression model to carry out the analysis and the discussion of the default factors on the mortgage loan. The findings show that, education and loan interest rate are two explanatory variables which affect borrowers’ paying behavior significantly. In which loan interest rate shows a positive correlation with paying normally, while the school record shows a inverse correlation. All of two are conformed with the anticipated supposition.