1,942 research outputs found

    A Prediction Model for Bank Loans Using Agglomerative Hierarchical Clustering with Classification Approach

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    Businesses depend on banks for financing and other services. The success or failure of a company depends in large part on the ability of the industry to identify credit risk. As a result, banks must analyze whether or not a loan application will default in the future. To evaluate if a loan application was eligible for one, financial firms used highly competent personnel in the past. Machine learning algorithms and neural networks have been used to train class-sifters to forecast an individual's credit score based on their prior credit history, preventing loans from being provided to individuals who have failed on their obligations but these machine learning approaches require modification to solve difficulties such as class imbalance, noise, time complexity. Customers leaving a bank to go to a competitor is known as churn. Customers who can be predicted in advance to leave provide a firm an edge in client retention and growth. Banks may use machine learning to predict the behavior of trusted customers by assessing past data. To retain the trust of those clients, they may also introduce several unique deals. This study employed agglomerative hierarchical clustering, Decision Trees, and Random Forest Classification techniques. The data with decision tree obtained an accuracy of 84%, the data with the Random Forest obtained an accuracy of 85% and the clustered data passed through the agglomerative hierarchical clustering obtained an accuracy of 98.3% using random forest classifier and an accuracy of 98.1 % using decision tree classifier

    Combining loan requests and investment offers

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    Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

    Managing credit risk and the cost of equity with machine learning techniques

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    Credit risks and the cost of equity can influence market participants' activities in many ways. Providing in-depth analysis can help participants reduce potential costs and make profitable strategies. This kind of study is usually armed with conventional statistical models built with researchers' knowledge. However, with the advancement of technology, a massive amount of financial data increasing in volume, subjectivity, and heterogeneity becomes challenging to process conventionally. Machine learning (ML) techniques have been utilised to handle this difficulty in real-life applications. This PhD thesis consists of three major empirical essays. We employ state-of-art machine learning techniques to predict peer-to-peer (P2P) lending default risk, P2P lending decisions, and Environmental, Social, Corporate Governance (ESG) effects on firms' cost of equity. In the era of financial technology, P2P lending has gained considerable attention among academics and market participants. In the first essay (Chapter 2), we investigate the determinants of P2P lending default prediction in relation to borrowers' characteristics and credit history. Applying machine learning techniques, we document substantial predictive ability compared with the benchmark logit model. Further, we find that the LightGBM has superior predictive power and outperforms all other models in all out-of-sample predictions. Finally, we offer insights into different levels of uncertainty in P2P loan groups and the value of machine learning in credit risk mitigation of P2P loan providers. Macroeconomic impact on funding decisions or lending standards reflects the risk-taking behaviour of market participants. It has been widely discussed by academics. But in the era of financial technology, it leaves a gap in the evidence of lending standards change in a FinTech nonbank financial organisation. The second essay (Chapter 3) aims to fill the gap by introducing loan-level and macroeconomic variables into the predictive models to estimate the P2P loan funding decision. Over 12 million empirical instances are under study while big data techniques, including text mining and five state-of-the-art approaches, are utilised. We note that macroeconomic condition affects individual risk-taking and reaching-for-yield behaviour. Finally, we offer insight into macroeconomic impact in terms of different levels of uncertainty in different P2P loan application groups. In the third essay (Chapter 4), we use up-to-date machine learning techniques to provide new evidence for the impact of ESG on the cost of equity. Using 15,229 firm-year observations from 51 different countries over the past 18 years, we document negative causal effects on the cost of equity. In addition, we uncover non-linear effects because the level of ESG effects on the equity cost decrease with the enhancements of ESG performance. Furthermore, we note the heterogeneity in ESG effects in different regions by breaking down our sample. Finally, we find that global crises change the sensitivity of the equity cost towards ESG, and the change varies in areas

    Understanding credit risk in Norwegian real estate crowdlending : Analysis of credit quality among Norwegian real estate crowdlending borrowers across FundingPartner, Kameo and Monio

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    The Norwegian crowdlending industry has grown rapidly in the last decade, resulting in the emergence of several platforms of notable sizes. Regulations are lagging, and government instances are discussing incorporating EU directives. This thesis aims to investigate risk differences in credit classifications across Norwegian crowdlending platforms. We identify risk factors and analyze potential differences in risk related to loans issued by FundingPartner, Kameo and Monio. We analyzed differences both for the platforms overall and within the credit classifications. The results provide an overview of differences in credit assessment that may benefit the decisions of both lenders and policymakers. The analysis is based on a manually assembled data set containing loan data, financial statements and policy rates. Our empirical analysis uses three bankruptcy models to evaluate borrowers' credit risk based on financial statements. The results from the bankruptcy models are tested to ensure significance. Moreover, we integrate project-specific risk elements such as collateral, loan size, loan term and interest rates to explain the differences we discovered. We also consider actual default rates and check if they are consistent with our empirical results. Despite having equal credit classification, we discovered significant differences between borrowers of such loans. FundingPartner issued A-classified loans with significantly riskier borrowers than Monio, despite Monio rewarding their lenders with higher interest rates. Borrowers of Monio are overall the least risky, yet the platform hosts the riskiest borrowers in our sample. Kameo borrowers with D-classified loans are significantly less risky than Monio's. Furthermore, we observe considerable differences in the use of collateral to secure lenders in the event of default. Lastly, we compare our empirical findings against confirmed defaults.nhhma

    Determinants of default in the bitcoin lending market. The case of Bitbond platform

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    This paper studies the bitcoin lending market and the factors explaining loans defaults. No financial intermediation implies that investors are faced directly with the credit risk. This increases information asymmetry at the cost of the lenders, so bitcoin lending platforms try to reduce this negative effect by providing information about the borrowers and their loan requests. Credit grade and interest rate are assigned by the platform, which are the main variables of the interest. This study has been conducted on the largest active bitcoin lending platform Bitbond covering 2013-2017 period with overall (N=1449) loans outstanding. Correlation analysis and univariate means tests have been used to analyse the data, while logistic regressions have been used for predicting default. Factors explaining default are loan amount, loan term and purpose of working capital, as well as industry of education and transportation and the total number of identifications. The interest rate assigned is the most predictive factor of the default followed by the grade, though other additional variables still improve the accuracy of the models. This paper contributes to the current literature since it is the first, to the best of our knowledge, analysing the bitcoin lending market

    Digital Financial Markets and (Europe's) Private Law – A Case for Regulatory Competition?

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    I call BS: Fraud Detection in Crowdfunding Campaigns

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    Donations to charity-based crowdfunding environments have been on the rise in the last few years. Unsurprisingly, deception and fraud in such platforms have also increased, but have not been thoroughly studied to understand what characteristics can expose such behavior and allow its automatic detection and blocking. Indeed, crowdfunding platforms are the only ones typically performing oversight for the campaigns launched in each service. However, they are not properly incentivized to combat fraud among users and the campaigns they launch: on the one hand, a platform's revenue is directly proportional to the number of transactions performed (since the platform charges a fixed amount per donation); on the other hand, if a platform is transparent with respect to how much fraud it has, it may discourage potential donors from participating. In this paper, we take the first step in studying fraud in crowdfunding campaigns. We analyze data collected from different crowdfunding platforms, and annotate 700 campaigns as fraud or not. We compute various textual and image-based features and study their distributions and how they associate with campaign fraud. Using these attributes, we build machine learning classifiers, and show that it is possible to automatically classify such fraudulent behavior with up to 90.14% accuracy and 96.01% AUC, only using features available from the campaign's description at the moment of publication (i.e., with no user or money activity), making our method applicable for real-time operation on a user browser

    Bankruptcy prediction model using cost-sensitive extreme gradient boosting in the context of imbalanced datasets

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    In the process of bankruptcy prediction models, a class imbalanced problem has occurred which limits the performance of the models. Most prior research addressed the problem by applying resampling methods such as the synthetic minority oversampling technique (SMOTE). However, resampling methods lead to other issues, e.g., increasing noisy data and training time during the process. To improve the bankruptcy prediction model, we propose cost-sensitive extreme gradient boosting (CS-XGB) to address the class imbalanced problem without requiring any resampling method. The proposed method’s effectiveness is evaluated on six real-world datasets, i.e., the LendingClub, and five Polish companies’ bankruptcy. This research compares the performance of CS-XGB with other ensemble methods, including SMOTE-XGB which applies SMOTE to the training set before the learning process. The experimental results show that i) based on LendingClub, the CS-XGB improves the performance of XGBoost and SMOTE-XGB by more than 50% and 33% on bankruptcy detection rate (BDR) and geometric mean (GM), respectively, and ii) the CS-XGB model outperforms random forest (RF), Bagging, AdaBoost, XGBoost, and SMOTE-XGB in terms of BDR, GM, and the area under a receiver operating characteristic curve (AUC) based on the five Polish datasets. Besides, the CS-XGB model achieves good overall prediction results
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