What does not kill us makes us stronger: the story of repetitive consumer loan applications.

We investigate borrower and lender behaviours when the borrower has experienced a sequence of failed loan applications. Our analysis is based on half a million observations from an established peer-to-peer (P2P) loan platform in China, from 2010 to 2018. We find that borrowers who have better credit scores and who accept to pay higher interest rates are likely to reapply for funds after experiencing an earlier failed attempt. However, women and applicants with more education are discouraged from re-applying compared to their male or less-educated counterparts, respectively. On the funding supply side, lenders strive to fund safe borrowers who have high credit ratings and high income, though not those who offer a high interest rate

Peer-to-Peer (P2P) Lending in Europe: Evaluating the Default Risk of Borrowers in the Context of Gender and Education

In recent years, the importance of social lending activities and their effects on consumers have been highlighted by the widespread use of peer-to-peer lending platforms and the global race in fintech. Our study focuses on factors that affect the likelihood that European borrowers on peer-to-peer lending platforms, which are currently based in Estonia, Finland, and Spain, will default on their loans. Starting with the publicly accessible Bondora database, we examine the different economic and social characteristics of the borrowers to analyze the factors that contributed to loan default between 2013 and 2021. We use a Logit model to calculate the ex-post probability of default for factors derived from Principal Component Analysis as well as the original variables supplied by the database. The results show how crucially important education is for borrowers in lowering the risk of default, along with loan characteristics like high debt levels, long loan terms, and high interest rates. In addition, gender plays an important role in determining loan default, with a particular focus on women's conditions within the family. Regarding financial inclusion and its social implications, our findings suggest different ways to improve financial literacy and promote peer-to-peer lending. Future research could develop on the findings by applying them to other lending platforms and countries

The Impact of Social Disclosures Within Fixed Rate Peer-to-Peer Lending Markets

Financial journals have just begun to examine the implications of unsecured fixed-rate loans between lenders and borrowers administered over the internet. This study observes 31,550 loans issued between June 2007 and April 2013 with a 36-month term, that are fully paid or charged off, based on a data set from the largest P2P lending website. Initial findings within peer-to-peer (P2P) lending markets have identified that social disclosures may influence these markets. The result of this analysis unambiguously confirms social disclosures influence lenders and the factors significant for funding a loan are inconsistent with the factors significant to repayment of the loan. Prescriptive filters based on social disclosures can improve the likelihood of selecting a creditworthy borrower and increase the models explanatory power. The study finds that distinct forms of social disclosure and specific content within social disclosures predict the amount of funding received and probability of loan repayment

Internet Financial Credit Risk Assessment with Sliding Window and Attention Mechanism LSTM Model

With the accelerated pace of market-oriented reform, Internet finance has gained a broad and healthy development environment. Existing studies lack consideration of time trends in financial risk, and treating all features equally may lead to inaccurate predictions. To address the above problems, we propose an LSTM model based on sliding window and attention mechanism. The model uses sliding windows to enable the model to effectively exploit the contextual relevance of loan data. And we introduce the attention mechanism into the model, which enables the model to focus on important information. The result on the Lending Club public desensitization dataset shows that our model outperforms ARIMA, SVM, ANN, LSTM, and GRU models

When all data is credit data: consumer credit markets, technological development, and distributive justice

This thesis examines how advances in predictive technology influence the distributional outcomes due to consumer credit markets, using ‘alternative’ consumer credit scoring as a case study. The thesis contributes the first scholarly analysis of the distributional effects due to alternative credit scoring in the UK, and the role of legal, technological, political, and market forces in shaping these effects. It also contributes to the deeper and broader analysis of the economic and social outcomes due to consumer credit markets, and the boundaries of ‘fair’ credit. Alternative credit scoring—the use of alternative data and machine learning techniques in consumer credit decisions—has been heralded with the implicitly distributional promise of improving access to credit for marginalized consumers, particularly lower-income consumers. Leveraging theoretical and empirical insights, the thesis argues that this promise is credible but strictly bounded. First, due to the limits of consumer credit, particularly unsecured credit, as a mechanism for reducing poverty and inequality. Second, due to the potential negative distributional effects of alternative credit scoring—whether resulting from more precise, data-driven price discrimination targeted at lower-income consumers, or the expansion of affordable credit to higher-income consumers. Further empirical investigation is needed to estimate the distributional outcomes due to alternative credit scoring, and advances in predictive credit technology more broadly, as well as the mechanisms producing these outcomes, particularly in the UK. To the extent that regressive outcomes are at least plausible, the thesis sketches the contours of policy interventions that could more effectively limit these outcomes and foster more progressive outcomes due to technological development in consumer credit markets

Weibull Racing Survival Analysis for Competing Events and a Study of Loan Payoff and Default

We propose Bayesian nonparametric Weibull delegate racing (WDR) to explicitly model surviving under competing events and to interpret how the covariates accelerate or decelerate the event times. WDR explains non-monotonic covariate effects by racing a potentially infinite number of sub-events, relaxing the ubiquitous proportional-hazards assumption which may be too restrictive. WDR can handle different types of censoring and missing event times or types. For inference, we develop a Gibbs-sampler-based MCMC algorithm along with a maximum a posteriori estimation for big data applications. We use synthetic data analysis to demonstrate the flexibility and parsimonious nonlinearity of WDR. We also use a data set of time to loan payoff and default from Prosper.com to showcase the interpretability.Comment: 40 pages, 7 figures, 14 table

Primjena ansambl metoda, logističke regresije i neuronske mreže na mogućnost predviđanja Peer-to-Peer pozajmljivanja

Credit scoring has become an important issue because competition among financial institutions is intense and even a small improvement in predictive accuracy can result in significant savings. Financial institutions are looking for optimal strategies using credit scoring models. Therefore, credit scoring tools are extensively studied. As a result, various parametric statistical methods, non-parametric statistical tools and soft computing approaches have been developed to improve the accuracy of credit scoring models. In this paper, different approaches are used to classify customers into those who repay the loan and those who default on a loan. The purpose of this study is to investigate the performance of two credit scoring techniques, the logistic regression model estimated on categorized variables modified with the use of WOE (Weight of Evidence) transformation, and neural networks. We also combine multiple classifiers and test whether ensemble learning has better performance. To evaluate the feasibility and effectiveness of these methods, the analysis is performed on Lending Club data. In addition, we investigate Peer-to-peer lending, also called social lending. From the results, it can be concluded that the logistic regression model can provide better performance than neural networks. The proposed ensemble model (a combination of logistic regression and neural network by averaging the probabilities obtained from both models) has higher AUC, Gini coefficient and Kolmogorov-Smirnov statistics compared to other models. Therefore, we can conclude that the ensemble model allows to successfully reduce the potential risks of losses due to misclassification costs.Procjena kreditne sposobnosti postaje izuzetno važna s obzirom na sve intenzivniju konkurenciju među financijskim institucijama tako da čak i neznatno unapređivanje točnosti predviđanja može rezultirati značajnom uštedom. Financijske institucije traže optimalne strategije pomoću modela procjene kreditne sposobnosti. Stoga je proučavanje alata za procjenu kreditne sposobnosti široko rasprostranjeno. Kao rezultat toga, razvijene su različite parametarske statističke metode, ne-parametarski statistički alati i pristupi programskom računanju kako bi se povećala točnost modela procjene kreditne sposobnosti. U ovom radu primjenjuju se različiti pristupi za klasifikaciju kupaca, kao onih koji vraćaju zajam i onih koji ne mogu podmirivati svoje obveze. Svrha ove studije je istražiti uspješnost dviju tehnika vrednovanja kreditne sposobnosti, modela logističke regresije, procijenjene na temelju kategorizirane varijable modificirane pomoću WOE (Weight of Evidence) transformacije, i neuronskih mreža. Nadalje, istražuje se da li kombiniranje više klasifikatora i testiranje prikupljenih informacija ansambl metodom doprinosi boljim rezultatima. Da bi se procijenila izvedivost i učinkovitost ovih metoda, provodi se analiza podataka Lending Cluba. Istražuje se P2P pozajmljivanje, odnosno uzajamno pozajmljivanje bez posredovanja financijskih institucija, koje se još naziva i socijalno pozajmljivanje. Na temelju provedenog istraživanja, može se zaključiti da model logističke regresije daje bolje rezultate od neuronskih mreža. Izgleda da je predloženi ansambl model (kombinirajući logističku regresiju i neuronsku mrežu s prosjekom vjerojatnosti dobivenih iz oba modela) imao veću AUC krivulju, Gini koeficijent i Kolmogorov-Smirnov test veću statističku vrijednost u usporedbi s drugim modelima. Stoga možemo zaključiti da ansambl model omogućuje uspješno reduciranje mogućih rizika od gubitaka koji nastaju uslijed pogrešne klasifikacije troškova

P2P Mapper: From User Experiences to Pattern-Based Design

User experience is an umbrella term referring to a collection of information that covers the user’s behavior and interaction with a system. It is observed when the user is actively using a service or interacting with information, includes expectations and perceptions, and is influenced by user characteristics and application or service characteristics. User characteristics include knowledge, experience, personality and demographics. We propose a process and supporting software tool called Persona to Pattern (P2P) Mapper, which guides designers in modeling user experiences and identifying appropriate design patterns. The three-step process is: Persona Creation (a representative persona set is developed), Pattern Selection (behavioral patterns are identified resulting in an ordered list of design patterns for each persona), and Pattern Composition (patterns are used to create a conceptual design). The tool supports the first two steps of the process by providing various automation algorithms for user grouping and pattern selection combined with the benefit of rapid pattern and user information access. Persona and pattern formats are augmented with a set of discrete domain variables to facilitate automation and provide an alternative view on the information. Finally, the P2P Mapper is used in the redesign of two different Bioinformatics applications: a popular website and a visualization tool. The results of the studies demonstrate a significant improvement in the system usability of both applications