898 research outputs found
Network based scoring models to improve credit risk management in peer to peer lending platforms
Financial intermediation has changed extensively over the course of the last two decades. One of the most significant change has been the emergence of FinTech. In the context of credit services, fintech peer to peer lenders have introduced many opportunities, among which improved speed, better customer experience, and reduced costs. However, peer-to-peer lending platforms lead to higher risks, among which higher credit risk: not owned by the lenders, and systemic risks: due to the high interconnectedness among borrowers generated by the platform. This calls for new and more accurate credit risk models to protect consumers and preserve financial stability. In this paper we propose to enhance credit risk accuracy of peer-to-peer platforms by leveraging topological information embedded into similarity networks, derived from borrowers' financial information. Topological coefficients describing borrowers' importance and community structures are employed as additional explanatory variables, leading to an improved predictive performance of credit scoring models
Credit risk prediction in an imbalanced social lending environment
© 2018, the Authors. Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets
Credit Risk Evaluation in Peer-to-peer Lending With Linguistic Data Transformation and Supervised Learning
The widespread availability of various peer-to-peer lending solutions is rapidly changing the landscape of ï¬nancial services. Beside the natural advantages over traditional services,a relevant problem in the domain is to correctly assess the risk associated with borrowers. In contrast to traditional ï¬nancial services industries, in peer-to-peer lending the unsecured nature of loans as well as the relative novelty of the platforms make the assessment of risk a difï¬cult problem. In this article we propose to use traditional machine learning methods enhanced with fuzzy set theory based transformation of data to improve the quality of identifying loans with high likelihood of default. We assess the proposed approach on a real-life dataset from one of the largest peer-to-peer platforms in Europe. The results demonstrate that (i) traditional classiï¬cation algorithms show good performance in classifying borrowers, and (ii) their performance can be improved using linguistic data transformatio
Handling Uncertainty in Social Lending Credit Risk Prediction with a Choquet Fuzzy Integral Model
As one of the main business models in the financial technology field,
peer-to-peer (P2P) lending has disrupted traditional financial services by
providing an online platform for lending money that has remarkably reduced
financial costs. However, the inherent uncertainty in P2P loans can result in
huge financial losses for P2P platforms. Therefore, accurate risk prediction is
critical to the success of P2P lending platforms. Indeed, even a small
improvement in credit risk prediction would be of benefit to P2P lending
platforms. This paper proposes an innovative credit risk prediction framework
that fuses base classifiers based on a Choquet fuzzy integral. Choquet integral
fusion improves creditworthiness evaluations by synthesizing the prediction
results of multiple classifiers and finding the largest consistency between
outcomes among conflicting and consistent results. The proposed model was
validated through experimental analysis on a real- world dataset from a
well-known P2P lending marketplace. The empirical results indicate that the
combination of multiple classifiers based on fuzzy Choquet integrals
outperforms the best base classifiers used in credit risk prediction to date.
In addition, the proposed methodology is superior to some conventional
combination techniques
Loan Default Prediction: A Complete Revision of LendingClub
Predicción del default: Una revisión completa de LendingClub El objetivo del estudio es determinar un modelo de predicción de default crediticio usando la base de datos de LendingClub. La metodología consiste en estimar las variables que influyen en el proceso de predicción de préstamos pagados y no pagados utilizando el algoritmo Random Forest. El algoritmo define los factores con mayor influencia sobre el pago o el impago, generando un modelo reducido a nueve predictores relacionados con el historial crediticio del prestatario y el historial de pagos dentro de la plataforma. La medición del desempeño del modelo genera un resultado F1 Macro Score con una precisión mayor al 90% de la muestra de evaluación. Las contribuciones de este estudio incluyen, el haber utilizado la base de datos completa de toda la operación de LendingClub disponible, para obtener variables trascendentales para la tarea de clasificación y predicción, que pueden ser útiles para estimar la morosidad en el mercado de préstamos de persona a persona. Podemos sacar dos conclusiones importantes, primero confirmamos la capacidad del algoritmo Random Forest para predecir problemas de clasificación binaria en base a métricas de rendimiento obtenidas y segundo, denotamos la influencia de las variables tradicionales de puntuación de crédito en los problemas de predicción por defecto.The study aims to determine a credit default prediction model using data from LendingClub. The model estimates the effect of the influential variables on the prediction process of paid and unpaid loans. We implemented the random forest algorithm to identify the variables with the most significant influence on payment or default, addressing nine predictors related to the borrower's credit and payment background. Results confirm that the model’s performance generates a F1 Macro Score that accomplishes 90% in accuracy for the evaluation sample. Contributions of this study include using the complete dataset of the entire operation of LendingClub available, to obtain transcendental variables for the classification and prediction task, which can be helpful to estimate the default in the person-to-person loan market. We can draw two important conclusions, first we confirm the Random Forest algorithm's capacity to predict binary classification problems based on performance metrics obtained and second, we denote the influence of traditional credit scoring variables on default prediction problems
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
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
Credit Risk Prediction for Peer-To-Peer Lending Platforms: An Explainable Machine Learning Approach
Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules
and conditions set by banks and financial institutions. The plight yields to the growth in popularity of online
peer-to-peer lending platforms which are an easier way to obtain loan as they have fewer rigid rules.
However, high flexibility of loan funding in peer-to-peer lending comes with high default probability of loan
funded to high-risk start-ups. An efficient model for evaluating credit risk of borrowers in peer-to-peer lending
platforms is important to encourage investors to fund loans and justify the rejection of unsuccessful
applications to satisfy financial regulators and increase transparency. This paper presents a supervised
machine learning model with logistic regression to address this issue and predicts the probability of default
of a loan funded to borrowers through peer-to-peer lending platforms. In addition, factors that affect the credit
levels of borrowers are identified and discussed. The research shows that the most important features that
affect probability of default are debt-to-income ratio, number of mortgage account, and Fair, Isaac and
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