Credit Risk Analysis in Peer-to-Peer Lending System | Semantic Scholar (2024)

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43 Citations

Improving Credit Risk Prediction in Online Peer-to-Peer (P2P) Lending Using Imbalanced Learning Techniques
    Luis Eduardo Boiko FerreiraJ. P. BarddalHeitor Murilo GomesF. Enembreck

    Computer Science, Economics

    2017 IEEE 29th International Conference on Tools…

  • 2017

This work wrangle a real-world P2P lending data set from Lending Club, containing a large amount of data gathered from 2007 up to 2016, and analysis how supervised classification models and techniques to handle class imbalance impact creditworthiness prediction rates shows that sampling techniques outperform ensembles and cost sensitive approaches.

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Analyzing Peer-to-Peer Lending Secondary Market: What Determines the Successful Trade of a Loan Note?
    Ajay ByanjankarJ. MezeiXiaolu Wang

    Computer Science, Business

    WorldCIST

  • 2020

Using data from a leading European P2P platform, machine learning algorithms are applied to build classification models that can predict the success of secondary market offers and it is found that random forests offer the best classification performance.

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Data-driven optimization of peer-to-peer lending portfolios based on the expected value framework
    Ajay ByanjankarJ. MezeiM. Heikkilä

    Economics, Business

    Intell. Syst. Account. Finance Manag.

  • 2021

The loan selection process in P2P lending is treated as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk, and using internal rate of return as the measure of return.

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Can Listing Information Indicate Borrower Credit Risk in Online Peer-to-Peer Lending?
    Yi LiuQuanli ZhouXu-Na ZhaoYudong Wang

    Economics, Business

    Emerging Markets Finance and Trade

  • 2018

ABSTRACT Effective assessment of borrower credit risk is the greatest challenge for peer-to-peer (P2P) lenders, especially in the Chinese market, where borrowers lack widely recognized credit scores.

Predicting Credit Risk in European P2P Lending: A Case Study of “Bondora” Using Supervised Machine Learning Techniques
    Sampurna MondalSahil K. ShahV. Kumbhar

    Computer Science, Business

    2023 4th IEEE Global Conference for Advancement…

  • 2023

The study evaluates the performance of cutting-edge machine learning models, including Random Forest, Logistic Regression, XGBoost, Regularized Logistic Regression, Decision Tree, and Naïve Bayes, in predicting credit risk within the “Bondora” platform, and XGBoost emerges as the most effective model.

Improving Investment Suggestions for Peer-to-Peer Lending via Integrating Credit Scoring into Profit Scoring
    Yan WangX. Ni

    Business, Computer Science

    ACM Southeast Regional Conference

  • 2020

A two-stage framework that incorporates the credit information into a profit scoring modeling that could identify more profitable loans and thereby provide better investment guidance to the investors compared to the existing one-stage profit scoring alone approach is proposed.

The Prediction Analysis of Peer-to-Peer Lending Platforms Default Risk Based on Comparative Models
    Haifeng GuoK. PengXiaolei XuShuai TaoZhanghua Wu

    Economics, Business

    Sci. Program.

  • 2020

It is found that the abnormal return tends to trigger default risk significantly, but the default risk can be minimized if a platform has positive recommendations from customers and more transparent information disclosure or is affiliated as the member of the National Internet Finance Association of China.

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Why segmentation matters: a Machine Learning approach for predicting loan defaults in the Peer-to-Peer (P2P) Financial Ecosystem
    Adamaria PerrottaGeorgios Bliatsios

    Computer Science, Economics

    Risk Management Magazine

  • 2021

This paper addresses the borrower's default prediction problem in the P2P financial ecosystem by using Logistic Regression coupled with Weight of Evidence encoding, and compares the results of the chosen LR approach against two other popular Machine Learning techniques: the k Nearest Neighbors (k-NN) and the Random Forest.

Lending Club Default Prediction using Naïve Bayes and Decision Tree
    Mogi Jordan Christ

    Computer Science, Business

    International Journal of Advanced Trends in…

  • 2019

The results in this research show that J48 and Naïve Bayes are both good in predicting the default in P2P lending sector.

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A Novel Default Risk Prediction and Feature Importance Analysis Technique for Marketplace Lending using Machine Learning
    Sana Hassan ImamS. HuhnLars HornuRolf Drechsler

    Computer Science, Business

    Credit and Capital Markets – Kredit und Kapital

  • 2023

This paper proposes a holistic data processing flow for the loan status classification of marketplace lending multivariate time series data by using the Bidirectional Long Short-Term Memory model (BiLSTM) to predict “non-default,’ “distressed,” and “default” loan status, which outperforms conventional techniques.

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This paper will use Machine Learning algorithms to classify and optimize peer lending risk and use this data to improve the quality of loans and reduce the likelihood of a borrower default.

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    Credit Risk Analysis in Peer-to-Peer Lending System | Semantic Scholar (2024)

    FAQs

    What is the credit risk in P2P lending? ›

    The risk involved with peer-to-peer lending is the risk of default by the borrower, i.e., the borrower doesn't pay the interest and the principal amount.

    What are the risks of investing in peer-to-peer lending? ›

    Losing money due to bad debts (credit risk). Losing money due to a P2P lending site going bust (platform risk). Losing money due to a solvent wind down (more platform risk). Losing money due to fraud or negligence.

    What are the red flags for P2P? ›

    Inconsistent Stories: If the reason for the transaction keeps changing or doesn't seem to add up, take that as a warning sign. Unusual Payment Requests: If someone asks for payment in the form of gift cards or through multiple small transactions, it's a significant red flag.

    How do you calculate credit risk analysis? ›

    Lenders look at a variety of factors in attempting to quantify credit risk. Three common measures are probability of default, loss given default, and exposure at default. Probability of default measures the likelihood that a borrower will be unable to make payments in a timely manner.

    What are the major risk in P2P process? ›

    Beware of the top 5 risks in organizations during the procure-to-pay process. These include human errors, poor processes, non-compliance, extra costs, and fraud. Take the necessary steps to tackle them. One of which is using a fraud prevention software solution like Trustpair.

    Does peer-to-peer lending work? ›

    And peer-to-peer lending platforms may be a good alternative to payday loans or credit cards for some people. Depending on your credit, you may qualify for a competitive interest rate. But people with lower credit scores will likely see higher interest rates — sometimes even higher than the average credit card APR.

    What are the three types of risk that lenders or investors face? ›

    Financial risk is the possibility of losing money on an investment or business venture. Some more common and distinct financial risks include credit risk, liquidity risk, and operational risk. Financial risk is a type of danger that can result in the loss of capital to interested parties.

    What does an investor hope to gain by participating in peer-to-peer lending? ›

    1 Lower costs and rates

    Borrowers can benefit from lower borrowing costs and more flexible repayment terms, while investors can earn higher returns and diversify their portfolios. Lower operational costs are fundamental to offering competitive interest rates in P2P lending.

    What is the average ROI for peer to peer lending? ›

    Lenders for P2P loans may be enticed by the high returns they can make compared to other investing options. Typical returns for P2P investors per year average at about 5 percent to 9 percent while some investors see 10 percent or more returns.

    Is P2P high risk? ›

    1. Is P2P lending high risk? Peer-to-peer lending offers potentially higher returns than traditional investments but comes with higher default risk. You loan money directly to individuals or businesses without the same security as a bank.

    How much can you make from peer to peer lending? ›

    P2P Lending Performance

    The key values here are my account value, which is $38,259.11 and my annualized return, which is 10.58%. This is the best return I've ever had in investing. As time goes on, I'm going to try and maintain this return by investing in notes that return higher than 10%.

    What are the 5 C's of credit risk analysis? ›

    Lenders also use these five Cs—character, capacity, capital, collateral, and conditions—to set your loan rates and loan terms.

    What are the 4 C's of credit analysis? ›

    The “4 Cs” of credit—capacity, collateral, covenants, and character—provide a useful framework for evaluating credit risk. Credit analysis focuses on an issuer's ability to generate cash flow.

    What are the 5 C's of credit? ›

    Called the five Cs of credit, they include capacity, capital, conditions, character, and collateral. There is no regulatory standard that requires the use of the five Cs of credit, but the majority of lenders review most of this information prior to allowing a borrower to take on debt.

    Is peer to peer investing a good idea? ›

    P2P lending can be riskier than traditional lending. That's because there's a higher risk of default, so lenders are more likely to lose money. In exchange for the additional risk, however, P2P lenders usually charge a higher interest rate, which can help offset the risk of losing money.

    What are the risks of impact investing? ›

    One of the key risks is that impact investments may not generate the intended social or environmental impact. Another risk is that financial returns may be lower than anticipated. There are a number of different types of impact investments.

    What is the downside risk in investing? ›

    Downside risk is the potential for your investments to lose value in the short term. History shows that stock and bond markets generate positive results over time, but certain events can cause markets or specific investments you hold to drop in value.

    Is there any risk to stock lending? ›

    With stock lending, there is a small risk that a borrower could go bankrupt — maybe the asset they borrow from you increases so much in price that they can't afford to buy it back and return it to you.

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