Default Prediction Research Articles

Machine Learning-Based Loan Default Prediction: Models, Insights, And Performance Evaluation In Peer-To-Peer Lending Platforms

Machine Learning-Based Loan Default Prediction: Models, Insights, And Performance Evaluation In Peer-To-Peer Lending Platforms

Leveraging network topology for credit risk assessment in P2P lending: A comparative study under the lens of machine learning

Peer-to-Peer (P2P) lending markets have witnessed remarkable growth, revolutionizing the way borrowers and lenders interact. Despite the increasing popularity of P2P lending, it poses significant challenges related to credit risk assessment and default prediction with meaningful implications for financial stability. Traditional credit risk models have been widely employed in the field of P2P lending; however, they may not be capable to capture latent factor information inherent to a loan network based on similarity distances. Thus, in this study we propose an enhanced two-step modeling approach for Machine Learning (ML) that utilizes insights from network analysis and subsequently combines derived network centrality metrics with traditional credit risk factors to improve the prediction accuracy in the credit default prediction process. Through a comparative analysis of three classical ML models with varying degrees of complexity, namely Elastic Net (EN), Random Forest (RF), and Multi-Layer Perceptron (MLP), we showcase novel evidence that the systematic inclusion of network topology features in the credit scoring process can significantly improve the prediction accuracy of the scoring models. Additional robustness tests via the inclusion of randomly shuffled centrality metrics in the analysis, and a further comparison of the graph-based models against a pertinent state-of-the-art credit scoring model in form of XGBoost, further confirm our results. The insights from this study bear valuable conclusions for P2P lending platforms to further improve their scoring systems with graph-enhanced metrics, thereby reducing default risk and facilitating greater access to credit.

Machine Learning-Based Loan Default Prediction in Peer-to-Peer Lending

The peer-to-peer (P2P) lending market has recently undergone significant growth, transforming traditional lending practices. However, this evolution brings with it unique challenges, particularly in managing credit risk and ensuring the reliability of loan approvals. Accurate prediction of loan defaults remains a pivotal aspect of risk management in this sector. This study introduces a comprehensive approach to improve bad loan prediction in peer-to-peer (P2P) lending, sourcing Lending Club data. In the face of challenges posed by imbalanced datasets and risk management in the loan industry, our methodology significantly enhances prediction accuracy, particularly in identifying bad loans. The study implements a comprehensive process that includes data cleaning, feature engineering, feature selection, balancing the dataset and machine learning models, achieving a noteworthy accuracy rate over 92% and recall rate above 87%. This research advances academic understanding of loan prediction and generates real-world impact. The accurate models identifying explanatory provide a valuable framework for improving decision-making and strategic planning in P2P lending platforms.

Default prediction modeling (DPM) with machine learning algorithms: case of non-financial listed companies in Pakistan

Default prediction modeling (DPM) with machine learning algorithms: case of non-financial listed companies in Pakistan

Understanding Corporate Bond Defaults in Korea Using Machine Learning Models*

AbstractWe investigate corporate bond defaults from 1995 to 2020 using hand‐collected data from hard‐copy publications in Korea. Using an under‐sampling method, we construct default prediction models based on machine learning models as well as a logistic model. The empirical results show that the random forest model outperforms the others. However, regardless of the models used, model performance in financial crisis periods is significantly worse than it is in non‐crisis periods. This finding suggests the need for additional information to improve model performance during crises when the default prediction is the most relevant. Furthermore, the dominant predictor of defaults before the global financial crisis was the debt ratio, while subsequently, the coverage ratio has become the most important predictor.

MACHINE LEARNING-BASED APPROACHES FOR CREDIT CARD DEBT PREDICTION

The primary concern in the stock market and banks that offer credit cards has been a problem over time. Regardless of their capacity to pay, most card users abuse their credit cards and accrue debt from cash cards. The most significant issue facing cardholders and banks alike is this calamity. Predicting credit card customers' default payments became vital to lowering this risk. Data mining approaches, including decision tree, logistic regression, and Naïve Bayes with feature selection methods, were applied to secondary credit card debt data to identify the significant factors that impact credit card default and to enhance the prediction of credit card default. As a result, the decision tree with Gini index splitting criteria forward selection wrapper method was identified as the best model with the highest percentages of accuracy, precision, sensitivity, and area under ROC of 76.39%, 72.02%, 85.08%, and 0.891 respectively. Additionally, the significant factors that impact credit card default are gender, education level, repayment status in July 2005, repayment status in August 2005, status of repayment in September 2005, and the amount paid in June 2005 and May 2005. This study may help financial institutions assess creditworthiness and give consumers insights into their financial behaviors.

309 Effects of SARS-CoV-2 Variants on CD8+ T cell Epitope Diversity: Estimating Clinical Severity in the United States

OBJECTIVES/GOALS: Our goal was to distinguish SARS-CoV-2 CD8+ T cell epitopes of spike, membrane, and nucleocapsid products in 27 of the most frequent HLA-Aand -B alleles.We hypothesize that differences mediated by variation in SARS-CoV-2 and host HLA genetics affect the differential clinical severity and presentation of acute infection and PASC. METHODS/STUDY POPULATION: Genomic sequences of SARS-CoV-2 variants were blasted against the original Wuhan strain using Ensembl’s SARS-CoV-2 browser. We examined 16 COVID variants: 2 Alpha (B.1 and B.1.1.7), 5 Delta (AY.100, AY.25, AY.3, AY.3.1, and AY.44), and 9 Omicron (BA.1, BA.1.1, BA.2, BA.4, BA.5, BQ.1, BQ.1.1, XBB.1, and XBB.1.5), sequenced from the Louisiana patient population. cDNA sequences were translated using the Expasy tool. To predict MHC-I epitope binding, we used the Immune Epitope Database and Analysis Resource, via TepiTool utilizing the IEDB recommended default prediction and the 27 most frequent HLA-A and -B alleles. In silico peptide docking was conducted on FoldX, utilizing HLA-B*15:01 structures (n= 7) from the Protein Data Bank. RESULTS/ANTICIPATED RESULTS: CD8+ epitope conservation was estimated at 87.6-96.5% in S, 92.5-99.6% in M, and 94.6-99% for N. As the virus mutated, an increasing proportion of S epitopes experienced reduced predicted binding affinity: 70% of Omicron BQ.1- XBB.1.5 S epitopes experienced decreased predicted binding, as compared to ~ 3% and ~15% in Delta AY.100-AY.44 and Omicron BA.1-BA.5 respectively. Additionally, we identified several novel candidate haplotypes that may be susceptible to severe disease, notably HLA-A*32:01, -A*26:01, -B*58:01, and -B*53:01, and relatively protected from disease, such as -A*01:01, -A*02:01,-A*31:01, -B*15:01, -B*40:01, -B*44:03, and -B*57:01. In silico analysis of COVID peptides and HLA-B*15:01, a common allotype in the United States, largely matched predicted binding patterns. DISCUSSION/SIGNIFICANCE: To elicit long term COVID-19 immunity and prevent PASC, it is important to understand the relationship between T-cells, viral variants, and HLA genetics. This project is one of the first to explore the interaction between CD8+ epitope diversity and viral genetics for the majority of the United States population.

Correction: Maximizing the lender’s profit: profit-oriented loan default prediction based on a weighting model

Correction: Maximizing the lender’s profit: profit-oriented loan default prediction based on a weighting model

Credit Risk Prediction Using Explainable AI

Despite advancements in machine-learning prediction techniques, the majority of lenders continue to rely on conventional methods for predicting credit defaults, largely due to their lack of transparency and explainability. This reluctance to embrace newer approaches persists as there is a compelling need for credit default prediction models to be explainable. This study introduces credit default prediction models employing several tree-based ensemble methods, with the most effective model, XGBoost, being further utilized to enhance explainability. We implement SHapley Additive exPlanations (SHAP) in ML-based credit scoring models using data from the US-based P2P Lending Platform, Lending Club. Detailed discussions on the results, along with explanations using SHAP values, are also provided. The model explainability generated by Shapely values enables its applicability to a broad spectrum of industry applications.

Maximizing the lender’s profit: profit-oriented loan default prediction based on a weighting model

Maximizing the lender’s profit: profit-oriented loan default prediction based on a weighting model

Predicting Loan Default: A Comparative Analysis of Multiple Machine Learning Models

Financial decision-making, particularly in loan approval, requires precise risk prediction. To enhance the prediction accuracy, this study utilizes various machine learning models, namely Logistic Regression, XGBoost, an Artificial Neural Network (ANN), and a hybrid XGBoost + Logistic Regression (XGB+LR). These models were selected based on their unique capacities to capture complex patterns and relationships within the data, thereby potentially improving the loan default prediction task. The training and validation of these models were performed on a meticulously prepared dataset, following crucial preprocessing steps such as one-hot encoding, feature selection, and scaling. To ensure the models' optimal performance, intensive hyperparameter tuning was conducted. The application of these techniques resulted in a robust set of models. Each model's performance was rigorously evaluated through established metrics, including the Area Under the ROC Curve (AUC) and Accuracy (ACC). Among these models, the XGBoost model demonstrated superior predictive power, achieving an AUC of 0.798 and an ACC of 0.861 on the validation set. A detailed feature importance analysis using the XGBoost model further revealed that Credit_Score and Loan_Amount were the primary factors impacting loan approval decisions. Despite slight overfitting observed in the models, the results confirm the potential of machine learning in improving financial decision-making processes. This study sets the foundation for future advancements, which may include the application of advanced regularization techniques, further hyperparameter optimization, and the inclusion of a broader feature set.

Firm failure prediction for small and medium-sized enterprises and new ventures

AbstractNew ventures and small and medium-sized enterprises (SMEs) are the engines that drive the development of the economy, productivity, and business. However, they differ with respect to their natures, and that may affect their choices and success. This paper investigates the determinants of failure for SMEs and new ventures in Portugal by employing a logistic regression technique to develop the one-year prediction models individually over the period from 2010 to 2018. The results show that age and size always play significant roles in discriminating the failure risk of both types of firm, but the financial predictors selected in the final default prediction models for SMEs and new ventures vary. Moreover, based on financial, age, and size predictors, the SME model performs much better than that of the new venture in the classification accuracy reported. This indicates that separate treatment should be carried out while predicting the failure likelihood of SMEs and new ventures.

Leveraging Machine Learning Algorithm to Enable Access to Credit for Small Businesses in the United States of America

The research centers on the essential function of loans in driving economic expansion and the intrinsic danger of loan defaults. Small businesses play a crucial role in generating employment and fostering economic growth, highlighting the significance of precise loan eligibility evaluations. The Small Business Administration's (SBA) 7(a) loan program seeks to assist small businesses by providing loan guarantees to mitigate risks for financial institutions (Mini et al., 2018). The study utilizes sophisticated machine learning methods, particularly the Random Forest and XGBoost algorithms, to forecast loan defaults and ascertain ideal loan amounts for small businesses. A dataset comprising 27 variables is analyzed, encompassing loan attributes, borrower details, and loan outcomes. The results highlight the effectiveness of both Random Forest and XGBoost in generating loan default predictions, with a slight edge for XGBoost. Additionally, Linear Regression is used to estimate loan amounts for qualified borrowers. The analysis identifies key factors contributing to loan defaults, with variables such as Term, Disbursement Gross, SBA Approval, and Gross Approval playing a significant role in Random Forest predictions. The study reveals intriguing patterns in loan defaults across various industrial sectors, emphasizing the complex nature of assessing loan performance within these industries (Zhou et al., 2023). This research aims to improve lending practices, benefiting both lenders and borrowers while enhancing our understanding of risk management in the context of small businesses.

Tourism SME default: A note on predictors

This note addresses the lack of progress on small and medium enterprises (SMEs) default prediction in the tourism sector. Using the case of Croatian tourism SMEs, this note applies Altman's recently introduced Omega Score model to (i) predict SME default, (ii) explore and contrast important variables for default prediction in tourism vs. other sectors, and (iii) adapt the original Omega Score to tourism sector specifics. Financial ratios have historically been prominent among the predictors in scholarly investigations. However, our findings underscore the significance of supplementing these ratios with other predictors of tourism SME default, specifically with information on (i) creditor payment behaviour and (ii) employee characteristics and relationships with them. Furthermore, our research demonstrates that a refined selection of six variables, accompanied by adjusted weightings, can significantly enhance default prediction accuracy. The results provide valuable lessons for the strategic management of tourism firms.

Inter-industry network and credit risk

As previous literature has documented cross-industry returns and tail risk predictability, especially during a financial crisis, this research investigates the effects of industries' position within an economy, inter-industry connectedness, and industry returns on credit risk using a reduced-form approach. We employ an aggregate measure of tail risk emitted from an industry to capture its outgoing connectedness with other industries, and our empirical results show that the outgoing connectedness of some highly central industries positively impacts all sample firms’ default probabilities, controlling for a variety of firm-specific and macroeconomic variables that are well-known to be related to corporate defaults. In sum, our empirical results support that industry network risk helps explain corporate default and improves default prediction accuracy.

Retraction Note: Application of artificial intelligence technology in financial data inspection and manufacturing bond default prediction in small and medium-sized enterprises (SMEs)

Retraction Note: Application of artificial intelligence technology in financial data inspection and manufacturing bond default prediction in small and medium-sized enterprises (SMEs)

A spatial–temporal graph-based AI model for truck loan default prediction using large-scale GPS trajectory data

With the increasing uncertainties in freight transportation, truck loans are playing a crucial role in the stability and development of the logistics industry. A pivotal problem to truck loan management is controlling credit risk. Much research has focused on pre-loan default prediction considering applicants’ static personal information. However, few studies concentrate on post-loan risk control, which needs to track customers’ dynamic behaviors to ensure the loans can be recovered. We propose a Spatial–temporal Graph-based AI model for Truck loan Default prediction (SGTD) in post-loan management, which considers spatial and temporal dependencies of customers’ dynamic behaviors using large-scale GPS trajectory data. (1) The spatial dependency is caused by the functional similarity of the trajectories, which is only relevant to people’s travel habits and purposes. Therefore, we detect stay points from the nationwide trajectories and represent them with point-of-interest (POI) information as POI nodes. Then, we learn the latent vectors of POI nodes in a heterogeneous network through metapath learning, which are utilized to initialize vectors in modeling the temporal dependencies. (2) To learn temporal dependencies, we split the long trajectory into period sequences according to the cyclical nature of truck loans. Then we model each sequence with graph neural networks to capture the short-term closeness and period dependencies. Finally, we introduce LSTM networks to learn the long-term trend dependency and predict the default probability. The outcomes of extensive experiments on a real-world dataset demonstrate the effectiveness of SGTD in AUC, KS, accuracy, and F1-score, as well as the practical economic contribution. The results also implicate that GPS data can contribute to learning drivers’ socioeconomic information, and the proposed method is applicable to other transportation scenarios.

Corporate default prediction with payment disturbances in managers’ earlier entrepreneurial practices

Corporate default prediction with payment disturbances in managers’ earlier entrepreneurial practices

Accuracy Comparison between Five Machine Learning Algorithms for Financial Risk Evaluation

An accurate prediction of loan default is crucial in credit risk evaluation. A slight deviation from true accuracy can often cause financial losses to lending institutes. This study describes the non-parametric approach that compares five different machine learning classifiers combined with a focus on sufficiently large datasets. It presents the findings on various standard performance measures such as accuracy, precision, recall and F1 scores in addition to Receiver Operating Curve-Area Under Curve (ROC-AUC). In this study, various data pre-processing techniques including normalization and standardization, imputation of missing values and the handling of imbalanced data using SMOTE will be discussed and implemented. Also, the study examines the use of hyper-parameters in various classifiers. During the model construction phase, various pipelines feed data to the five machine learning classifiers, and the performance results obtained from the five machine learning classifiers are based on sampling with SMOTE or hyper-parameters versus without SMOTE and hyper-parameters. Each classifier is compared to another in terms of accuracy during training and prediction phase based on out-of-sample data. The 2 data sets used for this experiment contain 1000 and 30,000 observations, respectively, of which the training/testing ratio is 80:20. The comparative results show that random forest outperforms the other four classifiers both in training and actual prediction.

Design and implementation of a loan default prediction system using random forest algorithm

Loan default prediction is a crucial task in the lending industry; it helps financial institutions make informed decisions about granting loans. It is usually a daunting task for the bank or financial institution to predict customers who will default on a loan especially when there are thousands of applicants. This loan default prediction system aimed to improve the Area Under the Curve (AUC) score. This loan default prediction system used various data sources, such as demographic information, credit history, and financial performance to predict the likelihood of a loan being defaulted. The system used a random forest (RF) machine learning algorithm to analyze the data and build predictive models. The model was then used to make predictions about new loan applicants and existing borrowers who may default in the future. The system can be customized to meet the specific requirements of different lending institutions. The system enables lenders to make better decisions on loan approval, interest rate determination, and credit risk, management. The loan default prediction system also provides insights into risk factors that contribute to loan default and helps lenders develop effective strategies to mitigate these risks, making it an indispensable tool for lenders. The resultant system achieved an improved AUC score of 98%.