Credit Dataset Research Articles

Credit Anomaly Detection Method based on Bayesian Networks

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.

Harnessing probabilistic neural network with triple tree seed algorithm-based smart enterprise quantitative risk management framework

Enterprise risk management (ERM) frameworks convey vital principles that help create a consistent risk management culture, irrespective of employee turnover or industry standards. Enterprise Management System (EMS) are becoming a popular research area for assuring a company’s long-term success. Statistical pattern recognition, federated learning, database administration, visualization technology, and social networking are all used in this field, which includes artificial intelligence (AI), data science, and statistics. Risk assessment in EMS is critical for enterprise decision-making to be effective. Recent advancements in AI, machine learning (ML), and deep learning (DL) concepts have enabled the development of effective risk assessment models for EMS. This special issue seeks groundbreaking research articles that showcase the application of applied probability and statistics to interdisciplinary studies. This study offers Improved Metaheuristics with a Deep Learning Enabled Risk Assessment Model (IMDLRA-SES) for Smart Enterprise Systems. Using feature selection (FS) and DL models, the provided IMDLRA-SES technique estimates business risks. Preprocessing is used in the IMDLRA-SES technique to change the original financial data into a usable format. In addition, an FS technique based on oppositional lion swarm optimization (OLSO) is utilized to find the best subset of features. In addition, the presence or absence of financial hazards in firms is classified using the triple tree seed algorithm (TTSA) with a probabilistic neural network (PNN) model. The TTSA is used as a hyperparameter optimizer to improve the efficiency of the PNN-based categorization. An extensive set of experimental evaluations is performed on German and Australian credit datasets to illustrate the IMDLRA-SES model’s improved performance. The performance validation of the IMDLRA-SES model portrayed a superior accuracy value of 95.70% and 96.09% over existing techniques.

Sequential three-way decision with automatic threshold learning for credit risk prediction

Machine learning algorithms treat credit risk prediction as a binary classification problem. However, two-way decisions with a single threshold force to make either a default or non-default decision may be inappropriate. To reduce the risk of decision errors, this study introduces three-way decisions and proposes a sequential three-way decision model with automatic threshold learning to evaluate credit risk. Initially, the model uses the loan amount and interest to determine the decision loss of the three-way decision, assigning distinct decision thresholds to different samples. Subsequently, the model employs decision cost and information gain to formulate an objective for threshold optimisation. Finally, the model continuously optimises the classification process by using the outcomes of certain decisions as supplementary information. In addition, to validate our model, we conduct comparative experiments with various methods on a real credit dataset from a Chinese bank. The results indicate that the model not only enhances classification performance across several metrics but also assists financial institutions in reducing decision error costs.

A novel framework for enhancing transparency in credit scoring: Leveraging Shapley values for interpretable credit scorecards.

Credit scorecards are essential tools for banks to assess the creditworthiness of loan applicants. While advanced machine learning models like XGBoost and random forest often outperform traditional logistic regression in predictive accuracy, their lack of interpretability hinders their adoption in practice. This study bridges the gap between research and practice by developing a novel framework for constructing interpretable credit scorecards using Shapley values. We apply this framework to two credit datasets, discretizing numerical variables and utilizing one-hot encoding to facilitate model development. Shapley values are then employed to derive credit scores for each predictor variable group in XGBoost, random forest, LightGBM, and CatBoost models. Our results demonstrate that this approach yields credit scorecards with interpretability comparable to logistic regression while maintaining superior predictive accuracy. This framework offers a practical and effective solution for credit practitioners seeking to leverage the power of advanced models without sacrificing transparency and regulatory compliance.

Research on credit risk assessment optimization based on machine learning

Credit business is a vital part of the bank's core business, which has an extremely important impact on the bank's income and development. In the operation of credit business, credit risk assessment is particularly crucial, and accurate risk assessment can minimize risks while maximizing the bank's returns. We propose a method to optimize credit risk assessment using machine learning techniques. In this work, we employ a random forest machine learning model to process and analyze large amounts of loan application data. By using correlation analysis, information enrichment, etc., the characteristics that have the most impact on credit risk assessment are screened. Subsequently, the model was constructed using a random forest algorithm. Random forests improve the generalization ability and accuracy of the model by building multiple decision trees and introducing randomness between these trees. In the experimental analysis part, we compare the performance of various models on the German credit dataset, and the results show that the deep learning model outperforms the traditional machine learning model in most indicators, verifying the effectiveness of our method.

Model for converting data into NoSQL data warehouse for developing a real-time financial data warehouse system

This research introduces a novel model, the Financial Data Warehouses API (FDW-API), developed using PHP, Node.js, and Express.js. The model is designed to transform banking credit dataset information into a data warehouse format using a Non-Only SQL (NoSQL) database, stored in JSON format. Three types of databases were employed: MongoDB Node, MongoDB Serverless, and Cassandra. The study includes a comparative analysis of the data retrieval speed from all three databases. The model's applicability was tested in a real-time credit approval web application, demonstrating its effectiveness in transforming and storing data. Testing involved loading datasets ranging from 200, 300, 400, 500, 600, 800, and 1000 entries. Results indicate that the MongoDB serverless database outperformed others in terms of efficiency. Additionally, the FDW-API model streamlines data transformation and storage, facilitating real-time analysis and decision-making for financial institutions and data-driven businesses. Its flexibility integrates seamlessly with existing systems, reducing development time and costs, while its scalability accommodates growing data volumes and evolving business needs, providing a valuable tool for strategic insights and competitive advantage.

Optimizing fairness and accuracy: a Pareto optimal approach for decision-making

AbstractIn the era of data-driven decision-making, ensuring fairness and equality in machine learning models has become increasingly crucial. Multiple fairness definitions have been brought forward to evaluate and mitigate unintended fairness-related harms in real-world applications, with little research on addressing their interactions with each other. This paper explores the application of a Minimax Pareto-optimized solution to optimize individual and group fairness at individual and group levels on the Adult Census Income dataset as well as on the German Credit dataset. The objective of training a classification model with a multi-objective loss function is to achieve fair outcomes without compromising utility objectives. We investigate the interplay of different fairness definitions, including definitions of performance consistency and traditional group and individual fairness measures, amongst each other coupled with performance. The results presented in this paper highlight the feasibility of incorporating several fairness considerations into machine learning models, which can be applied to use cases with multiple sensitive features and attributes that characterize real-world applications. This research is a valuable step toward building responsible and transparent machine learning systems that can be incorporated into critical decision-making processes.

Investigating the Effect of Class Balancing Methods on the Performance of Machine Learning Techniques: Credit Risk Application

Credit risk arises as a result of the failure of the loans given by banks to the customers to fulfill their obligations at the end of the specified term. Technological advances allow the use of machine learning methods in various sectors. These methods aim to facilitate the identification of customers at risk with the system adapted to the creditworthiness processes of banks. For this purpose, in order to make the most appropriate evaluation in the lending process of banks, re-sampling techniques to eliminate the problem of class imbalance encountered in unbalanced data sets were made balanced and their effects on machine learning were investigated. During the implementation phase, German, Australian and HMEQ credit data sets were used. Different machine learning classification methods such as Logistic Regression (LR), K-Narest Neighbor (KNN), Naive Bayes (NB), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Decision Trees (DT), Random Forests (RF), Gradient Boosting Decision Trees (GBDT), Extremely Randomized Trees, Hard and Soft Voting were used to detect risky customers. The problem of class imbalance was balanced with resampling and hybrid techniques such as Random Oversampling (ROS), Random Undersampling (RUS), Balanced Bagging Classifier (BBC), SMOTE-Tomek Links and SMOTE-ENN. In this context, the performances of three different data sets were examined in four different scenarios. As a result of the study, the hybrid method, in which oversampling and undersampling methods are used together for the class balancing problem, showed the best classification performance among machine learning techniques.

Credit card fraud detection using the brown bear optimization algorithm

Fraud detection in banking systems is crucial for financial stability, customer protection, reputation management, and regulatory compliance. Machine Learning (ML) is vital in improving data analysis, real-time fraud detection, and developing fraud techniques by learning from data and adjusting detection strategies accordingly. Feature Selection (FS) is essential for enhancing fraud detection through ML to achieve optimal model accuracy. This is because it helps to eliminate the negative impact of redundant and irrelevant attributes. To enhance the accuracy of the given dataset, the researchers utilized multiple methods to determine the most fitting features. However, it is important to note that when implementing these methods on datasets with larger feature sizes, they may encounter issues with local optimality. Despite this, the researchers continue to work on improving the effectiveness of these methods. This study presents an effective methodology based on the Brown-Bear Optimization (BBO) algorithm to enhance the capacity to accurately identify financial CCF transactions by recognizing pertinent features. BBO has balanced capabilities to reduce dimensionality while enhancing classification accuracy. It is improved by adjusting the positions randomly to enhance exploration and exploitation capabilities, and then it is cloned into a binary variant named Binary BBOA (BBBOA). The Support Vector Machine (SVM), k-nearest Neighbor (k-NN), and Xgb-tree are the ML classifiers used with the suggested methodology. On the Australian credit dataset, the proposed methodology is compared with the basic BBOA and ten current optimizers, such as Binary African Vultures Optimization (BAVO), Binary Salp Swarm Algorithm (BSSA), Binary Atom Search Optimization (BASO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Sailfish Optimizer (BSFO). Regarding Wilcoxon’s rank-sum test (α=0.05), the superiority and effective consequence of the presented methodology are clear on the utilized dataset and got an accuracy of classification up to 91% in the utilized dataset combined with an attribute reduction length down to 67%. The proposed methodology is further validated using 10 benchmark datasets and outperformed its competitors in most utilized datasets regarding different performance measures. In the end, the proposed methodology is further validated using ten benchmark datasets from the UCI repository. It outperformed its competitors in most of the utilized datasets regarding different performance measures.

Improved RBM‐based feature extraction for credit risk assessment with high dimensionality

AbstractTo address the high‐dimensional issues in credit risk assessment, an improved multilayer restricted Boltzmann machine (RBM) based feature extraction method is proposed. In the improved multilayer RBM methodology, the reconstruction error method is first applied to ensure the number of RBM layers to construct an optimal model and then the weighted pruning approach is used to remove redundant and irrelevant traits. For verification purposes, two real‐world credit datasets are employed to demonstrate the effectiveness of the proposed multilayer RBM methodology. The experimental results reveal that a significant improvement in credit classification performance can be obtained by the improved multilayer RBM methodology. This indicates the improved multilayer RBM model proposed in this paper can be used as a promising tool to solve the high‐dimensionality issues in credit risk evaluation.

Using Genetic Algorithm Feature Selection to Optimize XGBoost Performance in Australian Credit

To reduce credit risk in credit institutions, credit risk management practices need to be implemented so that lending institutions can survive in the long term. Data mining is one of the techniques used for credit risk management. Where data mining can find information patterns from big data using classification techniques with the resulting level of accuracy. This research aims to increase the accuracy of classification algorithms in predicting credit risk by applying genetic algorithms as the best feature selection method. Thus, the most important feature will be used to search for credit risk information. This research applies a classification method using the XGBoost classifier on the Australian credit dataset, then carries out an evaluation by measuring the level of accuracy and AUC. The results show an increase in accuracy of 2.24%, with an accuracy value of 89.93% after optimization using a genetic algorithm. So, through research on genetic algorithm feature selection, we can improve the accuracy performance of the XGBoost algorithm on the Australian credit dataset.

Transductive Semi-Supervised Metric Network for Reject Inference in Credit Scoring

Credit scoring is an essential technique for credit risk management in the financial industry. However, most credit scoring models face the challenge of reject inference, which refers to the lack of post-loan performance data for rejected applicants, leading to sample selection bias and inaccurate credit assessment. Traditional credit scoring methods tackle this issue by assuming that the missing labels for rejected samples are missing at random (MAR) and by measuring sample similarity directly in the original feature space. Nevertheless, these strategies are not suitable for real-world business scenarios. Inspired by metric learning and transductive learning, we propose a novel credit scoring model called transductive semi-supervised metric network (TSSMN), which formalizes reject inference as a semi-supervised binary classification problem with the prior assumption of missing not at random (MNAR). TSSMN consists of two interconnected modules: the embedding metric network (EMN) that maps samples from the original feature space to the metric space for similarity measurement, and the transductive propagation network (TPN) that performs label propagation based on sample similarity. We evaluate TSSMN on a real-world credit dataset and compare it with traditional credit scoring methods. The results indicate that TSSMN can overcome sample selection bias and more accurately classify credit applicants. Therefore, TSSMN has the potential to enhance credit risk assessment in real-world business scenarios.

Optimizing Ensemble Learning to Reduce Misclassification Costs in Credit Risk Scorecards

Credit risk scorecard models are utilized by lending institutions to optimize decisions on credit approvals. In recent years, ensemble learning has often been deployed to reduce misclassification costs in credit risk scorecards. In this paper, we compared the risk estimation of 26 widely used machine learning algorithms based on commonly used statistical metrics. The best-performing algorithms were then used for model selection in ensemble learning. For the first time, we proposed financial criteria that assess the impact of losses associated with both false positive and false negative predictions to identify optimal ensemble learning. The German Credit Dataset (GCD) is augmented with simulated financial information according to a hypothetical mortgage portfolio observed in UK, European and Australian banks to enable the assessment of losses arising from misclassification costs. The experimental results using the simulated GCD show that the best predictive individual algorithm with the accuracy of 0.87, Gini of 0.88 and Area Under the Receiver Operating Curve of 0.94 was the Generalized Additive Model (GAM). The ensemble learning method with the lowest misclassification cost was the combination of Random Forest (RF) and K-Nearest Neighbors (KNN), totaling USD 417 million in costs (USD 230 for default costs and USD 187 for opportunity costs) compared to the costs of the GAM (USD 487, USD 287 and USD 200). Implementing the proposed financial criteria has led to a significant USD 70 million reduction in misclassification costs derived from a small sample. Thus, the lending institutions’ profit would considerably rise as the number of submitted credit applications for approval increases.

Deep convolutional neural networks with genetic algorithm-based synthetic minority over-sampling technique for improved imbalanced data classification

Imbalanced data classification presents a challenge in machine learning, inducing biased model learning. Moreover, data dimensionality poses another challenge as it highly impacts classifier performance. This paper proposes a new deep-learning method that combines feature selection with oversampling to address these challenges. The proposed approach, GA-SMOTE-DCNN, integrates a genetic algorithm (GA) for feature selection, SMOTE for oversampling, and a deep 1D-convolutional neural network (DCNN) for classification. This study reveals that pre-splitting the data into training and testing sets before applying SMOTE results in higher accuracy, showing an improvement in accuracy ranging between 1.94% and 3.98% compared to post-SMOTE splitting for each dataset. This method achieved accuracy rates of 86.81% for the Balance Scale dataset, 86.15% for the Oil Spill dataset, 89.21% for the Yeast dataset, 91.32% for the Mammography dataset, 88.23% for the Australian credit dataset, and 89.53% for the German Credit dataset when compared with benchmark methods, underscoring its significance in tackling high-dimensional and imbalanced data classification problems. This method demonstrates scalability in effectively addressing challenges associated with high-dimensional and imbalanced data classification across various domains.

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

AbstractThe predictive and interpretable power of models is crucial for financial risk management. The purpose of this study was to perform credit risk prediction in a structured causal network with four stages—data processing, structural learning, parameter learning, and interpretation of inferences—and use six real credit datasets to conduct empirical research on the proposed model. Compared with traditional machine learning algorithms, we comprehensively explain the results of credit default through forward and reverse reasoning. We also compared our model with the post hoc interpretation models local interpretable model‐agnostic explanations (LIME) and shapley additive explanations (SHAP) to verify the interpretability of Bayesian networks. The experimental results show that the prediction performance of Bayesian networks is superior to traditional machine learning models and similar to the performance of ensemble models. Furthermore, Bayesian networks offer valuable insights into the interplay of features by considering their causal relationships and enable an assessment of how individual features influence the prediction outcome. In this study, what‐if analysis was performed to assess credit default probabilities under various conditions. This analysis provides decision‐makers with the necessary tools to make informed judgments about the risk profile of borrowers. Consequently, we consider Bayesian networks as a viable tool for credit risk prediction models in terms of prediction performance and interpretability.

On impurity functions in decision trees

Impurity functions are crucial in decision trees. These functions help determine the impurity level of a node in a decision tree, guiding the splitting criteria. However, two primary ambiguities have surrounded impurity functions: (1) the question of their non negativity and (2) the debate over their concavity. In this paper, we address these uncertainties by delving into the characteristics of impurity functions. We establish that the non negativity of an impurity function is inconsequential. Through counter examples, we disprove the equivalence between an impurity function and a concave function. We identify an impurity function that is not concave and a concave function that is not an impurity function. Interestingly, we find an impurity function that results in a negative impurity reduction. Furthermore, we validate several significant properties of impurity functions. For example, we demonstrate that when an impurity function is concave, the impurity reduction remains nonnegative for multiway divisions. We also discuss the sufficient conditions for a concave function to be an impurity function. Our numerical results further indicate that a positive linear combination of the two most popular impurity functions, namely Gini Index and Entropy, may surpass the individual performance of each when applied to the well-known German credit dataset.

Comparative Analysis of Machine Learning Models for Credit Scoring: A Case Study on the South German Credit Dataset

In this research, proposed a novel hybrid machine learning approach that combines the strengths of Random Forest, Multi-Layer Perceptron (MLP), and LightGBM algorithms for classification tasks. This research work focuses on evaluating the performance of this hybrid model using the South German Credit dataset obtained from Kaggle, comprising bank client data, client last contact information, and labels. With 45,211 records and 16 attributes, this dataset provides a suitable environment for assessing the effectiveness of our proposed approach. Employ various evaluation metrics including accuracy, sensitivity and specificity and Receiver Operating Characteristic (ROC) to comprehensively analyze the model's performance. Through experiments, aim to demonstrate the efficacy of the hybrid approach in accurately classifying instances and providing insights into its potential applications in real-world scenarios

Modeling of extended osprey optimization algorithm with Bayesian neural network: An application on Fintech to predict financial crisis

<abstract> <p>Accurately predicting and anticipating financial crises becomes of paramount importance in the rapidly evolving landscape of financial technology (Fintech). There is an increasing reliance on predictive modeling and advanced analytics techniques to predict possible crises and alleviate the effects of Fintech innovations reshaping traditional financial paradigms. Financial experts and academics are focusing more on financial risk prevention and control tools based on state-of-the-art technology such as machine learning (ML), big data, and neural networks (NN). Researchers aim to prioritize and identify the most informative variables for accurate prediction models by leveraging the abilities of deep learning and feature selection (FS) techniques. This combination of techniques allows the extraction of relationships and nuanced patterns from complex financial datasets, empowering predictive models to discern subtle signals indicative of potential crises. This study developed an extended osprey optimization algorithm with a Bayesian NN to predict financial crisis (EOOABNN-PFC) technique. The EOOABNN-PFC technique uses metaheuristics and the Bayesian model to predict the presence of a financial crisis. In preprocessing, the EOOABNN-PFC technique uses a min-max scalar to scale the input data into a valid format. Besides, the EOOABNN-PFC technique applies the EOOA-based feature subset selection approach to elect the optimal feature subset, and the prediction of the financial crisis is performed using the BNN classifier. Lastly, the optimal parameter selection of the BNN model is carried out using a multi-verse optimizer (MVO). The simulation process identified that the EOOABNN-PFC technique reaches superior accuracy outcomes of 95.00% and 95.87% compared with other existing approaches under the German Credit and Australian Credit datasets.</p> </abstract>

Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation

Accurate credit risk prediction effectively supports decision makings and risk prevention in quantitative management. The general paradigm of previous works usually conducts supervised classification with internal information (credit attributes) of instances, while recent studies have introduced external information like texts, images, relations, to improve predictive accuracy. However, how to improve forecasting without explicit external relations still needs to be explored. Motivated by this and also by the increasing popularity of Graph Neural Network (GNN) with its fast infiltration into other disciplines, we propose an integrated graph representation learning approach to realize improved credit risk prediction. It includes two stages: (i) treat instances as nodes and use kNN to extract and construct edges; (ii) implement GNN models to discriminate risk/default cases by node classification. In this way, both “unsupervised” graph transformation and “supervised” node classification have been integrated to formulate the hybrid kNN–GNN model, and experiments on widely-used credit datasets demonstrate its outperformance over direct classification by conventional machine learning techniques. Sensitivity of hyperparameter k indicating different graph sparsity is also analyzed to reveal its optimal selection. Furthermore, ensemble multi-graphs and introduce edge weights are examined to investigate possible advancements with some enhancements observed for both, providing feasible ways to extend the upper bound of this hybrid model’s performances. Our findings exhibit valid improvements in credit risk prediction under the circumstance of only internal information available, and in depth present the future prospects of innovative integrations and applications of GNN methods in dealing with many other operational research tasks.

Fintech vs bank credit: How do they react to monetary policy?

Fintech credit, which includes peer-to-peer and marketplace lending as well as lending facilitated by major technology firms, is witnessing rapid growth worldwide. However, its responsiveness to monetary policy shifts remains largely unexplored. This study employs a novel credit dataset spanning 19 countries from 2005 to 2020 and conducts a PVAR analysis to shed some light on the different reaction of fintech and bank credit to changes in policy rates. The main result is that fintech credit shows a lower (even non-significant) reactivity to monetary policy shocks in comparison to traditional bank credit. Given the still marginal – although fast growing – macroeconomic significance of fintech credit, its contribution in explaining the variability of real GDP is less than 2%, against around one quarter for bank credit.