Hybrid Credit Research Articles

A Hybrid Credit Risk Evaluation Model Based on Three-Way Decisions and Stacking Ensemble Approach

A Hybrid Credit Risk Evaluation Model Based on Three-Way Decisions and Stacking Ensemble Approach

Juxtaposing Gender Differentials in Credit Assessment of Farmers in Nigeria: A Hybridized Credit-Scoring Approach

Using data from 360 smallholder farmers in Southeast Nigeria, the study creates the architecture for a new farmer's hybrid credit rating system used in classifying farmers who applied for microfinance loans based on their creditworthiness. We discovered new evidence that the hybridized credit scoring algorithm demonstrated unprecedented concordance in assessing the financial viability of farmers along gender lines. The discriminant analysis, in particular, closely aligned with the credit score model, with 34.4% and 46.7% of male and female farmers grouped as creditworthy, reflecting the model's estimates of 33.3% and 45.5%, indicating gaps of 12.3% and 12.2%, respectively, to the advantage of the female farmers. Our findings further suggest that annual income, marital status, and farm size strongly influence the separation between creditworthy and non-creditworthy farmers. While age, loan term, and a history of defaults had a negative impact on discrimination, in light of the findings, we recommend a collaboration between authorities, financial institutions, and extension workers in offering tailored trainings to both male and female farmers, assisting them in meeting up-to-date credit prerequisites, adopting modified farming techniques, and improving their general preparedness to be accepted for loans in this changing credit evaluation landscape so as to bridge the disparity and promote financial inclusion for farmers irrespective of gender affiliations.

Inaccuracies Caused by Hybrid Credit Models and Remedies as Implemented by ZRE

Inaccuracies Caused by Hybrid Credit Models and Remedies as Implemented by ZRE

A new hybrid credit scoring ensemble model with feature enhancement and soft voting weight optimization

The explosive development of artificial intelligence (AI) has reshaped all aspects of life, including credit scoring. At the same time, the rapid expansion of the consumer finance industry has led to a huge demand. In this study, a new hybrid ensemble model with feature enhancement and soft voting weight optimization is proposed to achieve superior predictive power for credit scoring. For mining and characterizing the implicit information of the features, a new voting-based feature enhancement method is proposed to adaptively integrate the outlier detection and clustering capabilities through the weighted voting mechanism to form a feature-enhanced training set. To balance the feature-enhanced training set precisely and effectively, a new bagging-based undersampling method is proposed to obtain a balanced training set by undersampling from the negative binomial distribution through the bagging strategy. To maximize the performance of the model, a new weight-optimized soft voting method is proposed to optimize the soft voting weights of the base classifiers in the classifier ensemble using the COBYLA algorithm and then constructing the stacking-based ensemble model. Five datasets and five evaluation indicators were used for evaluation. The experimental results demonstrate the superior performance of the proposed model and prove its robustness and effectiveness.

State guarantees to counteract the financial effects of the COVID-19 pandemic on industrial supply chains

As the recent COVID-19 pandemic has clearly demonstrated, appropriate state support policies are crucial for supporting industrial supply chains during crises to prevent viable businesses from defaulting. In this context, this study proposes a hybrid credit risk model to appropriately size public interventions for viable (worthy) businesses through systematic risk assessment during a period of turmoil. This study discusses the effects of the credit crunch-based economic downturn and proposes a methodology to assist policymakers in managing limited public resources to effectively support industrial supply chains. The proposed approach initially focuses on the dynamics of credit risk during economic recession periods, identifying the conditions that may justify a public intervention strategy based on public guarantees. Subsequently, a hybrid credit risk model is developed to appropriately size public interventions by quantifying systematic risk. Finally, a numerical application is presented to demonstrate the effectiveness of the proposed approach.

Development of a Hybrid Credit Scoring Model for the Banking System

Development of a Hybrid Credit Scoring Model for the Banking System

Financially engineering a “self-generative” political economy of creditworthiness: Expertocratic exemption problems for sustainable debt and democracy

Repeated crises have proven credit rating agency (CRA) models/methods erroneous and “business-as-usual” unsustainable. Nevertheless, considerable dubious “default risk” management and technoscientific capitalist expertise remain unchanged. Unpacking sovereign ratings, we appreciate how “debt sustainability analysis” (DSA) distortions underpin expertocratic CRA (default) anomaly. Their neoliberal “politics of limits” performance helps market (shareholder) imperatives trump those of democratic (stakeholder) politics. Given surging inflation and debt (distress) to remedy Covid-19-induced shocks, ratings aid constitute and (re)validate the subjectivities/affinities and organizational conditions advancing a “self-equilibrating,” “self-generative” agencement political economy of creditworthiness (PEC). Antagonizing sustainable budgetary government’s programmatic/expertocratic and operational/democratic asymmetry, econophysics ratings diminish fiscal sovereignty. Universal PEC management through hybrid credit risk/uncertainty qualculation mitigates negative externality contestation shielding CRAs from serious reform. Ratings procyclicality and contagion reinforce this precarious sociotechnical agencement PEC as the status quo.

A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural network

The credit risk prediction technique is an indispensable financial tool for measuring the default probability of credit applicants. With the rapid development of machine learning and the application of big data, increasingly sophisticated models have been designed to construct effective credit risk prediction models. In this study, we propose a two-stage hybrid model to enhance the prediction performance of credit risk. First, to make full use of the classified information hidden in credit data, we employ XGBoost to linearize and transform the original features into a high-dimensional sparse feature matrix. Second, to effectively process the transformed high-dimensional data and to discover the relationships between the features, a recently proposed graph-based neural network (forgeNet) model, which is good at addressing high-dimensional data, is deployed to predict the credit risk. The real-world credit data of the Lending Club for the period from 2007 to 2016 were collected and partitioned based on the economic cycle to validate the robustness of the proposed model. The experimental results show that feature transformation and feature graph mining are two pragmatic processes for credit risk prediction when analyzing credit data. Furthermore, the proposed model is robust against different economic cycles and achieves the best average prediction results of 87.52%, 93.13% and 85.59% in terms of accuracy, F1-score, and G-mean compared with other benchmarks, including individuals, hybrid models, and ensembles. The average performance of the proposed model rose by 6.14, 7.59 and 6.18 percentage points, respectively, which demonstrates the outperformance of the proposed two-stage model in credit risk prediction applications

Power Exchange Option with a Hybrid Credit Risk under Jump-Diffusion Model

In this paper, we study the valuation of power exchange options with a correlated hybrid credit risk when the underlying assets follow the jump-diffusion processes. The hybrid credit risk model is constructed using two credit risk models (the reduced-form model and the structural model), and the jump-diffusion processes are proposed based on the assumptions of Merton. We assume that the dynamics of underlying assets have correlated continuous terms as well as idiosyncratic and common jump terms. Under the proposed model, we derive the explicit pricing formula of the power exchange option using the measure change technique with multidimensional Girsanov’s theorem. Finally, the formula is presented as the normal cumulative functions and the infinite sums.

Credit Card Fraud Detection System Using Machine Learning

The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

Credit Systems for Bycatch and Biodiversity Conservation

Credit systems for mitigation of bycatch and habitat impact, incentive-based approaches, incentivize changes in fishery operator behavior and decision-making and allow flexibility in a least-cost method. Three types of credit systems, originally developed to address environmental pollution, are presented and evaluated as currently underutilized incentive-based approaches. The first, a cap-and-trade approach, evolved out of direct regulation through restricted limits with flexibility through the creation of tradeable unused portion of the limit, called credits. The second, a penalty-reward system, incentivizes bycatch- and habit-impact- reducing vessel behavior through rewards for positive behavior, and penalties for negative behavior. The third is a hybrid of the first two. All three systems can be used in the context of both absolute (fixed) and relative (rate-based or proportional) credits. Transferable habitat impact credit systems are developed for area management. The cap-and-trade credit system is directly compared to a comparable property rights system in terms of characteristics, strengths, weakness, and applicability. The Scottish Conservation Scheme and halibut bycatch reduction in the Alaskan multispecies groundfish fishery provide real-world examples of success with credit systems. The strengths, weaknesses, and applicability of credit systems are summarized, along with a set of recommendations. Cap-and-trade credit systems provide an important alternative to property rights, such as when rights are not feasible, and for this reason should prove useful for international fisheries. Penalty-reward and hybrid credit systems can substitute for cap-and-trade credit systems or property rights or complement them by addressing a related but otherwise unaddressed issue.

The review of the open challenges in the IRB loan portfolio credit risk modeling

The Basel Committee on Banking Supervision finalized the Basel III accord in the December 2017 and launched the set of its standards – the Basel Framework – in December 2019. Both documents allow bank to use mathematical models for the credit risk estimation. There are quantitative and qualitative requirements for models to be allowed for use in the prudential regulation of banks. The approach is called an Internal-Ratings-Based one (IRB). This paper aims at discussing a set of issues related to IRB credit risk modeling and such model estimates use. Those issues include data pooling in the credit registries, applying copula-discriminant analysis, validating the borrower concentration per grade, assigning the hybrid credit rating, use of model estimates when voting at the credit committee, estimate of the ultimate credit risk-taking by banks.

Valuation of Exchange Option with Credit Risk in a Hybrid Model

In this paper, the valuation of the exchange option with credit risk under a hybrid credit risk model is investigated. In order to build the hybrid model, we consider both the reduced-form model and the structural model. We adopt the probabilistic approach to derive the closed-form formula of an exchange option price with credit risk under the proposed model. Specifically, the change of measure technique is used repeatedly, and the pricing formula is provided as the standard normal cumulative distribution functions.

Binary BAT algorithm and RBFN based hybrid credit scoring model

Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.

Pricing vulnerable options in a hybrid credit risk model driven by Heston–Nandi GARCH processes

This paper proposes a hybrid credit risk model, in closed form, to price vulnerable options with stochastic volatility. The distinctive features of the model are threefold. First, both the underlying and the option issuer’s assets follow the Heston–Nandi GARCH model with their conditional variance being readily estimated and implemented solely on the basis of the observable prices in the market. Second, the model incorporates both idiosyncratic and systematic risks into the asset dynamics of the underlying and the option issuer, as well as the intensity process. Finally, the explicit pricing formula of vulnerable options enables us to undertake the comparative statistics analysis.

Credit Fraud Detection Based on Hybrid Credit Scoring Model

Credit risk rating can be described by several economic activity indicators. Utilizing these financial movement markers to build a tenable credit scoring model will enormously improve the precision of the model. This method can be used in a series of credit evaluations and specific economic conditions. A reasonable scenario in which the uncertainty is consistent. In this paper, the logistic regression algorithm is joined with weighted evidence to fabricate another credit score model. Through the relationship existing in economic activities, the connection of each economic movement is additionally dissected by utilizing the correlation orthogonal transformation in the weight of proof to improve the exactness of the model. In practice, due to numerous weaknesses in the records, there is significant error in the logistic regression. Hence, building of hybrid scoring model can increase the accurateness of credit score. Thus improved the prediction rate of user credit scores and reducing the occurrence of credit fraud.

A novel hybrid credit scoring model based on ensemble feature selection and multilayer ensemble classification

AbstractCredit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets.

Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification

Hybrid credit scoring model using neighborhood rough set and multi-layer ensemble classification

Matrix Sequential Hybrid Credit Scorecard Based on Logistic Regression and Clustering

The Basel II Accord pointed out benefits of credit risk management through internal models to estimate Probability of Default (PD). Banks use default predictions to estimate the loan applicants’ PD. However, in practice, PD is not useful and banks applied credit scorecards for their decision making process. Also the competitive pressures in lending industry forced banks to use profit scorecards, which show the profitability of customers. Applying these scorecards together makes the loan decision making process for banks more confusing. This paper has an obvious and clean solution for facilitating the confusion of loan decision making process by combining the credit and profit scorecards through introducing a matrix sequential hybrid credit scorecard. The applicability of the introduced matrix sequential hybrid scorecard results are shown using data from an Iranian bank.

An Efficient Multi-layer Ensemble Framework with BPSOGSA-Based Feature Selection for Credit Scoring Data Analysis

Credit scoring is extensively used by credit industries and financial institutions for financial decision-making. It is a way to assess the risk associated with an applicant based on historical data. However, the historical data may have large number of redundant and noisy features which could affect performance of credit scoring models. Main focus of this paper is to develop a hybrid credit scoring model by combining the feature selection and multi-layer ensemble classifier framework to improve the prediction performance of credit scoring model. The proposed hybrid credit scoring model uses hybrid binary particle swarm optimization and gravitational search algorithm (BPSOGSA) for feature selection and multi-layer ensemble classifier framework with five heterogeneous classifiers. A novel V-shaped transfer function for BPSOGSA is also designed for effective feature selection, which is used to transform the continuous search space to binary search space. Also, a novel fitness function for BPSOGSA is proposed to calculate the fitness value for each search agent. Further, multi-layer ensemble classifier framework along with a novel aggregation function is designed based on generalized convex function. The proposed hybrid credit scoring model is validated using Australian, German-categorical, German-numerical and Japanese credit scoring datasets. The experimental results on all the datasets demonstrate that the proposed credit scoring model outperforms other methods such as random forest and ensemble frameworks, namely majority voting, layered majority voting, weighted voting and layered weighted voting in terms of accuracy, sensitivity, G-measure and ROC characteristics.