Credit Risk Assessment Model Research Articles

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.

An Enhanced Credit Risk Evaluation by Incorporating Related Party Transaction in Blockchain Firms of China

Related party transactions (RPTs) can serve as channels for the spread of credit risk events among blockchain firms. However, current credit risk-assessment models typically only consider a firm’s individual characteristics, overlooking the impact of related parties in the blockchain. We suggest incorporating RPT network analysis to improve credit risk evaluation. Our approach begins by representing an RPT network using a weighted adjacency matrix. We then apply DANE, a deep network embedding algorithm, to generate condensed vector representations of the firms within the network. These representations are subsequently used as inputs for credit risk-evaluation models to predict the default distance. Following this, we employ SHAP (Shapley Additive Explanations) to analyze how the network information contributes to the prediction. Lastly, this study demonstrates the enhancing effect of using DANE-based integrated features in credit risk assessment.

The Evaluating Impact of Artificial Intelligence on Risk Management and Fraud Detection in the Commercial Bank in Bangladesh

The integration of Artificial Intelligence (AI) in the banking sector represents a significant leap forward in the realms of risk management and fraud detection. This paper explores the transformative effects of AI in these areas, emphasizing both the improvements and the challenges brought about by its implementation. In risk management, AI's influence is diverse and profound. Advanced algorithms allow for the creation of more sophisticated credit risk assessment models by detecting subtle patterns in large datasets that might be overlooked by human analysts. This capability enhances the accuracy of credit risk evaluations. Additionally, real-time monitoring of transactions helps in the immediate mitigation of risks, which is particularly crucial when dealing with market and liquidity risks. AI also significantly aids in automating compliance with regulatory requirements, reducing the likelihood of human errors and enabling quicker adaptation to changes in regulations. Operational risks are also minimized through AI's ability to automate routine tasks and strengthen cybersecurity measures. AI systems are adept at identifying anomalies that may indicate fraud by scrutinizing transaction data and customer behavior. The predictive capabilities of AI enable banks to anticipate and prevent potential fraud schemes. Moreover, AI systems can adapt and evolve in response to changing tactics used by fraudsters, maintaining their effectiveness over time. AI enhances customer authentication processes through the use of advanced technologies such as biometric verification, providing an additional layer of security. However, the implementation of AI in banking raises significant concerns regarding data privacy and security due to the sensitive nature of banking information. Furthermore, AI models can inherently carry biases that lead to discriminatory outcomes, necessitating ongoing monitoring and adjustments to these models. The complexity and lack of transparency in AI systems also pose challenges, particularly when AI-driven decisions have significant impacts on customers. The evolving regulatory frameworks for AI in banking present another layer of complexity, as banks must continuously adapt to comply with new and changing regulations. This paper highlights the need for a balanced approach to leveraging AI's potential in banking, addressing both its transformative benefits and the ethical and regulatory challenges involved. By doing so, banks can harness AI to enhance their operations while ensuring fairness, transparency, and compliance with regulatory standards.

A hierarchical attention-based feature selection and fusion method for credit risk assessment

A stable financial environment is requisite for the continuous growth of the E-business market, emphasizing the importance of credit risk assessment. Generally, credit risk assessment involves evaluating the creditworthiness of the economic entity and predicting the probability of defaulting on their financial obligations. Existing works mainly focus on exploring more features from various sources to help default prediction, but tend to overlook the feature cost and feature fusion problem. In this paper, we proposed a novel credit risk assessment model based on the hierarchical attention method, characterizing the ability to manage feature acquisition cost and adaptively integrate multi-view features. Particularly, to fulfill the utility of multi-source features, three segmenting standards are proposed to categorize multi-source features into different views according to their characteristics and statistical attributes. Then, a feature-level attention mechanism is proposed to estimate feature importance and guide feature selection, while a view-level attention mechanism is proposed to aggregate view representations and produce default predictions. The proposed method is capable of enhancing crucial features in a hierarchical way and undertaking feature selection to meet feature acquisition cost, thereby boosting the model performance with feature cost awareness. Experimental results show that the proposed method can approximate the best performance with 5% of selected features, and can also achieve a 1.1% AUC improvement compared with the best baseline method over the full feature set, demonstrating our effectiveness of enhancing default prediction performance while reducing feature costs.

Navigating Uncertainties: A Comparative Exploration of Risk Management Strategies in The Indian Auto Finance Industry Among Selected Companies

The automotive financing business in India is an essential component of the nation's economic progress. It offers customers and car manufacturers the essential financial support they require to fulfill their financial obligations. Nevertheless, this industry is confronted with a plethora of uncertainties, such as shifting market needs, shifting regulatory requirements, unstable economic conditions, and changing customer habits. Through the analysis of case studies of renowned companies, the research identifies significant risk factors and analyzes the efficiency of various approaches for mitigating those risks. The research makes use of a mixed-methods approach, including qualitative insights from industry professionals through interviews and surveys in addition to quantitative data analysis. The findings shed light on a variety of techniques, including credit risk assessment models, the diversification of loan portfolios, the adoption of sophisticated analytics, and the deployment of rigorous compliance frameworks. Furthermore, the research sheds light on the role that technology advancements, such as blockchain and artificial intelligence, play in boosting the capacities of risk management. The comparison highlights considerable disparities in the efficiency of strategies, which are driven by factors such as the size of the organization, its position in the market, and its acceptance of technical advancements. This study makes a contribution to the current body of research by offering insights that can be put into practice by industry practitioners and policymakers in order to improve the resilience and sustainability of the Indian auto financing sector in the face of uncertainty. The findings that were obtained are intended to provide assistance to businesses in the process of refining their risk management frameworks, therefore generating a financial climate that is more stable and secure.

Influence of Artificial Intelligence on Credit Risk Assessment in Banking Sector

Purpose: The aim of the study was to examine the influence of artificial intelligence on credit risk assessment in banking sector. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that AI-driven models demonstrate superior performance in identifying risky borrowers and capturing complex credit risk patterns compared to traditional methods. Additionally, the integration of explainable AI (XAI) techniques has enhanced transparency and interpretability in credit risk assessment processes, facilitating better understanding among stakeholders and improving decision-making transparency. Unique Contribution to Theory, Practice and Policy: Decision theory & technology acceptance model (TAM) may be used to anchor future studies on influence of artificial intelligence on credit risk assessment in banking sector. Continuously invest in research and development to advance the theoretical understanding of AI-driven credit risk assessment models. This includes exploring the integration of machine learning with behavioral economics theories to better predict borrower behavior and default probabilities. Encourage banks to adopt a hybrid approach that combines the strengths of AI-driven models with human expertise. Develop comprehensive regulatory guidelines and standards to govern the use of AI in credit risk assessment and ensure ethical and responsible practices. This includes establishing transparent model validation and governance frameworks to mitigate the risks of algorithmic bias, data privacy violations, and discriminatory lending practices. Regulatory authorities should also promote industry-wide collaboration and knowledge sharing to foster innovation while safeguarding consumer interests and financial stability.

An FTwNB Shield: A Credit Risk Assessment Model for Data Uncertainty and Privacy Protection

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

A SC Financial Credit Risk Assessment Model Based on Particle Filter and SVM with Gain Information

The accuracy of credit risk prediction in SC financing is critical for many enterprises, based on machine learning algorithms can be good for SME credit risk assessment research, for this reason, this paper establishes a combinatorial model that can improve credit risk prediction, using support vector machine (SVM) and particle filtering to achieve credit risk classification and prediction, we and introduce information gain (IG) to extract the prediction of The model uses SVM and particle filtering to classify and predict credit risk, and we introduce information gain (IG) to extract feature variables that contribute significantly to the prediction results and optimize model feature inputs. Compared with the benchmark model, the prediction accuracy of the model in this paper is 97.62%, which is 8.97% higher than that of SVM, and the performance of IG with feature optimization improves the prediction accuracy by another 3%.

Development and Implementation of a Multilayer Deep Learning-Based Bank Credit Risk Forecasting System

The complexity of the financial environment and the international community makes the capital flow face various challenges, and it is difficult to obtain accurate credit prediction results in the actual application environment. Considering the complex non-linear characteristics of customer information, the Analytic Hierarchy Process is studied to meet the needs of bank credit risk assessment. On this basis, a depth neural network with different complexities was selected for the three indicators built to classify the features. The composition of the neural network module and the number of neurons were determined by experiment, and Dropout was used to prevent overfitting of the test dataset. Stability and ablation experiments showed that the model can control the error between datasets to 0.021. The ablation experiment showed that the numbers of hidden layers and neurons were the best. Simulation tests showed that the sensitivity and accuracy of this method were 85.25% and 92.55%, respectively, which were superior to other classification methods. The real data of banks in the past four years were tested. The results could accurately classify the risks of enterprises and individual customers, and the results of stress test showed that the model is stable. It is found that traditional credit risk assessment models rely on statistical means and rule decisions, and these methods may not fully reveal the complex non-linear relationship and the internal relationship of financial indicators in high-dimensional data. The combination of deep learning technology and hierarchical analysis can better deal with and explain the complex non-linear problems in bank risk assessment.

Enterprise Credit Risk Decision: Application Based on Improved AHP

The credit risk of Shouguang vegetable enterprises in China is the biggest obstacle to corporate loans. Building a credit risk assessment model for Shouguang vegetable enterprises and accurately rating the risk of loan enterprises is the key to successful loans. This article aims to construct an AHP evaluation model for the credit risk of Shouguang vegetable enterprises. The data is sourced from managers, bank credit personnel, university researchers, and enterprise related customers who are familiar with the enterprise, while considering four risk levels: impact degree(I), occurrence probability(P), risk manageability(M), and government support(S). This article uses AHP and risk index scores to evaluate the credit risk of Shouguang vegetable enterprises. This model calculates the risk index score based on survey data from 41 corporate credit risk professionals, constructs a pairwise comparison judgment matrix, and conducts consistency testing. It calculates the risk level membership vectors of impact degree, occurrence probability, risk manageability, and government support level at four risk levels, and then calculates the comprehensive evaluation membership vector of credit risk for Shouguang vegetable enterprise. The calculation results show that the comprehensive credit risk assessment level of Shouguang vegetable enterprise belongs to the general risk level, with a membership value of 0.5836. The results still show that the credit rating of Shouguang vegetable enterprises in the four risk levels of impact degree, occurrence probability, risk manageability, and government support are all average risk levels, but there are differences in membership values. The maximum membership value under the impact degree level is 0.6163, and the minimum membership value under the risk manageability level is 0.5572. This study provides a feasible and practical model for enterprise credit risk assessment and conducts a detailed evaluation of the credit risk of Shouguang vegetable enterprise, providing valuable reference for enterprise managers, bank credit personnel, and related researchers.

Research on the Financial Credit Risk Management Model of Real Estate Supply Chain Based on GA-SVM Algorithm: A Comprehensive Evaluation of AI Model and Traditional Model

By combining machine learning AI model with traditional credit risk measurement model, this paper aims to more accurately measure the credit default risk of real estate enterprises. Through professional credit management knowledge, the integration of these two models can fill the gaps of traditional models outside of financial data, and use the research experience of traditional models in the financial field to provide guidance for machine learning. This paper focuses on the key issues of capital intensity, exploration of Supply Chain Finance Growth and Credit Risk Control within the Real Estate Sector. By combining qualitative and quantitative methods. Through literature research and empirical analysis, this paper develops a supply chain finance credit risk assessment index system tailored for small and medium-sized enterprises operating in China's real estate sector. The index system comprises 23 risk assessment indicators, and comprehensively considers many factors such as corporate financial status and market environment. Then, a credit risk assessment model for small and medium-sized real estate enterprises is developed from a supply chain finance perspective, utilizing genetic algorithm optimization for Support Vector Machine (GA-SVM)., and the advantages of this model compared with the traditional model in accuracy and generalization ability are verified through comparative analysis of experiments. The research results can help reduce the information asymmetry in the real estate industry, enhance the credit rating evaluation process for small and medium-sized enterprises, and furnish references to aid commercial banks in credit and credit risk management within the real estate supply chain finance sector.which has important theoretical and practical value.

Credit Risk Assessment of Listed Companies Based on Vector Angle Cosine

A study is conducted on the credit risk assessment model of listed companies in China. Firstly, factor analysis is used to construct an indicator system from five dimensions: debt paying ability, development ability, operating ability, profitability, and cash flow. Then, the cosine method of vector angle is used to replace the subjective expert scoring method and assign weights to the indicators. Finally, based on the scoring model, the scores of 25 listed companies are calculated and their credit risk is evaluated.

Optimizing bank credit risk assessment models using big data analytics

Abstract With the strong promotion of financial globalization and marketization and the rapid development of financial industry innovation, credit risk management is becoming an important work that banks need to pay more and more attention to. This paper focuses on the construction of the bank credit risk model to carry out research. First of all, according to the credit characteristics selection method, the optimal indicators are combined with the indicators for the correlation test of variables. On this basis, the sample data is calculated using the up-sampling method of SMOTE to complete the algorithm and sampling. After obtaining the data, data preprocessing is carried out using the MinMaxScaler scaler method, and the processed data is inputted into the improved momentum BP neural network to complete the credit risk assessment. Profiling with risk feature data, it is obtained that each feature is not a separate individual from the other. They are interdependent and connected and have obvious correlations. For example, there are 47 features in the dataset with sample missing ratios greater than 0.97, which is too high and indicates an invalid feature. The results of the study show that SMOTE’s up-sampling method and Momentum BP algorithm can quickly utilize big data to provide a more accurate decision basis for bank credit risk assessment.

Research on Personal Credit Risk Assessment Model Based on Proportional Bootstrap and Stacking

Research on Personal Credit Risk Assessment Model Based on Proportional Bootstrap and Stacking

Credit Risk Modeling Using Interpreted XGBoost

Purpose: The aim of the paper is to develop a credit risk assessment model usingb the XGBoost classifier supported by interpretation issues. Design/methodology/approach: The risk modeling is based on Extreme Gradient Boosting (XGBoost) in the research. It is a method used for regression and classification problems. It is based on a sequence of decision trees using a gradient-based optimization method of the loss function to minimize the errors of weak estimators. We use also methods for performing local and global interpretability: ceteris paribus charts, SHAP and feature importance approach. Findings: Based on the research results, it can be concluded that XGBoost achieved higher values of performance metrics than logistic regression, except sensitivity. It means that XGBoost indicated a smaller percentage of all bad client. Results of local interpretability enable a conclusion that in the case of the client in question, the credit decision is positively influenced by credit scores from external suppliers, while it is negatively influenced by minimal external scoring and short seniority. The number of years in the car and higher education are also positive. Such information helps to justify a negative credit decision. Results of global interpretability enable a conclusion that higher values of the traits associated with the z-scores are accompanied by negative Shapley values, which can be interpreted as a negative effect on the explanatory variable. Research limitations/implications: XGBoost, A ceteris paribus plot, SHAP, and feature importance methods can be used to develop a credit risk assessment model including machine learning interpretability. The main limitation of research is to compare the results of XGBoost only to the logistic regression results. Future research should focus on comparing the results of XGBoost to other machine learning methods, including neural networks. Originality/value: One of the key processes in a bank is the credit decision process, which is the evaluation of a client’s repayment risk. In the consumer finance sector, the processes are usually largely automated, and increasingly the latest machine learning methods based on neural networks and ensemble learning methods are being used for the purpose. Although machine learning models allow for achieving higher accuracy of credit risk assessment compared to traditional statistical methods, the main problem is the low interpretability of machine learning models. The models often perform as the “black box”. However, the interpretation of the results of risk assessment models is very important due to the need to explain to the client the reasons for assessing their credit risk.

Determinants of Credit Default in Banking in Nigeria

Purpose: The aim of the study was to examine the determinants of credit default in banking in Nigeria.
 Methodology: The study adopted a desktop methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library
 Findings: The determinants of credit default in Nigerian banking encompass a range of factors, including economic stability, regulatory oversight, risk management practices, loan portfolio quality, and macroeconomic conditions. Addressing these factors is crucial for banks to effectively manage credit risk and reduce the likelihood of credit defaults.
 Unique Contribution to Theory, Practice and Policy: Agency Theory, Pecking Order Theory and Information Asymmetry Theory may be used to anchor future studies on the examining determinants of credit default in banking in Nigeria. banks should initiate financial literacy programs for borrowers to improve their understanding of financial products and responsibilities. Regulators should encourage the adoption of advanced credit risk assessment models and provide guidelines on best practices in risk management.

Vehicle leasing credit risk assessment modeling by applying extended logistic regression

In vehicle leasing industry which presents a great business opportunity, information completed by applicants was assessed and judged by leasing associates manually in most cases; therefore, assessment results would be affected by their personal experience of leasing associates and decisions would be further affected accordingly. There are few researches on applicant credit risk assessment due to not easy to obtain of vehicle leasing data. Further, the difficulty in vehicle leasing risk assessment is increased due to class imbalance problems in vehicle leasing data. In order to address such issue, a research on credit risk assessment in vehicle leasing industry was conducted in this study. The great disparity in the ratio of high risk and low risk data was addressed by applying synthetic minority over-sampling technique (SMOTE). Then, classification effect of risk assessment model was improved by applying logistic regression in a two-phase manner. In the section of empirical analysis, the feasibility and effectiveness of the approach proposed in this study was validated by using data of actual vehicle leasing application cases provided by a financial institution in Taiwan. It is found that the proposed approach provided a simple yet effective way to build a credit risk assessment model for companies that provide vehicle leasing.

RETRACTED ARTICLE: The Application of Artificial Immune Network in E-Commerce Credit Risk Assessment

In order to improve the accuracy of e-commerce credit risk assessment, this paper suggests utilizing an artificial immune network to upgrade the text mining algorithm. Through this process, a new e-commerce risk assessment model reliant on the improved algorithm can be constructed with the intention of decreasing the likelihood of risk in digital transactions. The results show that the accuracy and loss rate of the improved clustering algorithm are 97.3% and 4.3%, respectively, both of which are better than the comparison algorithm. Then, the empirical analysis of the e-commerce credit risk assessment model proposed in the study shows that the average fitness and accuracy of the model after stability are 0.0022 and 95.63%, respectively, demonstrating superior performance compared to the comparison model. The above results show that the improved algorithm and the risk assessment model have good performance. Therefore, using this model to evaluate the credit risk of e-commerce can not only improve the accuracy of credit evaluation and promote the sustainable development of e-commerce. Furthermore, it can catalyze the adoption of innovative credit evaluation methods and promote the application of artificial intelligence technology in e-commerce.

Rural micro-credit model design and credit risk assessment via improved LSTM algorithm.

Rural microcredit plays an important role in promoting rural economic development and increasing farmers' income. However, traditional credit risk assessment models may have insufficient adaptability in rural areas. This study is based on the improved Long Short Term Memory (LSTM) algorithm using self organizing method, aiming to design an optimized evaluation model for rural microcredit risk. The improved LSTM algorithm can better capture the long-term dependence between the borrower's historical behavior and risk factors with its advantages in sequential data modeling. The experimental results show that the rural microcredit risk assessment model based on the self organizing LSTM algorithm has higher accuracy and stability compared to traditional models, and can effectively control credit default risk, providing more comprehensive risk management support for financial institutions. In addition, the model also has real-time monitoring and warning functions, which helps financial institutions adjust their decisions in a timely manner and reduce credit losses. The practical application of this study is expected to promote the stable development of rural economy and the advancement of financial technology. However, future work needs to further validate the practical application effectiveness and interpretability of the model, taking into account the special circumstances of different rural areas, in order to achieve sustainable application of the model in the rural microcredit market.

Knowledge graph driven credit risk assessment for micro, small and medium-sized enterprises

Micro, Small, and Medium-sized Enterprises (MSMEs) are essential for the growth and development of the country's economy, as they create jobs, generate income, and foster production and innovation. In recent years, credit risk assessment (CRA) has been an essential process used by financial institutions to evaluate the creditworthiness of MSMEs and determine the likelihood of default. Traditionally, CRA has relied on credit scores and financial statements, but with the advent of machine learning (ML) algorithms, lenders have a new tool at their disposal. By and large, ML algorithms are designed to classify borrowers based on their credit history and transactional data while leveraging the entity relationship involved in credit transactions. This study introduces an innovative knowledge graph-driven credit risk assessment model (RGCN-RF) based on the Relational Graph Convolutional Network (RGCN) and Random Forest (RF) algorithm. RGCN is employed to identify topological structures and relationships, which is currently nascent in traditional credit risk assessment methods. RF categorises MSMEs based on the enterprise embedding vector generated from RGCN. Extensive experimentation is conducted to assess model performance utilising the Indian MSMEs database. The balanced accuracy of 92% obtained using the RGCN-RF model demonstrates a considerable advancement over prior techniques in identifying risk-free enterprises.