2023· International Journal of Artificial Intelligence & Digital Transformation· 0 citations
TL;DR
An AI-based credit scoring approach that uses machine learning, deep learning, and hybrid models to improve accuracy and scalability is proposed, concluding that AI-driven credit scoring enhances smart banking, customer experience, and financial inclusion.
Abstract
Credit scoring is vital in modern banking for loan decisions, risk management, and financial inclusion. Traditional models rely on limited, static financial data and struggle to adapt to changing conditions. This paper proposes an AI-based credit scoring approach that uses machine learning, deep learning, and hybrid models to improve accuracy and scalability. The framework integrates financial data with alternative data such as transaction behavior, digital footprints, and repayment patterns. Techniques like decision trees, random forests, SVMs, gradient boosting, and neural networks are analyzed and compared with traditional methods. It also emphasizes explainable AI (XAI) to ensure transparency, fairness, and regulatory compliance. Results show that AI models outperform conventional approaches in accuracy and risk prediction. The study highlights the importance of interpretability, bias reduction, and strong governance, concluding that AI-driven credit scoring enhances smart banking, customer experience, and financial inclusion.
The banking sector requires accurate risk assessment to maintain financial stability and reduce losses. Traditional risk assessment methods rely on historical data, credit scores, and statistical techniques but often struggle with large-scale data, complex patterns, and real-time decision-making. Advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have introduced intelligent solutions for evaluating credit, fraud, operational, market, and liquidity risks. AI-based models analyze vast amounts of structured and unstructured financial data to identify hidden patterns and generate predictive insights. Techniques such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, Neural Networks, and Deep Learning models improve risk prediction accuracy and fraud detection. This study proposes an AI-driven risk assessment framework comprising data collection, preprocessing, feature extraction, model training, risk prediction, and decision support. Performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC shows that AI models outperform traditional approaches by enhancing prediction capability and reducing manual intervention. Despite challenges related to data privacy, interpretability, regulatory compliance, and ethics, AI-based risk assessment significantly strengthens modern banking risk management and supports sustainable financial operations.
Anita Verma· International Journal of Com...· 0 citations
Financial Technology (FinTech) has transformed banking and lending by enabling fast, accessible, and scalable digital credit services. As digital lending expands, traditional credit assessment methods are becoming less effective in analyzing complex borrower behaviors and alternative data sources. Artificial Intelligence (AI) has emerged as a powerful solution for credit risk assessment, utilizing machine learning, deep learning, predictive analytics, and natural language processing to evaluate borrower risk more accurately. This study examines AI-based credit risk assessment techniques in FinTech, focusing on credit scoring models, automated underwriting, big data analytics, and real-time risk monitoring. The proposed framework includes data preprocessing, feature engineering, model training, and risk classification. Findings indicate that AI-driven models significantly improve prediction accuracy, fraud detection, risk segmentation, and loan approval decisions compared to traditional methods. Ensemble learning and deep neural networks demonstrate strong performance in large-scale credit assessment tasks. AI also promotes financial inclusion by leveraging alternative data for individuals with limited credit histories. However, challenges related to model transparency, algorithmic bias, data privacy, and regulatory compliance remain. Overall, AI-powered credit risk assessment is a key driver of intelligent, customer-centric, and sustainable FinTech lending systems.
Arvind S. Menon· International Journal of Com...· 0 citations
With the increasing fluctuations in the global economy and the development of digital finance in China, commercial banks are facing two major challenges: the declining quality of credit assets and the growth of bad loans. Traditional credit scoring models based on Logistic Regression are easy to explain, but they struggle to handle complex data that is high-dimensional or changes over time. To improve the accuracy of credit risk prediction, this study develops a hybrid model called Scorecard-LSTM. This model combines traditional credit scoring methods with LSTM networks, also added an Attention Mechanism to help the model focus more on key customer behaviors. The study uses data from a commercial bank in Taiwan, including basic customer information, financial status, and repayment history. By using methods like WOE encoding, feature binning, and joint training, the model connects the clear logic of traditional scorecards with the powerful learning ability of deep learning. The experimental results show that this hybrid model is much better than traditional models in both accuracy and stability. With an AUC value of 0.7741, the model performs very well in identifying bad loans and providing early warnings for potential defaults.
Ziyan Cheng· Frontiers in Business, Econo...· 0 citations
This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring, and Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.
Radouane Aboulmaouda, Khadija Slimani, N. Chaoui· Journal of Risk and Financia...· 0 citations
The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles," and makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements.
Bojun Chen· Advances in Economics, Manag...· 0 citations
Predicting credit risk is vital for banks as it safeguards financial stability, minimizes default losses, optimizes capital, and ensures regulatory compliance. This study aims to predict credit risk (High/Low) in commercial banks by integrating machine learning with traditional econometric approaches. The Structural Learning in Vague Environments (SLAVE) fuzzy rule-based model handles ambiguity in financial decisions, while the eXtreme Gradient Boosting (XGBoost) uncovers non-linear patterns among predictors. Input variables—profitability, liquidity risk, ESG (environmental, social, and governance) score, and monetary freedom—were selected via multicollinearity tests and three panel regression models, including ordinary least squares (OLS), fixed effects, and random effects models. The empirical investigation uses a panel dataset of forty commercial banks across seven Middle Eastern countries from 2014 to 2023, yielding 400 observations. Regression results reveal that profitability and ESG score significantly reduce credit risk. Liquidity risk and monetary freedom increase credit risk. XGBoost combined with the SHapley Additive exPlanations (SHAP)-based interpretation identifies ESG Score as the most influential predictor. The SLAVE model was evaluated using three data splits: 70/30, 80/20, and 90/10. The 80/20 split achieved the highest accuracy, with superior performance in identifying low-risk banks. Stronger ESG performance and stable monetary environments contribute to fostering sustainable banking and reducing credit risk, making these indicators valuable for risk management frameworks in the Middle Eastern banking sector.
Jamil J. Jaber, A. A. Alkhawaldeh, Qusay Ayman Sulayman Mazahreh et al.· Risks· 0 citations
