Jul 2026· International Journal of Business Ecosystem & Strategy (2687-2293)· 0 citations· 41 references
TL;DR
An efficient and effective credit risk assessment model capable of learning incrementally based on machine learning algorithm called Adaptive Heterogeneous Dynamic Ensemble Selection (AHDES) that uses big data from alternative sources for financial for the underbanked is proposed.
Abstract
Financial institutions predict the probability of default when making lending decisions using conventional credit risk scoring models. However, in emerging economies, the underbanked, widows, small and medium enterprises owners and youth are not able to access traditional forms of collateral or identification required by financial institutions due to lack of data for them to get access to loans. Financial institutions cannot obtain much of the information required about an applicant and they use alternative data sources such as public data or social media to deal with the problems of information asymmetry, adverse selection and moral hazard. To eliminate such problems, credit risk assessment models must be built on the foundation of artificial intelligence and machine learning approaches in an effort to perform an accurate credit risk analysis, to properly assess the behaviour of the customers and subsequently perform a thorough verification of the potential of the applicant to repay the loan thereby allowing less privileged people to access credit. However, big data sourced from the internet and public data sources is characterized by huge volume, variety, veracity, the curse of dimensionality, class imbalance, concept drift and non-linearity among others. These challenges affect the generalization performance of credit risk assessment models used by most financial institutions. This paper proposes an efficient and effective credit risk assessment model capable of learning incrementally based on machine learning algorithm called Adaptive Heterogeneous Dynamic Ensemble Selection (AHDES) that uses big data from alternative sources for financial for the underbanked. The algorithm is agile and adaptive to unexpected world events, changes in customer behaviour and cognitively counter bias likely to be introduced by information asymmetry. Experimental results on four large credit datasets and five evaluation metrics show that our proposed algorithm performs better than other selected benchmark models allowing the underbanked and less privileged to access loans to stimulate economic growth.
This study explores the use of Explainable Artificial intelligence techniques to improve the interpretability of credit default prediction and highlights the practical value of explainable machine learning in developing more understandable, trustworthy, and accountable credit risk assessment systems for real-world financial decision-making.
Muskan, B. Sidhu· International Journal of Com...· 0 citations
With the rapid development of FinTech, commercial banks are facing an increasingly complex credit environment. Traditional credit risk assessment models struggle to meet the processing demands of massive and multi-dimensional data. Big data technology provides new perspectives and tools for bank risk control. Based on the theoretical mechanism of big data risk control, this paper constructs a multi-dimensional indicator system including demographic characteristics, asset status, and behavioral preferences. Using the personal credit dataset of a domestic commercial bank, Logistic Regression (LR) model and XGBoost machine learning model were established for empirical comparative analysis. The results show that introducing big data variables and adopting the XGBoost model can significantly improve the accuracy and AUC value of default prediction, effectively reducing the credit default risk of commercial banks. Finally, countermeasures are proposed to address problems such as data silos, weak model interpretability, and privacy protection.
Jia-Chen Yan· Asia Pacific Economic and Ma...· 0 citations
Credit risk in business-to-business (B2B) transactions can threaten financial stability if it is not managed effectively. This study evaluates the use of machine learning (ML) methods to predict credit risk in B2B transactions, using 4,828 observations from large companies in Bosnia and Herzegovina over a five-year period. Three ensemble-based ML models (Bagging Decision Tree, Random Forest, and Gradient Boosting) were compared with logistic regression. All ML models showed strong predictive performance, with Gradient Boosting performing slightly better overall. Liquidity, activity, and leverage indicators were the most important predictors across all models, while non-financial variables made only a limited contribution. The findings highlight the importance of accounting information in credit risk assessment and are relevant to IFRS 9, where probability of default is a key input to expected credit loss estimation under the general approach. These results provide a basis for further development of credit risk models for non-financial companies.
Suzi Mikulić· Ekonomska Misao i Praksa· 0 citations
A systematic literature review of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026, synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment.
Bolun Zhang, Jun Luo, Ruobing Wu et al.· Journal of Risk and Financia...· 0 citations
The study focused on developing a creditworthiness prediction model utilizing artificial neural network. Credit risk evaluation has a relevant role for financial institutions, as lending could result in real and immediate losses. In particular, default prediction was one of the most challenging activities in managing credit risk. The objective was to enhance the accuracy and reliability of credit risk assessments by leveraging the computational power and learning capabilities of artificial neural networks. The parameters of the dataset include the customer's place of work, loan history, monthly salary, loan amount, transaction history, and credit history, all stored in the trained database. When a customer comes to apply for a loan, the system checks if the user is qualified based on these parameters and then approve or disapprove the loan accordingly. Rigorous testing and validation were conducted to ensure the model's robustness and generalizability. The results demonstrated that the neural network-based model significantly outperformed traditional statistical methods, providing more precise predictions of creditworthiness. An Object-Oriented Analysis and Design Methodology (OOADM) approach, which incorporated Unified Modeling Language for analysis and design, was used. The development stage was completed using a set of software tools, including Python and the MySQL database system.
E. C., Uzo Blessing Chimezie, Ukekwe Emmanuel C· International Journal of Lat...· 0 citations
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.
L. O'Connor· International Journal of Art...· 0 citations
