This study evaluates AI credit-scoring models against standard statistical models using a real-life data set with 1000 loan applications. The main research question is about whether machine-learning methods are more valuable in terms of predictive accuracy, interpretability and stability to changes in the process of macroeconomic deterioration as compared to traditional methods. The four model variants were developed: the logistic regression and decision tree as examples of the classical ones, and XGBoost gradient-boosting ensemble and multilayer perceptron neural networks as examples of AI-based alternatives. The methodological engine was R (v4.3.1), which established a 70:30 train-test strata union, inner cross-validation and harmony spatial search to tune the hyperparameter. The findings show that XGBoost produces an optimal balanced accuracy cumulative gain curve (cumulative gain, CGC: 0.89; area under the receiver operating characteristic, AUC: 0.89) that is trumped by the neural network (CGC: 0.87; AUC: 0.87) and succeeded by the logistic regression (CGC: 0.76; AUC: 0.76). The SHAP analysis shows that the amount of credit, duration of loan, and age are central predictors. During stress-test simulation, XGBoost is stable in making predictions (AUC = 0.83) compared to a severe decline in logistic-regression results (AUC = 0.68). The results therefore justify the claim that the state-of-the-art AI tools have better predictive potential and robust interpretability, thus rising as viable alternatives to the systems in vogue in modern finance organisations.

Corporate fraud remains a persistent problem that highlights the need for improved internal control and governance. Research on corporate governance (CG) and forensic accounting (FA) has been largely performed as separate studies. Little has been done to look at how accountants’ knowledge and the specific training of accountants in fraud awareness for their company’s leaders affect preventing fraud (FP). The study surveyed 150 accountants in India from April 2023 to May 2024. The results are based on Chi-Square testing and binary logistic regression. The study investigated how companies in India incorporate CG policy understanding and FG use for KMP and boards and how these factors affect FP. The findings indicate that understanding CG, using FA, and having specific training on fraud awareness for KMPs and boards of directors are all significant factors in reducing the occurrence of fraud. In addition, general employee training has no impact on FP. The theories of agency, stakeholder, and fraud triangle were combined to create one model to provide guidance to both organizations and regulators on how to institutionalize FG and to improve transparency in governance.

Thanks to the accessibility of machine learning (ML) techniques, financial technologies are growing in almost every facet of finance, including asset management (robot advisors), payments (blockchain currencies), and peer-to-peer lending. High accuracy is sometimes attained by machine learning models at the price of inadequate explainability. Furthermore, high-risk AI systems dependent on ML should be “trustworthy” and adhere to a collection of obligatory standards, including sustainability and fairness, as per the proposed legislation. As of right now, no standardized measures exist that can facilitate a thorough monitoring of the reliability of AI implementations in the financial industry. To close the lag, we provide a collection of collected statistical techniques depending on the Lorenz Zonoid approach which may be utilized for evaluating and tracking the reliability of an AI application over time. In particular, the techniques will assess Explainability, Accuracy, Fairness and Sustainability.

The integration of blockchain technology (BCT) within the banking sector is transforming how organisations achieve operational efficiency and trust. However, the adoption of BCT in the Indian banking sector remains underexplored, particularly regarding organisational alignment and performance. This study explores the critical factors of technology and organisation influencing the adoption of blockchain and its effects on banking performance, with a distinct interest in the moderating effects of IT alignment. Also, to explore whether perceived usefulness and perceived ease of use act as mediators between blockchain technology and banking performance. Using the Technology-Organisation-Environment (TOE) and Technology Acceptance Model (TAM), data were collected from 326 professionals working in prominent Indian banks and FinTech companies. Analysis of the relationships was conducted with Structural Equation Modelling (SEM). The results of this study show that technology readiness significantly influences both perceived usefulness and perceived ease of use, while the regulatory environment significantly affects perceived usefulness. Furthermore, IT alignment significantly moderates the relationship between blockchain technology and business performance, and perceived usefulness serves as a significant mediator, whereas perceived ease of use does not significantly mediate this relationship. The study provides a theoretical contribution by incorporating the TAM and TOE models in a banking context and serves to inform practitioners by providing insights into blockchain adoption strategies in Indian financial institutions.

Operational efficiency and trust within the sphere of banking sector organizations is transformed by the integration of blockchain technology (BCT). Organizational performance and alignment though the implementation of BCT is still underexplored in the context of Indian banking sector. This study is an attempt to dive into the adoption of blockchain technology and its effects on banking performance keeping the moderating effects of IT alignment as one of the central factors to explore. The study will also take path to examine whether the relationship between blockchain technology and banking performance is moderated by the perceived usefulness and perceived ease of use. The sample size of 326 professionals working in prominent Indian banks and FinTech companies was taken, and data was collected using Technology-Organisation-Environment (TOE) and Technology Acceptance Model (TAM). Structural Equation Modelling (SEM) have been used to study the relationship between variables. The study has illustrated that blockchain performance is significantly affected by trialability and perceived ease of use, while perceived usefulness and cost savings have minimal direct impact. The study also revealed that IT alignment plays significant moderating role. The study is a significant addition to the theoretical base using the TAM and TOE models in the context of banking and will serves as an informative guide for the practitioners in the field of blockchain adoption strategies in Indian financial institutions.

This study looks at the performance of present systems and a proposed AI driven hybrid architecture using crucial performance measures like response time, load flexibility, and data quality for integrating circular economy principles into intelligent systems. The study found that traditional systems have trouble with efficiency and flexibility, but the hybrid method, which uses more than one technology, greatly enhances performance of circular economy analytics. AI-driven hybrid design framework is better than its competitors because it is more accurate, responsive, and able to handle changing workloads in machine learning based decision system. If you need to quickly learn data or make a choice, these changes will be quite beneficial.