Sarcouncil Journal of Engineering and Computer Sciences

Abstract: Artificial intelligence technologies have unnaturally converted credit analytics through sophisticated machine learning algorithms that enhance the probability of delinquency estimation across different geographical regions and portfolio parts. Contemporary AI-driven credit scoring systems demonstrate superior performance compared to traditional statistical types by incorporating vast datasets, indispensable information sources, and advanced logical ways able to make complex non-linear connections between borrower characteristics and delinquency risk. Regional adaptation strategies enable fiscal institutions to customize models for advanced requests with expansive data scarcity and strict nonsupervisory conditions, while emerging requests profit from innovative approaches exercising indispensable data sources, such as mobile phone operation patterns, mileage payment histories, and social media activity to compensate for limited traditional credit bureau information. Advanced AI ways, including deep learning infrastructures, ensemble styles, and resolvable AI fabrics, give enhanced predictive capabilities while maintaining nonsupervisory compliance through transparent decision-making processes. Portfolio-specific customization enables acclimatized results for retail consumers advancing through real-time transactional analysis, SME lending through cash inflow and network analysis, and commercial credit assessment through comprehensive fiscal statement integration with macroeconomic pointers. Perpetration challenges encompass data quality operations, bias discovery and mitigation, and integration with banking structure, taking comprehensive governance fabrics that balance innovation with non-supervisory compliance scores while ensuring equal access to credit across different demographic groups and request parts.