Risk assessment is a critical component of the Software Development Lifecycle (SDLC) to ensure timely delivery, maintain quality, and reduce project failures. Traditional risk assessment approaches rely heavily on expert judgment and manual analysis, which can be subjective and prone to errors. This paper proposes a Machine Learning (ML)-based risk assessment framework for SDLC, leveraging historical project data and software metrics to predict potential risks at different stages of development. Several ML algorithms, including Random Forest, Support Vector Machine, and Neural Networks, are evaluated for their effectiveness in identifying high-risk components. Experimental results demonstrate that the proposed approach can enhance risk prediction accuracy, support proactive mitigation strategies, and improve overall software project success rates. The framework provides a scalable and data-driven solution for early risk detection in modern software engineering practices.
Arvind S. Menon, K. Raman· International Journal of Mac...· 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
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