This study, titled "Predictive Analytics for Loan Default Risk Assessment in Commercial Banks," evaluates the predictive accuracy, loan portfolio risk composition, Non-Performing Asset (NPA) reduction trends, and financial feasibility of advanced machine learning risk scoring engines in commercial banking. Commercial banks face significant credit risk exposure, with unsecured personal loans representing 40% and MSME business loans accounting for 30% of portfolio volume. A five-year project lifecycle (2021-2025) of an ensemble predictive analytics platform is evaluated using standard capital budgeting parameters: Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR). Quantitative analysis indicates that deploying ensemble neural network classifiers raises default risk ROC-AUC scores to 0.96 compared to 0.72 under traditional logistic regression models. Higher predictive precision improves default detection accuracy from 68.5% to 96.2%, reducing annual credit loss costs from 480 Crores to 65 Crores and lowering Gross NPAs to 320 Crores while achieving an 89.5% Provision Coverage Ratio (PCR) by 2025. The financial model yields a positive NPV of 284.5 Crores and an IRR of 38.6%, far exceeding the 10% discount hurdle rate. The study concludes that investing in predictive credit risk analytics is highly viable, providing commercial banks with enhanced asset quality, lower provisioning overhead, and improved capital adequacy.
Pamarthi Sai, M. Prasad, B. Vijay· American Journal of AI Cyber...· 0 citations
Artificial Intelligence (AI) has emerged as a transformative technology in the banking and financial sector, enabling institutions to improve the accuracy, speed, and reliability of credit risk assessment. Traditional credit evaluation methods often rely on limited financial indicators and manual decision-making processes, which may result in delayed approvals and inaccurate risk predictions. This study examines the application of AI-based techniques, including Machine Learning (ML), Deep Learning (DL), and predictive analytics, in assessing the creditworthiness of borrowers. AI models analyze large volumes of structured and unstructured data, such as credit history, transaction patterns, income details, repayment behavior, and alternative financial indicators, to identify potential default risks with greater precision. The research highlights how AI-driven credit risk assessment enhances loan approval decisions, minimizes non-performing assets (NPAs), reduces operational costs, and supports financial institutions in maintaining regulatory compliance. Furthermore, the study discusses the challenges associated with AI adoption, including data privacy, algorithmic bias, model interpretability, and cybersecurity concerns. By integrating intelligent risk assessment frameworks into lending operations, banks can strengthen financial stability, improve customer experience through faster loan processing, and achieve more effective risk management. The findings indicate that AI-based credit risk assessment represents a significant advancement over conventional credit evaluation methods and has the potential to reshape the future of banking by promoting efficient, transparent, and data-driven lending decisions. Keywords: Artificial Intelligence (AI), Credit Risk Assessment, Machine Learning, Banking, Financial Institutions, Loan Default Prediction, Predictive Analytics, Credit Scoring, Risk Management, Deep Learning.
Saifanaaz, M. Prasad, T. Meghana· International Journal of AI...· 0 citations
This study, titled "Stock Market Price Prediction Using Machine Learning Algorithms for Investment Decision-Making," evaluates the economic viability and operational efficiency of implementing algorithmic prediction models in retail and institutional portfolio management. In modern financial markets, predicting stock prices is highly challenging due to nonstationarity, noise, and complex nonlinear relationships. This research investigates the implementation of machine learning algorithms—specifically Linear Regression, Support Vector Regression (SVR), Random Forest, and Long Short-Term Memory (LSTM) Networks—for real-time price forecasting. The study conducts a cost-benefit analysis of the technological investment, evaluating capital expenditure, operational maintenance costs, and the net financial benefit of stock price forecasting and portfolio optimization from 2021 to 2025. Standard financial appraisal metrics—Net Present Value (NPV), Internal Rate of Return (IRR), Payback Period (PBP), and Benefit-Cost Ratio (BCR)—are applied to determine the long-term profitability of the investment. The empirical analysis indicates that the LSTM model achieved the highest prediction accuracy, with a Mean Absolute Percentage Error (MAPE) of only 2.1%. An ML-optimized portfolio generated consistent excess returns (alpha) over the Nifty 50 Index across the five-year planning horizon, yielding a positive NPV of 392.4 Crores and an IRR of 45.1%. The study concludes that the integration of machine learning algorithms into investment decision-making processes represents a highly viable and financially feasible strategy for modern banking and asset management portfolios, delivering significant risk-adjusted financial returns.
Mukkala Aravind, M. Prasad, Srilekha Rageru· American Journal of AI Cyber...· 0 citations
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