Mathan N

The integration of Artificial Intelligence (AI) and Knowledge Management (KM) structures have emerged as a powerful strategy to streamline business processes and gain a competitive edge. The primary objective is to examine how the integration of AI and KM improves business efficiency, fosters innovation, and enhances competitive advantage across various industries, providing insights into the measurable benefits. Key variables include the level of AI integration, knowledge management (KM) effectiveness, business process efficiency, competitive advantage, and employee satisfaction. These factors were measured using standardized scales to determine their interrelations and impact on business performance. Data was collected using surveys from 750 employees across 10 companies, alongside 15 interviews with senior managers. SPSS was used to analyze quantitative data. SPSS was also used for correlation, regression, and descriptive statistics of key variables. There are strong positive correlations between business process efficiency and AI integration level (0.64) as well as between competitive advantage and KM effectiveness (0.67), and the results show that all variables have high mean scores, with business process efficiency having the highest mean (4.22) and employee satisfaction having the lowest (3.98). The investigation concludes that integrating AI technologies with KM systems significantly improves business process efficiency and provides a competitive edge. Organizations should prioritize these integrations to stay competitive, though challenges such as resistance to change must be managed.

Risk prediction is crucial in corporate financial management, influencing strategic decision-making, investment optimization, and proactive mitigation. Traditional models struggle to handle modern financial data's complexity, nonlinearity, and temporal volatility. To address these limitations, this research proposes an advanced, intelligent risk prediction framework based on a Siberian Tiger Optimization-driven Temporal Convolutional Neural Network (STO-TCN). The framework is specifically designed to improve predictive accuracy, adaptability, and robustness in fluctuating financial environments. Research utilizes a comprehensive dataset comprising publicly available corporate financial statements, stock exchange disclosures, and macroeconomic indicators across diverse industry sectors. To enhance data integrity and model performance, two preprocessing techniques were applied: z-score standardization to ensure uniform data scaling and outlier detection to minimize the distortion caused by anomalous data entries. The TCN component effectively captures sequential patterns in financial time-series data, while the STO algorithm optimizes model hyperparameters and network weights, accelerating convergence and reducing overfitting. Experimental results demonstrate that the STO-TCN framework significantly outperforms traditional models with an accuracy of 0.9844, particularly in highly dynamic market scenarios using Python. This predictive framework offers a scalable and adaptive solution for corporate financial risk assessment, with practical applications in investment planning, regulatory compliance, financial governance, and enterprise sustainability. Further investigation incorporates real-time data streams and evaluates performance in small and medium-sized enterprises (SMEs) to broaden its applicability.