Ameya Ambulkar

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.

Background: In the current digital economy, integrating cutting-edge technology into company operations has become essential to long-term sustainability and competitiveness. The term Digital Transform (DT) describes the deliberate use of digital technologies and processes to boost consumer engagement, operational effectiveness, and innovative capacity. For small and medium-sized enterprises (SMEs), DT presents both opportunities and challenges due to controlled resources and structural limitations. Objective: To explore how technological, organizational, and environmental factors collectively influence the success of DT in SMEs was the primary objective of this research. Method: an organized questionnaire collected data from 192 SME representatives across several industries. Using structural equation modeling (SEM), the research tested the relationships among particular variables. The model combined key constructs such as IT infrastructure readiness (ITIR) digital leadership (DL), organizational agility (OA), regulatory support (RS), market pressure (MP), workforce adaptability (WA), and Digital Transformation success (DTS). The DT process was assessed based on criteria that included the incorporation of digital platforms, real-time data utilization, and customer-driven innovation. SPSS and AMOS were utilized for data analysis, containing reliability tests, confirmatory factor analysis, descriptive statistics, and path analysis. Result: Results discovered that organizational agility (β=0.38, p<0.01) and IT readiness (β=0.33, p<0.01) had the most substantial effects on transformation success, with external market pressure (β=0.27, p<0.05) also playing a meaningful role. Digital leadership was found to significantly mediate organizational capabilities. Conclusion: These findings highlight the importance of internal capabilities supported by external drivers in shaping successful transformation outcomes.