The digital transformation of financial services has significantly altered how insurance products are marketed, distributed, and purchased. Electronic purchasing platforms have emerged as strategic channels through which insurance providers can improve accessibility, reduce operational costs, and enhance customer experience. Despite the growing availability of digital insurance services, customer adoption remains inconsistent across markets due to technological, organizational, behavioral, and security-related factors. This study evaluates customer adoption of electronic purchasing platforms for general insurance policies within Professional Insurance Services. The research synthesizes existing literature on technology acceptance, e-commerce adoption, digital insurance systems, online consumer behavior, and organizational effectiveness to develop an integrated framework for understanding adoption behavior. A quantitative research design is proposed, employing survey-based data collection and statistical analysis to examine the relationships among perceived usefulness, ease of use, trust, website quality, perceived risk, service quality, cybersecurity concerns, and customer purchase intentions. The findings indicate that website functionality, service quality, trustworthiness, and perceived convenience significantly influence adoption decisions, while perceived risk and cybersecurity concerns negatively affect acceptance. The study contributes to the growing body of knowledge on digital insurance adoption by integrating technological and consumer behavior perspectives. Practical implications are provided for insurance organizations seeking to enhance customer engagement, improve platform design, and increase electronic policy purchases. The study further identifies strategic opportunities for Professional Insurance Services to strengthen digital competitiveness through customer-centered platform optimization and enhanced digital trust mechanisms.

The exponential growth of enterprise artificial intelligence (AI) systems has brought about new levels of efficiency in decision-making, automation, and predictive analytics in fields of finance, healthcare, and supply chain management. Nevertheless, this proliferation has also increased the fear about algorithmic responsibility, especially with regard to unclear decision-making, inherent biases, cybersecurity risks, and non-compliance with regulations. Although there has been an increasing amount of research on AI governance, the current frameworks are still highly fragmented because, in most cases, they tend to focus on risk management, cybersecurity, and ethical matters separately, but not as one system. This project will create an overall governance framework operationalizing algorithmic accountability in enterprise AI settings through a systematic combination of risk analytics, cybersecurity measures, and compliance mechanisms. The study takes a conceptual and analysis approach, which summarizes the findings of the peer reviewed articles, international regulatory guidelines, and industry best practices. Comparative analysis of existing frameworks - such as those suggested by international standard-setting organizations - has shown some essential gaps in cross-domain integration, real-time monitoring, and enforceable accountability facilities. The suggested framework presents a multi-level governance structure that aligns the technical protection with the organizational supervision and auditing of ethical processes. The integration of quantitative risk assessment models, AI-specific cybersecurity controls, like adversarial robustness and secure model lifecycle management, and embedded ethical compliance mechanisms, including fairness, transparency, and explainability, are among the key contributions. The results show that a combined governance strategy is an effective way to improve the transparency, auditability, and resiliency of enterprise AI systems. The study will make a contribution to the developing discussion of AI regulation by providing a model that is scalable and implementation-focused, which can be used to help organizations make decisions and achieve regulatory alignment. The framework offers implementable lessons to businesses aiming to use AI in a responsible manner without compromising to the new global standards.