This paper shows how Big Data Analytics (BDA) and Artificial Intelligence (AI) automation facilitate regulatory compliance in Finance. Regulatory compliance is essential in helping institutions to mitigate reputational, litigation, and financial risk. Existing literature reveals several preconditions for compliance. However, much of the literature has adopted an internal view of compliance without considering external regulatory frameworks. This research draws on the cognitive model of regulation that looks at regulatory compliance as a social construct. It uses a triangulation research method comprising literature review, interview of trade compliance experts, and questionnaire survey of compliance practitioners to understand how regulation affects compliance and what role ICTs play in implementing compliance. The findings of this study present a regulatory compliance framework comprising four cognitive stages and a conceptual regulatory compliance system that presents how BDA and AI automation are applied to mitigate regulatory complexity and enhance regulatory compliance. The conceptual regulatory compliance system shows how BDA and AI enable institutions to dynamically assess regulatory risk, automatically monitor compliance, and intelligently predict risk violations mitigating regulatory complexity and preventing producing unnecessary documents. It provides theoretical contributions to understanding regulatory evolution and compliance and practical implications for understanding how regulation evolves to be more complicated and elements of a regulatory compliance system mitigate proliferating regulations. Additionally, it provides avenues for future research into the relationship between competing regulatory mandates and how institutions cope with that. Regulations are important for ensuring compliance and governance in finance and to curb systemic risk. Complying with regulations is difficult due to their growing volume, complexity, and fragmentation. Institutions use large-scale Information and Communication Technologies (ICTs), such as Big Data Analytics (BDA) and Artificial Intelligence (AI) automation, to monitor compliance and mitigate regulatory complexity. However, less is known about how firms comply with regulation. Most literature does not thoroughly investigate regulatory elements nor explicitly relate them to compliance.

Migrating enterprise policy systems to .NET Core is a key objective of digital transformation in the Insurance IT ecosystem. This change directly addresses strategic drivers: enabling adoption of cloud-first development, resisting market pressure for more flexible and usable enterprise solutions, and preparing for changing demands from regulation and compliance. Phases of operational benefit aligned with risk mitigation form the basis of the migration roadmap, with a strong focus on engaging all relevant stakeholders. Market pressure for a SEAMLESS user experience across ALL applications is a fundamental driver for Investment in digital transformation. Gaps remain in enterprise Operations, where Legislative and regulatory accountability Demand rigid and complex solutions that Liberty has not yet been able to provide. New risk-based capital requirements, Data-Sovereignty controls, Controls for sensitive Data in the Cloud, and new Audit requirements create a long list of challenges for the ecosystem that can no longer be Deferred. At the same time, Cross-organisational integration is becoming more important and integrating partners from the insurance supply-chain requires a much more flexible approach to development and Deployment. These factors combine to generate a credible case for accelerated digital investment with a focus on Migration to Cloud Platforms, with related Risk mitigation, Quality Improvements, and flexibility benefits that close Industry gaps.

The global banking industry is subject to ever-growing regulatory requirements, designed to prevent financial tour de force repeats tearing through the world economy. The changes are incomplete and new rules being enacted each year. Implementing and executing these rules and regulations requires the guiding principles from senior management to reach the product desks in a clear and efficient way. Technical systems must implement these rules. Differences in interpretation, implementation, and warnings must be addressed during normal operations. Most importantly, systems must provide warning alerts to management and the business as early as possible, to allow for proper handling. History has shown that the importance of early warnings has been overlooked repeatedly. Real-time capabilities are essential to meet these business needs. Organizations must therefore be ready to embrace a next-generation architecture that enables real-time alert and warning generation. Systems based on a streaming architecture, combined with systems enabling the real-time flow of events between domains supported by orchestration, provide a solid foundation to meet these requirements.