AI-Powered Data Governance for Insurance: A Comparative Tool Evaluation

Аннотация

As insurers are increasingly utilizing artificial intelligence for underwriting, pricing, and claim processing in an automated manner, end-to-end, open, and industry-level data governance solutions became the top priority. Although numerous AI-driven governance technologies are available, they are mostly purpose-built for generic corporate requirements and do not entirely meet the decision-making-oriented, ethics-conscious, and regulation- compliant insurance industry requirements. This paper presents a comparative evaluation of six top governance platforms—Collibra, Informatica CLAIRE, BigID, Immuta, IBM Watson Knowledge Catalog, and Alation—on eight dimensions, such as explainability, consent management, and insurance-specific flexibility. The research also illustrates the industry specific adoption of AI driven data governance in finance, health care and insurance along with a comparative insight amongst the three most data centric industry. The study reviews insurance governance practices to assess capability gaps in the existing available commercial tools and strategic recommendations to insurers and tech vendors. This paper provides the basis for building AI governance systems that are compatible, scalable, fair, transparent, and flexible to the specific working context of the insurance data universe by overcoming technical limitations and moral dilemmas. KEYWORDS AI Governance, Insurance Technology, Data Governance Tools, Decision-Centric Automation, Regulatory Compliance, Ethical AI 1. INTRODUCTION The insurance sector is undergoing a gigantic digital revolution on the heels of advances in artificial intelligence (AI), data analytics, and cloud computing. AI is being used to apply to core processes such as underwriting, policy tailoring, claims payment, and fraud detection, which had previously been dominated by regulations and human controls [1,2]. Though these innovations hold the promise for insurers of enhanced efficiency, risk sensitivity, and consumer participation, they also present a new crop of governance issues, such as data ethics, algorithmic explainability, and regulatory compliance [3,4]. The crux of such development lies in the question of trust. Therefore, it is required that decisions offered or enabled by AI systems are explainable, understandable, and amenable to analysis compared to those generated by human beings. Insurers' stakes are extremely high. Eligibility, premium, and payments on claims decisions affect policyholders' financial well-being directly. Consequently, insurers are being increasingly pressured by government regulators, industry regulators, and customers to ensure that their AI systems do not only work, but also that they are transparent, fair, and aligned with legal requirements and ethical guidelines [5,6]. https://www.academicpublishers.org/journals/index.php/ijdsml 280 AMERICAN ACADEMIC PUBLISHER In response to these pressures, a number of differing AI-driven data governance systems have evolved. These tools claim to automate policy enforcement, anomaly detection, data lineage monitoring, and consent workflow management. All of these tools, however, have been designed as horizontal corporate solutions, and they are targeted at banking, healthcare, and so on structured data domains. However, these AI-powered data governance platforms are also used in insurance As a result, their capacity to meet the special insurance industry governance requirements of fractured legacy systems, behavior data sources, and context-driven decision-making is largely unproven. This study aims to address the gap by providing an assessment comparison of top AI-powered data governance platforms in the insurance data ecosystem. It examines the efficiency of such tools in driving key governance features, including metadata control, explainability, consent life cycle monitoring, and regulatory compliance, and capability deficits that are likely to impede their maximum functionality within insurance contexts. Literature review, industry analysis, and tool-level analysis are applied in the research to identify gaps in current governance tools and invoke a beneficial framework for insurers and vendors to collaborate to formulate open, decision-oriented, and ethically suitable governance frameworks. The result is intended to guide technological strategy and policymaking in AI-based insurance business. 2. METHODOLOGY The study employs the comparative analysis method shown in Figure 1. to liken the landscape of AI-powered data governance solutions and ascertain the prospects for their adoption in the insurance data context. Growth in volumes of data and complexity of data in insurance activities—from underwriting to claims settlement, consumer risk scoring, and reporting to the regulator—make advanced, dynamic, and open governance models a necessity. The general aim of this method is to develop a structured and reproducible model enabling comprehensive comparison of the current tools and sugg