Trusted Data Foundations for Insurance & Investment AI
Keywords:
Big Data Governance Frameworks, AI/ML Data Modeling Paradigm, Non-Linear Risk Modeling, Feature Engineering in Financial AI, Data Quality Management, Ethical AI and Model Governance, Decision-Making Tool Architecture, Risk and Pricing Model Controls, Portfolio Management Analytics, Insurance Underwriting Data Governance, Model Segmentation Strategies, Data Processing Latency Optimization, Compliance-by-Design Architectures, Responsible Financial AI Systems, Technical Architecture for Big Data Decision Systems.Abstract
To address the institutional reliance on a combination of traditional data sources and poor quality alternative data, two distinct—but related—problems linked to the Governance of Big Data are analysed. In a Big Data and AI-driven world, the modelling paradigm changes from a linear perspective to a non-linear one, leading to a shift in focus: the analysis, selection and creation of model features becomes the main modelling task and modelling is performed in a segmented way. Data quality and data processing time gain special importance. Moreover, Data Governance impacts any enterprise involved in developing decision-making tools via an AI/ML Data Modeling Paradigm. Deficiencies, inadequacies, ethical concerns and governance checks of data required for the development of risk, pricing and portfolio management models for Investment Solutions, as well as for the data driving the underwriting decision process and risk scoring for Insurance Solutions, are analysed. Finally, the underlying Technical Architecture is proposed. Specific checks, balances and design controls would help Institutions to develop Big Data-based Decision-making Tools with more robust results and safer performance.
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