P&C insurers are increasingly pressured to identify and effectively predict risk. While traditional methods, such as actuarial models and manual assessments, are effective for identifying patterns in large-scale policy and claims data, they struggle to capture complex patterns, like resistance curves. This paper examines how predictive risk modelling can be implemented in practice using Guidewire DataHub and Power BI Embedded Analytics. Power BI is used for interactive visualization and real-time decision support, whereas Guidewire Data Hub is utilized as a centralized platform for storing and managing structured insurance data. It utilized structured data from claim history, underwriting attributes, policy details, and customer profiles to build a predictive model. Machine learning algorithms, such as Random Forest and Logistic Regression, were then applied to classify policyholders as High, Medium, or Low risk after preprocessing and feature selection. Standard metrics (accuracy, precision, recall, ROC-AUC) were used to evaluate model performance. The Random Forest classifier achieves an accuracy of 84% and identifies high-risk profiles most effectively. It then integrated these predictions with Power BI dashboards, allowing underwriters and analysts to explore risk at both the individual and portfolio levels. The study illustrates how building data platforms that integrate machine learning and embedded analytics facilitates more innovative underwriting, fraud detection and pricing. In a competitive, data-driven insurance environment, the ability to turn raw insurance data into actionable insights provides significant operational and strategic value.

Risk Modeling, Property and Casualty Insurance, Guidewire DataHub, Machine Learning in Insurance

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