Explore research-led perspectives on machine learning, ontology engineering, knowledge discovery and data intelligence — enhanced with an Agentic AI lens for modern insurance and financial-services experiences.
Six featured articles from the latest InsureBuddy collection.
HyperpersonalisationTurn behavioral signals into context-aware recommendations and next-best actions.
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WalletConnect microinsurance, payments and intelligent service in one digital journey.
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Digital InsuranceExplore immersive engagement, digital humans, virtual risk and new insurance experiences.
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Composite AITurn conversational feedback into a continuous Voice-of-Customer intelligence loop.
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Insurance CXMove complex conversations to the right human with context, sentiment and skill-aware routing.
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Composite AICombine multiple AI capabilities and domain intelligence to modernize insurance operations.
Read article →Knowledge EngineeringOntology forms a key emerging domain for organizing, managing and understanding information. The paper reviews machine learning methods used in ontology engineering and identifies practical approaches for applying ML to complex information repositories.
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Domain IntelligenceThe research addresses the manual effort involved in creating, validating and updating domain ontologies. It proposes machine-learning algorithms to automate ontology generation and continuous maintenance, reducing the cost and time required for domain knowledge engineering.
Explore paper →Applied AnalyticsData mining extracts useful information and patterns from large volumes of data. The paper examines Knowledge Discovery in Databases and compares classification, clustering and regression approaches, including their advantages and limitations.
Explore paper →Knowledge DiscoveryThe research presents a machine-learning model for automated knowledge acquisition across multiple domains. It uses natural language processing and data extraction to populate ontologies, classify instances and support relationship extraction, indexing, mapping, knowledge discovery and rule generation.
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Semantic IntelligenceThe proposed methodology uses semantic and thematic graph generation, data mining and neural-network techniques to extract useful knowledge, improve retrieval and construct domain-independent hierarchical structures.
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