Why AI matters in insurance
Insurers face relentless pressure to stay profitable in a highly competitive market while balancing compliance, claims efficiency, and customer trust. Yet, fragmented policy data, legacy systems, and manual validation keep decisions slow and reactive. Modern insurers must use AI to unify information, automate decisions, and ensure every process—from underwriting to settlement—is explainable, auditable, and cost-efficient. Further, insurers can leverage the domain model to predict risk and detect fraud with greater accuracy.
Across the insurance landscape:
• 60% of insurers lack a single view of customer & policy data
• AI reduces claims cycle times by up to 70%
• Fraud detection even more difficult with deepfake
• 1 in 3 customers switch providers due to poor digital experience
• Compliance costs consume 20% of operating budget
Every function did its job. The decision still took too long
Underwriting had its model. Claims had its workflow. Servicing had its data. Fraud had its signals. But the decision still depended on someone moving information between them.
BigTapp connects the data, knowledge, AI and workflows behind these functions, so insurers can move from fragmented intelligence to coordinated decisions and action.
Start small. Prove value. Scale what works.
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Identify
Select a high-value insurance problem with clear business KPIs.
Prove
Build a focused MVP or production pilot with the required data, controls and integrations.
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Operationalize
Embed AI into the actual insurance workflow, not just a standalone demo.
Scale
Reuse agents, data products, connectors and accelerators across adjacent use cases.
Compound
Build toward a broader AI-first operating model as successful use cases accumulate.
AI across the insurance lifecycle
From acquiring the right customer to underwriting, servicing, claims and renewal, put intelligence where insurance decisions happen.
Customer 360 & Personalisation
Policy, claims and interaction data unified on the insurance reference data model, so underwriting, claims and servicing agents all mean the same thing by customer, coverage and exposure.
Fraud Detection & Risk Analytics
Predictive models and graph analytics surface suspicious claims and portfolio concentrations that no single case file reveals, putting the pattern in front of an investigator early.
Agent Co-Pilot
Real-time client context, product fit and next best action assembled for the agent with the reasoning attached, so the conversation starts from a recommendation rather than a search.
AI-Driven Underwriting
Submissions pre-screened and validated against rules and history, returning a priced, evidenced recommendation for an underwriter to accept or overturn, cutting turnaround by up to 60%.
Claims Automation
Documents captured, validated against policy and routed to settlement through agents that hand work between them, pausing at the approval limits you set. Three times faster, with fraud losses down.
Regulatory & Compliance Automation
Policies and claims continuously reviewed against regulatory checklists, with audit-ready evidence assembled as gaps appear rather than before an exam, fully traceable.
When AI needs better data, we build that too
Reference Data Model
Common business language across customers, policies, coverage, claims and risk.
Data & Platform Engineering
Cloud data platforms, integration, migration, governance and modernisation.
Intelligent Document Processing
Turn policy documents, claim files, medical reports and submissions into decision-ready data.
Purpose-built AI for insurance's hardest jobs
Named solution packs, built on Beeonix, BigTapp's agentic AI platform, and governed by design. Each turns a priority insurance use case into a running system, across sales, service, claims, compliance and leadership.
From insurance use cases to production AI
Solution Packs, Accelerators and Beeonix work together to help insurers move from a priority use case to measurable business value, faster and with lower risk.
Numbers over narratives.
Real outcomes from insurers, across claims, investment analytics and customer experience.
