AI in Claims Is Proven. Scaling It Is the Real Work.
- AI leaders in insurance generate 6.1x the total shareholder return of laggards – yet only 7% of carriers have moved AI beyond pilots.
- The gap isn’t strategy or budget. It’s the application architecture connecting AI ambitions to live claims operations.
- What follows will show you five specific ways application engineering is closing that gap – and what to ask your teams and partners next.
The case for AI in insurance claims is no longer theoretical. According to McKinsey, AI leaders in insurance generate 6.1x the total shareholder return of their laggard peers, with domain-level AI rewiring delivering a 3-5% improvement in claims accuracy and up to a 40% reduction in customer onboarding costs, among other measurable gains.” This preserves accuracy and adds “among other measurable gains” to signal it’s part of a broader list. Yet BCG finds that only 7% of insurers have successfully brought AI to scale – most remain stuck in pilots.
The bottleneck isn’t the AI model. It’s the application architecture sitting underneath it.
What follows will show you five practical ways application engineering is transforming insurance claims – from automation priorities and cloud design to data governance and partner selection – so you can move from intention to execution.
1: Scaling AI in Claims: From Pilot to Production-Grade Platform
Most carriers have run a successful claims AI pilot. Few have scaled it. The difference lies in how the underlying claims application is built.
Production-scale AI requires modular claims components, clear separation between AI model services and orchestration layers, and event-driven workflows that allow progressive activation. Start with assist-only mode – where AI surfaces recommendations and adjusters approve – before moving to straight-through processing for eligible claim types.
CIOs should push for application development and modernization patterns that treat AI as a service your platform calls, not a feature bolted onto a legacy core. That distinction determines whether your AI investment compounds or stalls.
2: Automate the Right Claims Steps First – and Protect the Rest
Not every claims task should be automated at the same time – or at all.
Automate first (high volume, low variance, manageable regulatory exposure):
- FNOL intake and acknowledgment
- Document ingestion and classification
- Basic eligibility and coverage checks
- Routine payment triggers
Keep human-led (high severity, contested, or legally sensitive):
- Complex liability decisions
- Multi-party subrogation
- Any claim where explainability is a regulatory requirement
Consider a mid-size P&C insurer automating FNOL intake. Adjusters stop spending time on data entry and acknowledgment emails – redirecting that capacity to contested claims requiring judgment. BCG finds that leading firms equipping service and operations employees with AI-empowered tools bolster productivity by more than 30%.
Scalence’s Robotic Process Automation capabilities are designed to automate exactly these high-volume, rules-based claims steps – with human-in-the-loop escalation paths built in from day one.
3: Modernizing Claims in the Cloud Without a Costly Lift-and-Shift
Many insurers moved claims workloads to the cloud and found costs rose rather than fell. The reason is almost always the same: they relocated existing architecture instead of re-engineering it.
Cloud-native claims platforms require microservices, API-first design, and event-driven communication between services. Without these, you’re paying cloud prices for on-prem behavior. FinOps practices – resource tagging, team-level chargebacks, and decommissioning discipline – prevent the “all-you-can-eat” overprovisioning that quietly erodes ROI.
Scalence’s Cloud Services and Platform Monitoring and Management practices are built around exactly this problem: keeping cloud-hosted claims applications performant, cost-controlled, and operationally transparent.
4: Build a Claims Data Platform Regulators and Adjusters Can Both Trust
PwC’s 2025 Global Actuarial Modernization Survey found that 94% of insurers cite efficiency as their primary modernization driver – yet only 42% have a single source of truth for data, and actuarial teams still spend more than half their time on data preparation.
Without unified, governed data, AI-assisted claims decisions are inconsistent and hard to audit. Regulators will ask: who decided what, and based on which data?
Application engineering answers that question by embedding governance directly into the claims platform: data lineage tracking, decision logs, override mechanisms, and clear source-of-truth layers across policy, claims, and payment systems. Scalence’s Data Governance and Compliance capabilities are designed to make this auditability operational, not aspirational.
5: Choosing an Application Engineering Partner Who Can Actually Deliver
Strategy documents don’t process claims. Execution does.
When evaluating an application engineering partner, ask five questions:
- Do they have a track record in insurance-specific claims workflows – not just general digital transformation?
- Can they demonstrate a platform-agnostic architecture approach that avoids lock-in?
- How do they embed data governance and security into the application layer, not just policy documents?
- Do they support SRE, observability, and operational resilience post-launch?
- Can they flex between build-operate-transfer, managed services, and hybrid models as your needs evolve?
The right partner doesn’t sell you a platform. They help you build one that fits your operating model, regulatory environment, and long-term data strategy. Explore how Scalence approaches this and browse client success stories across financial services and complex engineering environments.
Ready to Move From Pilot to Production?
Insurers that treat claims modernization as a technology upgrade tend to stall. Those that treat it as an architecture and operating model decision – supported by the right engineering partner – are the ones generating measurable gains in efficiency, accuracy, and shareholder value.
If your claims AI investments aren’t yet delivering at scale, the architecture deserves a closer look. Talk to our team or reach us at inquiries@scalence.com to map your current state and outline a practical modernization roadmap.
FAQ: Executive Questions on Claims Application Engineering
What should CIOs prioritize before scaling AI in claims?
Fix data fragmentation first. AI performs inconsistently when claims, policy, and payment data live in separate systems with no unified layer. Establish a governed data foundation before expanding AI scope.
What claims tasks are too risky to automate?
Any decision requiring legal defensibility, multi-party liability assessment, or regulatory explainability should remain human-led. Automate the workflow around those decisions, not the decisions themselves.
Who is accountable when an AI-powered claims workflow makes a wrong decision?
Accountability stays with the insurer. Application engineering must build in decision logs, override mechanisms, and model explainability so your teams can demonstrate – to regulators and claimants – how every decision was reached.
What FinOps practices matter most for cloud-based claims platforms?
Resource tagging by claims function, team-level cost visibility, automated alerts for overprovisioning, and a regular decommissioning cycle. Governance prevents cloud spend from drifting past projections.
