How Insurers Can Tackle Operational Drag with Signal Intelligence

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Insurance carriers today operate in an environment marked by rapid digital transformation, heightened customer expectations, and increased regulatory scrutiny. Yet despite heavy investments in modern core platforms, significant operational inefficiencies persist—often silently eroding margins and service quality. The issue isn’t just in what gets done—but how disconnected functions execute decisions in isolation.

“The most critical breakdowns occur not at the point of failure, but in the disconnect between actions and their broader context,” says Abhishek, Senior Vice President at Xebia.

The Real Cost of Disconnection

These breakdowns often occur across underwriting, claims, and customer experience. Underwriting frequently begins with incomplete and unstructured data, forcing underwriters to make judgment calls without full visibility into historical claims or customer interactions. Claims teams process cases without insight into underwriting intent or risk appetite. Customer experience teams manage escalations without knowing whether the root cause lies in product design, misclassified risk, or a systemic flaw.

“The consequence is silent margin erosion—not from fraud or policy leakage, but from processing the same issue multiple times across different teams, each without shared context,” explains Abhishek. “Carriers can absorb $4 million to $7 million annually in preventable costs simply due to misrouted signals and context-poor decisions.”

Solving this requires not just automation—but intelligent orchestration that connects decisions across the insurance value chain.

Why Modern Systems Aren’t Enough

Despite core systems becoming more advanced, inefficiencies persist. According to Abhishek, “These platforms optimize execution, not decision accountability.” CRMs log complaints. Claims engines process transactions. Underwriting platforms apply rules. But no system enforces who made a judgment call, or why an override occurred.

The result? Fragmentation becomes normalized. Each function assumes the task is complete, even if the problem remains unresolved. Human judgment or static logic often still governs signal routing, leaving room for gaps and missed context.

“Until telemetry is embedded at the signal level—capturing who made which decision, when, and why—modernization only speeds up fragmented execution rather than fixing its flaws,” says Abhishek.

The Cost of Functional Silos

Functional silos delay decisions, generate rework, and deliver inconsistent service—not due to individual inefficiency, but unclear ownership across handoffs.

Abhishek outlines a typical scenario: “A customer complaint might sit idle in the CRM because it isn’t properly tagged. Meanwhile, underwriters receive massive files without summaries. Risk teams intervene late. Claims teams apply overrides without documentation. When audits happen, no one can reconstruct the decision logic.”

Each step may appear compliant, but the full journey is riddled with friction.

“It’s not a failure of people—it’s a failure of connected ownership and contextual continuity,” Abhishek emphasizes.

Turning Data Into Actionable Signals

Insurers don’t suffer from a lack of data. They suffer from a lack of signal intelligence.

“A signal isn’t just a data point—it’s an insight with intent,” says Abhishek. “But tagging at intake is minimal, scoring is inconsistent, fallback logic is weak, and telemetry is often incomplete.”

Without structured signal intelligence, teams respond to visible problems in isolation—missing early intervention points and converting preventable issues into high-cost escalations.

Case in Point: Orchestration in Action

Xebia recently led a three-phase execution framework for live claims operations—without changing any core platforms.

“We focused on orchestrating flow, enforcing ownership, and surfacing actionable insights,” explains Abhishek.

Phase 1: GenAI Summarization & Auto-Tagging
GenAI Summarization & Auto-Tagging: We overlaid GenAI on incoming claims documents to extract summaries and auto-tag them by urgency, policy ID, and LOB. This reduced underwriting decision time by over 40%, not because the underwriting process changed, but because underwriters no longer had to sift through irrelevant data.

Phase 2: Override Accountability
Each manual override required a documented rationale, tied to the user ID and timestamped. Compliance overhead dropped significantly—audits no longer required chasing override trails.

Phase 3: Signal Heatmaps & Root-Cause Detection
By analyzing escalated complaints and rejected claims, a cluster linked to a product configuration flaw was identified. What had been manually escalated multiple times finally triggered a targeted product fix.

“No new systems were introduced. We simply connected what already existed,” says Abhishek. “The measurable impact came from better handoffs, enforced data capture, and traceable execution.”

What Technology Works—and Why

“The most effective tools don’t just automate—they guide, trace, and enforce decisions in real time,” says Abhishek.

Technologies delivering the most impact include:

• GenAI-Powered Summarization: By converting lengthy documents into concise, decision-ready briefs, GenAI summarizers reduce triage and underwriting time by up to 60%. But their true value is unlocked only when tied to confidence scoring and fallback logic—ensuring accuracy, traceability, and trust.
• Inbox AI for Intake Management: Intelligent tagging of complaints at the point of entry—based on line of business (LOB), urgency, and policy ID—prevents them from sitting idle in generic queues. This accelerates routing and ensures that escalations are not missed.
• Comms Copilots for SLA-Aligned Updates: These tools automate communication updates based on product tone, regulatory templates, and SLA commitments. This eliminates reliance on manual follow-ups by CX teams and ensures consistent compliance-driven closure messaging.
• Execution Telemetry: By tracking signal flow across systems, telemetry tools highlight issues like idle case signals, broken escalation loops, or decisions that weren’t logged—in real time. This visibility allows for proactive resolution before service degradation occurs.

“Each tool embeds directly in the workflow—eliminating friction without creating new gaps,” notes Abhishek.

Compliance Built In, Not Bolted On

Scaling AI and automation must go hand-in-hand with embedded governance. “Compliance can’t be something you audit after the fact—it must be enforced at the moment of execution,” says Abhishek.

Key mechanisms include:

• GenAI Confidence Controls: If a GenAI-generated summary doesn’t meet predefined confidence thresholds, the system automatically triggers fallback workflows or manual verification prompts, preventing unreliable data from driving downstream decisions.
• Enforced Rationale for Overrides: Manual override decisions cannot proceed unless accompanied by a documented rationale, which is versioned, timestamped, and tied to the user ID and case file—eliminating gaps in audit trails.
• Escalation Governance: Every escalation is tagged by severity, timestamped, and tracked via SLA timers, ensuring regulatory service timelines are met and that escalations receive consistent attention.
• Signal Accountability: Every decision-driving signal—whether from a complaint, risk event, or customer action—is scored, routed, and stored in a traceable format. No signal is lost, dropped, or needs to be reconstructed during audits.

“With these mechanisms, you’re not just scaling operations—you’re scaling control,” says Abhishek. “You reduce regulatory risk and increase audit readiness without slowing down execution.”

“Fixing insurance operations isn’t about replacing core systems. It’s about connecting decisions, enforcing traceability, and embedding intelligence in the execution layer. Automation alone isn’t enough—what matters is contextual orchestration across the value chain,” Abhishek concludes.

To truly transform insurance, organizations must shift focus from functional excellence to connected execution—where every decision, action, and signal is visible, accountable, and intelligently routed.

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This article is part of the MIT SMR CONNECTIONS thought leaders briefing, “Intelligent Execution: Closing Margin Leaks Across Underwriting, Claims, and CX”, presented in collaboration with Xebia and AWS.