Today, Finance and Accounting (F&A) and operations leaders in insurance face extraordinary complexity. Chief Financial Officers (CFO) must move from retrospective reporting to real-time steering and dynamic capital allocation. Chief Operating Officers (COO) and Chief Underwriting Officers (CUO) need measurable gains in productivity and experience across claims, underwriting, policy administration, as well as broker and customer journeys. Chief Data Officers (CDO) and Chief Information Officers (CIO) must scale Generative AI (Gen AI) and Agentic AI from pilots to enterprise-grade capabilities, while maintaining governance and control.

This extends beyond greenfield environments to brownfield landscapes with entrenched Enterprise Resource Platforms (ERP), Policy Administrative Systems (PAS), siloed data platforms and legacy reporting

The good news: nearly every insurer faces the same challenges. Those who build an integrated, Artificial Intelligence (AI)-first operating model will lead. Research suggests that over the past 5 years, insurance AI leaders delivered 6.1x the total shareholder returns of laggards.¹

Shared Industry Challenges
The Common Starting Point

1. Fragmented ERP and PAS

Insurers run fragmented ERPs and ledgers across SAP S/4HANA, Oracle Cloud and others. Property and Casualty (P&C), Life and Annuities (L&A) rely on diverse policy and claims systems from Guidewire, Duck Creek to mainframes and proprietary platforms.

The Outcome: No unified view of premiums, claims, expenses or profitability by line, broker or portfolio; reconciliations between PAS, sub-ledgers and the General Ledger (GL) are labor-intensive; AI pilots remain narrow and unscalable.

2. Data Fragmentation Across Platforms and Tools

Even where modern data platforms exist, they are rarely unified. Business teams pull from different Business Intelligence (BI) tools, each with its own definitions and transformations.

The Outcome: Conflicting metrics for the same measures (combined ratio, loss ratio, broker profitability) force constant reconciliations across BI, data platforms, PASs and ERPs; AI models use inconsistent data, reducing effectiveness and trust.

3. Siloed Transformation and Isolated AI Pilots

Siloed pilots in finance, claims and underwriting create local wins, but without co-ordination they fragment data and priorities, widening the enterprise transformation gap.

The Outcome: Data definitions and metrics diverge across functions, while AI models are redundantly developed in organizational silos.

4. Talent and Accountability Gaps

Most importantly, no one is accountable for end-to-end orchestration. Finance, actuarial, operations, IT and data teams all own pieces of the puzzle while governance remains inconsistent.

The Outcome: Higher operational and model-risk exposure as adoption accelerates.

This is the industry baseline: While many organizations have mature CoE capabilities, most remain function-centric and cost-focused, not true enterprise performance engines.

The Opportunity
Building an AI-driven Operating Model

Most insurers still use shared services for transactional finance and siloed operational work, plus CoEs for IFRS 17 / LDTI, regulatory reporting and tax. They may also run digital programs in claims, underwriting or finance. But these setups mainly target cost reduction, not cross-functional alignment, unified data governance or enterprise-scale AI. Even where CoEs are more mature, they tend to stay function-centric rather than acting as enterprise performance engines.

To make F&A a true business enabler, insurers need complete, trusted, timely performance data and predictive insights to drive transformation. Gen AI and Agentic AI can deliver major gains, with early adopters seeing 26-31 percent² across F&A supply chain and customer experience services. But scaling remains hard due to fragmented data and siloed models.

The Solution
A Unified CoE Powered by Strategic Partnership

Together, they create an integrated F&A + operations CoE that evolves with the business – a “living” model that continuously adopts new AI and unlocks new value streams.

How it Works

The CoE is organized into pods – cross-functional teams spanning finance, operations, actuarial, data, AI and governance. Each pod drives a slice of transformation while the CoE synchronizes them into a single operating model. These pods jointly design, transform and run operations, driving continuous value.

F&A Performance Pod

Mission: Owns R2R, FP&A, controls and Gen AI use cases.

Scope: Close / consolidation, reporting, planning / forecasting, policies / controls

Strategic Partner’s Role: Domain expertise, processownership, Gen AI use-case design and delivery

Insurer’s Role: Controllers, FP&A leaders, executive oversight

Data & Platform Pod

Mission: Design and run a cross-functional data warehouse / fabric linking ERPs, PASs and platforms

Scope: Data models / semantic layers, ERP / PAS data contracts, data quality, lineage and access controls

Strategic Partner’s Role: Define data products, BI / reporting, embed semantics, prioritize platform work

Insurer’s Role: Cloud / data engineering, integrations, platform operations, finance leadership

AI, Automation & Agentic Pod

Mission: Build, deploy and govern Gen AI and Agentic AI across F&A and operations

Scope: Copilots for close / reporting / planning and claims / underwriting briefs; agents for reconciliation, routing, approvals, documentation; RPA / workflow / low-code where needed

Strategic Partner’s Role: Prioritize / design use cases, run AI-enabled processes, monitor outcomes / exceptions

Insurer’s Role: Build / host AI platforms and integrate agent frameworks into the enterprise stack

Claims & Underwriting Operations Pod

Mission: Transform claim-to-close and underwriting support to improve loss and expense ratios

Scope: First Notice of Loss (FNOL), adjudication, subrogation / recoveries; underwriting intake, submission triage, data enrichment; broker operations and bordereaux processing

Strategic Partner’s Role: Re-design and deliver operations; automation and AI use-case design (triage, document processing)

Insurer’s Role: Claims / underwriting operations leadership and product owners

Actuarial & Risk Analytics Pod

Mission: Safeguard risk and compliance, prove value realization

Scope: AI model-risk governance, data governance policies and routines; benefits tracking across cost, speed, leakage, accuracy and NPS

Strategic Partner’s Role: Own outsourced operational-risk frameworks; run tracking and value reporting

Insurer’s Role: Risk, compliance, internal audit and finance leadership

Governance & Value Management Pod

Mission: Safeguard risk and compliance, demonstrate value realization

Scope: AI / model-risk governance; data governance policies and routines; benefits tracking across cost, speed, leakage, accuracy and Net Promoter Scores (NPS)

Strategic Partner’s Role: Own outsourced operational-risk frameworks, run tracking and value reporting

Insurer’s Role: Risk, compliance, internal audit and finance leadership

Cross-pod Engagement
Use Cases Across the Enterprise

• 
  F&A Performance Pod: Defines pain points and target scope across entities / lines
• 
  Data & Platform Pod: Builds data products; maps PAS to sub-ledgers / GL with automated reconciliation
• 
  AI, Automation & Agentic Pod: Runs agents to compare PAS vs GL, flag mismatches, propose journals (within guardrails) and draft variance commentary via Gen AI copilots
• 
  Governance Pod: Materiality rules; auto-resolve < USD 10k, review USD 10k to 100k, escalate >USD 100k
• 
  Re-use: Actuarial and Claims pods leverage the framework for loss-trend analysis and reserve diagnostics

• 
  Claims & Underwriting Operations Pod: Finds 5-7 percent preventable leakage (overpayments, duplicates, out-of-policy)
• 
  Data & Platform Pod: Connects payments to policy terms, limits, history and fraud signals
• 
  AI, Automation & Agentic Pod: Pre-checks every payment, flags anomalies, auto-holds questionable payouts, routes complex cases to senior adjusters
• 
  Governance Pod: Risk tiers; auto-approve < USD 5k, review USD 5k–50k, manager approval >USD 50k
• 
  Re-use: F&A links savings to loss ratio; Actuarial uses patterns for reserves and ultimate loss

• 
  Claims & Underwriting Operations Pod: Tackles rising submissions, flat headcount and declining hit rates
• 
  Data & Platform Pod: Builds unified submission profiles using internal history, quote-to-bind ratios and external credit / hazard data
• 
  AI, Automation & Agentic Pod: Triage agents act in real time; auto-decline out-of-appetite risks, fast-track attractive submissions with pre-filled assessments and route complex cases with enriched briefs
• 
  Governance Pod: Defines appetite boundaries for auto-decline, standard routing (~70 percent of submissions) and senior escalation
• 
  Re-use: Actuarial / Risk validate pricing; F&A uses bind patterns for revenue planning and broker profitability

• 
  Actuarial & Risk Pod: Quarterly reserve reviews take 4-6 weeks due to manual extraction and triangulation
• 
  Data & Platform Pod: Automates claims development triangles by line, geography and accident year, harmonized across PASs
• 
  AI, Automation & Agentic Pod: Generates initial Incurred-But-Not-Reported (IBNR) via multiple methods, flags material movements and drafts commentary; Gen AI synthesizes reserve-movement explanations
• 
  Governance Pod: Appointed actuaries review, validate and approve all reserves; agents provide estimates and diagnostics only
• 
  Re-use: F&A speeds close; Claims spots process issues earlier; Underwriting adjusts pricing using reserve-adequacy signals

• 
  F&A Performance Pod (with Claims & Underwriting): Acquires monthly broker insights for renewals, commission talks and planning
• 
  Data & Platform Pod: Builds integrated broker profitability data products (premium, commission, claims, expenses, capital), harmonizing broker hierarchies and product categories
• 
  AI, Automation & Agentic Pod: Auto-calculates profitability, flags underperforming brokers, generates scorecards and drafts broker-specific trend / opportunity narratives via Gen AI
• 
  Governance Pod: Enforces privacy / access, validates allocation methods and tracks time-to-insight and decision impact
• 
  Re-use: Underwriting shapes renewal strategy / appetite; Claims spots elevated loss-ratio brokers; Actuarial / Risk uses broker mix for reserving and pricing

The Next-gen Insurer
Integrated, Intelligent, AI-enabled

An integrated F&A and operations CoE , powered by Agentic AI and a cross-functional data fabric, helps insurers move beyond reporting to enterprise-wide clarity and stronger business steering. The model works across P&C, L&A, Group and Specialty, and suits carriers, brokers, Managing General Agents (MGA), Third-Party Administrators (TPA) and providers. It also scales across jurisdictions and regulatory regimes. New PAS platforms, products and acquired entities can plug into the existing data fabric and CoE structure with minimal disruption. Importantly, this does not require perfect systems or a greenfield stack. It relies on three practical pillars: a shared data foundation, a CoE as the control center and a strategic finance partner – combining domain, data and Agentic AI expertise.

References

1. The future of AI for the insurance industry | McKinsey

2. How Gen AI and agentic AI redefine business operations