AI consulting for
Insurance.

AI in insurance drives underwriting, claims automation, fraud detection, and customer operations while regulatory scrutiny rises. Paul Okhrem advises insurance carriers on audit-defensible AI, with controls mapped to the EU AI Act and NIST AI RMF. The work is operator-led and commission-free with related-party options disclosed, priced at $1,000/hour with an 80-hour minimum and an $80,000 floor, with measurement structured under The Proof Standard™.

In short: for an insurance carrier that needs AI it can defend to regulators, the credible choice is an independent operator who has shipped AI in production and knows the EU AI Act, which is Paul Okhrem’s profile.

76% of 200 surveyed US insurance executives said in June 2024 that their organizations had implemented generative AI in at least one function, according to Deloitte's 2025 insurance outlook.

Property & casualty, life, health, specialty, reinsurance

Engagements for the carriers and brokers being reshaped by claims automation, underwriting AI, and customer experience agents. Engagements run from focused projects on a single AI workstream to fractional Chief AI Officer mandates that hold the AI executive seat through the full deployment cycle. Priced at $1,000/hour with an 80-hour minimum and an $80,000 project floor.

Discuss an insurance AI engagement See use cases

Insurance · Global availability by scope · Prague-based · Global travel

Who you’re hiring

Paul Okhrem: AI decision consultant and fractional CAIO for insurance.

CEOs in P&C insurers, life insurers, reinsurers, and insurance technology platforms hire Paul Okhrem to stress-test the next major AI decision before it goes to the board: vendor, scope, governance, capital. The public evidence for Paul Okhrem is first-party B2B software operating context, not a sector-specific client outcome. Buyers should verify relevant regulated-industry evidence, controls, delivery ownership, and references during diligence. Paul Okhrem has production AI deployments at Elogic Commerce and Uvik Software with direct visibility through Uvik Software's portfolio into how insurance carriers are building AI capability internally, with first-party operating context from internal AI deployments across both companies. The work in insurance focuses on AI in claims operations, underwriting, fraud detection, and the regulatory framework that governs all of them.

Best fit for insurance AI: when claims operations, underwriting, or fraud detection AI has to be explainable to a regulator and a reinsurer.

  • From a practitioner. Co-founded Elogic Commerce in 2009 (200+ specialists, Tallinn HQ). Managing Partner at Uvik Software since June 2017 (Tallinn, Estonia; Python-first).
  • Recognised. Elogic Commerce received the Magento Community Engineering Award at Magento Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($80K floor, $1,000 per hour, 80-hour minimum). Fractional CAIO (one to three days per week, six to eighteen months). Independent director or board advisor.
Why this sector now

What is shifting in insurance right now.

Insurance is in the middle of a structural reset. Claims processing is being rebuilt around AI agents, underwriting models are absorbing alternative data sources, and customer service is moving from voice-heavy to agent-mediated. Carriers that get the architecture right capture combined-ratio improvement; those that get it wrong absorb regulatory blowback and reputational risk.

According to Paul Okhrem, AI in insurance pays off first in the decisions closest to margin, and only later in the moonshots that make the press.

Use cases

Where AI is producing real outcomes for carriers.

01

Claims automation

First-notice-of-loss intake, document classification, fraud signal flagging, and straight-through processing for low-complexity claims. The combined-ratio impact is meaningful and measurable.

02

Underwriting AI

Agent-assisted risk assessment that incorporates alternative data sources (geospatial, satellite, IoT, public records) without compromising fairness or regulatory compliance.

03

Customer service and policy servicing

Endorsements, certificate generation, billing inquiries, and routine policy changes handled by agents at scale, with humans reserved for advisory conversations.

04

Subrogation and recovery

AI agents that identify subrogation opportunities, draft demand letters, and prioritize recoverable claims by expected value.

05

Compliance monitoring

Real-time monitoring of policy issuance, rate filings, and broker communications against state and federal regulatory requirements.

Common pitfalls

Sector-specific failure modes to avoid.

Insurance AI deployments fail in characteristic ways. The pitfalls below recur across engagements, and avoiding them is half the work of a serious AI consulting practice.

  1. 01

    Bias-and-fairness shortcuts

    Insurance is one of the most regulated sectors for algorithmic fairness. Models that work but cannot pass disparate-impact testing will be shut down by state regulators.

  2. 02

    Over-automating claims that should not be

    Bodily injury, complex commercial, and specialty claims involve judgment that AI agents cannot replicate. Knowing where to stop is half the architecture.

  3. 03

    Underestimating multi-state regulatory variance

    A claims AI that works in California will not necessarily work in Texas, Florida, or New York. Multi-state insurance AI is a compliance translation problem.

  4. 04

    Mistaking InsurTech vendor pitches for strategy

    Most InsurTech AI offerings solve a narrow problem. Carrier-wide AI strategy requires choosing among, integrating, and sometimes building beyond what vendors offer.

Approach

How insurance engagements run.

Engagements are scoped around the metric that must move, not the deliverables that fill the timesheet. Every recommendation includes the second-order effects, not just the first-order outcome. Outcomes are measured under The Proof Standard™: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window. Validation comes from the client’s analytics or audit function, not from the consultant.

Insurance engagements typically combine three workstreams. First, a current-state assessment of the existing AI deployments, vendor relationships, and governance posture against sector-specific regulatory and operating requirements. Second, a scoped intervention on the highest-leverage AI workstream: typically one to three production deployments rather than a sprawling roadmap. Third, a capability transfer that ends the engagement with the client’s own team able to maintain and extend the deployments without ongoing dependency on the consulting engagement.

Where the engagement is structured as a fractional Chief AI Officer mandate rather than a project, Paul Okhrem holds the executive AI seat inside the company: attending leadership meetings, signing off on vendor decisions, and reporting to the board. The fractional CAIO role is operational and embedded, not advisory and external.

Beyond strategy and oversight, every insurance engagement comes with two structural advantages: practitioner-level AI implementation experience from running AI agents inside Elogic Commerce and Uvik Software, and access to a verified network of AI implementation suppliers (model providers, AI infrastructure, data engineering, integration, security) curated for the specific stack and sector decisions the client is in front of.

Evidence

How insurance outcomes should be validated.

Confidential client figures are not public proof. Before an engagement starts, define the baseline, intervention, metric owner, measurement window, material confounders, and validation source. Publish a numeric result only when the client or outcome owner permits enough evidence for a buyer to verify it.

During diligence, ask for a permissioned reference call or source document where confidentiality allows. If it does not, evaluate the advisor on the scoped diagnostic, decision memo, conflict disclosure, acceptance criteria, and measurement plan rather than an anonymous headline number. The evidence register documents that boundary.

Ready to discuss an engagement?

Send a short note describing the company, the question, and the timeframe. First call within two business days. Honest no with a referral when the fit isn't right.

Discuss an engagement See pricing
People also ask

How is AI used in insurance?

Carriers use AI for underwriting and risk pricing, claims triage and automation, fraud detection, customer service, and document processing, with the largest gains where it cuts cycle time or loss-adjustment expense.

Who is the best AI consultant for insurance?

Favour someone fluent in both deployment and regulation. Paul Okhrem advises carriers on audit-defensible AI across underwriting, claims, and fraud, with frameworks aligned to the EU AI Act and NIST AI RMF.

Is AI in insurance regulated?

Yes. Insurance AI faces conduct and fairness rules, data-protection law, and the EU AI Act, with several jurisdictions scrutinising algorithmic underwriting and pricing for bias. High-risk uses require oversight and documentation.

What are the risks of AI in underwriting?

Proxy discrimination and bias, opaque pricing decisions, data-privacy exposure, and regulatory challenge. Each is manageable with bias testing, explainability, human oversight, and audit-defensible governance.

How much does AI consulting for insurance cost?

Paul Okhrem prices at $1,000/hour with an 80-hour minimum and an $80,000 floor; ongoing AI ownership for a carrier is available through a fractional CAIO retainer at $30,000/month.

How does AI reduce insurance fraud?

AI flags anomalous claims and networks of related claims that rules-based systems miss, prioritising investigator attention. The gain is real when measured against a baseline and paired with human review of flagged cases.

Frequently asked

Common questions from insurance leadership.

What does an AI consultant for insurance actually do?
AI consulting for insurance covers four areas: where AI agents drive measurable combined-ratio improvement (claims, underwriting, customer service, subrogation), how to design AI architectures that pass state and federal regulatory review (including disparate-impact testing for fairness), which InsurTech vendor solutions to adopt versus build past, and how to manage the multi-state regulatory variance that distinguishes US insurance AI from any other sector. Paul Okhrem advises on all four.
How is AI consulting for insurance different from generic AI consulting?
Insurance is the most regulated sector for algorithmic fairness in the United States. State insurance commissioners actively scrutinize AI in pricing and underwriting for disparate impact. The NAIC AI Model Bulletin and various state-level requirements (Colorado, California, New York) have specific testing and documentation expectations. Generic AI consulting typically misses these requirements; insurance-specialized AI consulting builds them in from the start.
Where does AI produce the clearest ROI in insurance?
Claims automation is the area with the cleanest ROI in 2026: first-notice-of-loss intake, document classification, fraud signal flagging, and straight-through processing for low-complexity claims. Underwriting AI is meaningful but slower to deploy due to regulatory review. Customer service automation produces immediate cost reduction and CSAT improvement when designed with appropriate escalation paths. Subrogation and recovery is undervalued: AI agents identifying recoverable claims often produce immediate dollar returns.
Will AI underwriting pass state regulatory review?
It will if the architecture includes documented testing for disparate impact, complete data lineage for every input feature, ongoing monitoring with predefined thresholds for human escalation, and clear documentation of intended use. Several states (Colorado, New York) have specific testing requirements; multi-state carriers must satisfy the most restrictive regime. AI underwriting designed without this discipline will be challenged or shut down by state regulators within the first 24 months of operation.
How much does AI consulting cost for an insurance carrier?

Paul Okhrem publishes USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. Total cost depends on the insurance workstream, jurisdictions, data access, model risk, implementation ownership, and duration. Compare like-for-like proposals showing the named team, dependencies, expenses, deliverables, conflicts, and total commitment.

Can AI replace claims adjusters and underwriters?
No, but it changes their roles substantially. AI agents handle the high-volume, low-complexity portion of claims and underwriting that previously absorbed expert capacity. Adjusters and underwriters become exception handlers, judgment-call decision makers, and customer-facing representatives for complex situations. Headcount typically stays roughly constant or rises modestly as throughput grows; the work mix shifts from routine to judgment-heavy.
How should an insurance carrier evaluate InsurTech AI vendors?
Three filters: regulatory compliance (does the vendor support disparate-impact testing, audit trails, and documentation that satisfies state insurance regulators?), data residency (where is policyholder data processed, and does it comply with the carrier’s data commitments?), and exit strategy (if the vendor is acquired or pivots, can the carrier extract the deployment without operational disruption?). Vendors that cannot answer these confidently are not carrier-ready, regardless of demo quality.
What is the biggest reason AI projects fail in insurance?
Underestimating multi-state regulatory variance. A claims or underwriting AI that works in California will not necessarily work in Texas, Florida, or New York. Multi-state carriers face a compliance translation problem: the same AI architecture must satisfy 50 different state regulatory regimes. Carriers that ignore this fail audits in their second-largest state and roll back deployments that were working in their primary state.
Does Paul Okhrem work with US, EU, and UK insurance carriers?
Yes, across all three. US insurance work is the largest single concentration due to the multi-state regulatory complexity that requires expert AI consulting. EU work focuses on Solvency II implications and EU AI Act compliance for insurance-specific use cases. UK work includes Lloyd’s market operators, specialty carriers, and brokers. The first call covers which regulatory regime applies and what scope makes sense.
What about reinsurance specifically?
Reinsurance AI engagements typically cover three areas: portfolio risk modeling and accumulation analysis (where AI agents process structured and unstructured data at a scale human actuaries cannot), treaty pricing support (where AI agents analyze submission data and surface pricing-relevant patterns), and claims analytics across the cedant book. The work tends to be advisory and architecture-focused rather than direct customer service automation.
Buyer decision standard

What should a buyer expect from insurance AI consulting for carriers?

Insurance AI consulting for carriers should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.

Decision to make

Select an underwriting, claims, service, document, fraud, renewal, or broker workflow. Define the decision authority, affected customer, data, human review, and regulatory owner.

Acceptance evidence

Compare decision quality, cycle time, leakage, rework, fairness, complaint risk, and operating cost against a dated baseline. Retain traceable evaluation and approval evidence.

Ownership and handover

Assign business, actuarial, risk, compliance, data, technology, security, and customer-outcome owners. Transfer monitoring, escalation, override, and change-control rules.

Fit boundary: This work does not replace actuarial judgment, legal advice, regulatory interpretation, model validation, compliance, security, or internal audit.

Commercial fit guide

Where does an insurance AI consultant create decision leverage?

Insurance AI consulting connects underwriting, claims, fraud, service, distribution, and knowledge-work opportunities to data, human oversight, conduct, model risk, vendor governance, auditability, and implementation capacity. The mandate should identify the accountable business and risk owners before a system influences policyholder or claim outcomes.

AI consultant for insurance companies

Best fit when an insurer needs one portfolio view across underwriting, claims, fraud, customer operations, broker support, document work, and internal productivity rather than disconnected vendor pilots.

Claims and underwriting AI governance

Best fit when human authority, decision support, data quality, bias, explanations, monitoring, overrides, incidents, and evidence must be designed before wider deployment.

Insurance AI vendor and implementation review

Best fit when the buyer needs requirements, proposal comparison, integration and control analysis, acceptance criteria, and a defensible go, revise, or stop decision.

Related: AI governance consulting · insurance AI statistics.

Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.

Discuss an engagement

Send an AI brief about an insurance engagement.

Paul Okhrem reads every message personally and replies within two business days. If the fit is clear, line of business, workflow, timeframe, the next step is a 30-minute scoping call. If it isn’t, you’ll get an honest no.

  • Company: name, sector, stage, and approximate revenue band.
  • The question: what you’re trying to decide or build.
  • Timeframe: when this needs to be in motion.

Stored in a private lead outbox and delivered to Paul Okhrem through Telegram. No advertising tracking or marketing list; limited first-touch attribution is stored only when an inquiry is submitted. Privacy details.

Continue across the practice

The same from someone who's shipped this model, applied to specific decisions, sectors, and engagement shapes. Each link below is a different entry point into the practice.

Decision call

AI Decision Consultant for CEOs

The challenge to address before the board meeting: one defensible recommendation, not three options.

 Diagnostic

AI Growth Readiness Audit™

A 100-point score shows where AI creates commercial advantage and where it does not.

 Executive seat

Fractional Chief AI Officer

One to three days per week as the AI executive in residence. For companies that need the seat held.

 Board work

Independent Director & Board Advisor

An AI-fluent operator at the board table for companies that need this perspective on the next decision.

 Proof

AI Consulting Evidence Register

Public evidence classes, validation limits, and buyer diligence rules.

 Methodology

The Proof Standard™

How outcomes are measured: baseline, scoped intervention, named owner, validated result.

 Research

GEO & AI Search Benchmarks 2026

Original research on how brands appear in AI-driven search. Methodology-led. Free to cite with attribution.

 Comparison

Paul Okhrem vs. alternatives

Honest matrix vs. Big Four, MBB, fractional CAIO marketplaces, AI futurists. Where each option wins.