What problem does it solve?
In general insurance, most policies come up for renewal every year, so renewals decide how much of the book an insurer keeps. In commercial lines, renewal submissions arrive in the same unstructured formats as new business, and underwriters must spot what changed in the risk before the renewal date, which is hard to do for every account when volumes peak. In personal lines, customers can leave quietly at renewal when a premium rises, and renewal peaks put pressure on contact centres. Life and protection policies usually stay in force while premiums are paid, so there the risk is a lapse after a missed or failed payment rather than a renewal decision.
Retention is also regulated. In the UK, home and motor insurers may not offer renewing customers a price above the equivalent new business price, and in the EU, AI used for risk assessment and pricing of individuals in life and health insurance is high risk under the AI Act. So the opportunity is in better renewal processing, earlier outreach and clearer explanations, not in using AI to find customers who will tolerate higher prices.
How does it work?
- Digitize the renewal. Renewal submissions and updated schedules are read and compared with the expiring policy, and changes in exposure are highlighted for the underwriter.
- Triage the renewal book. Low complexity renewals within appetite are prepared for automated or light touch processing under existing rules; complex or deteriorating risks go to an underwriter early.
- Spot lapse risk. A model flags customers likely to lapse or leave (failed payments, engagement changes, large premium changes) so service teams can reach out in time.
- Prepare the conversation. The assistant drafts renewal explanations and answers customers' questions about what changed and why, from the renewal documents.
- Keep price decisions governed. Renewal prices come from the insurer's pricing rules, checked against fair value and renewal pricing rules; the AI does not set them.
- Audience
- Back office
- Autonomy
- Copilot
- Adoption
- Emerging
- Channels
- Email, Web chat, Phone and voice, Internal tools
What is it worth?
Benchmarks are computed from the public deployments below: one data point per organization per KPI, with who made each claim.
No public deployment has disclosed a measurable outcome yet.
Value drivers: Revenue growth, Employee productivity, Customer experience, Compliance quality.
Indicative value
A personal lines insurer with 500,000 policies up for renewal each year
USD 600,000 to USD 3 million
Premium retained per year
How this is calculated
Formula: renewals * lapseRate * relativeReduction * averagePremium. The low scenario uses every low input, the high scenario every high input.
| Input | Low | High | Basis |
|---|---|---|---|
| Policies up for renewal per year renewals, policies per year | 500,000 | 500,000 | The reference insurer. |
| Baseline share of policies not renewed lapseRate, fraction of renewals | 0.15 | 0.2 | Editorial assumption. Replace with your own lapse and cancellation data. |
| Relative reduction in avoidable lapses relativeReduction, fraction of lapses | 0.02 | 0.05 | Editorial assumption; no insurer on this page publishes a measured retention effect. Measure it against a control group. |
| Average annual premium averagePremium, USD per policy | 400 | 600 | Editorial assumption. Replace with your own average premium. |
What it leaves out: Premium retained, not profit, and not all lapses are worth preventing. It leaves out underwriting time saved on renewal processing, the cost of outreach and the platform, and any customer who would have renewed anyway.
Who already uses it?
3 public deployments, strongest evidence first. Grades: A regulator or audit, B the organization itself, C vendor case study, D anonymous or estimate.
Hiscox
United Kingdom · Insurance · 2024
Hiscox London Market combined its own Hiscox AI Laboratories (Hailo) with Google Cloud's Gemini model to automate lead underwriting from email submission to quote in its sabotage and terrorism line. In scope risks are assessed by the model and the process generates an email to the broker with pricing and other data completed, ready for underwriter review. After a December 2023 proof of concept, in which Hiscox said the manual extraction step can take up to three days and quotes could be produced within three minutes, the model went live in August 2024. It initially covers renewals of existing US and Canadian sabotage and terrorism risks, excluding the New York and Chicago metro areas.
No outcome disclosed.
Zurich Insurance Group
Switzerland · Insurance · 2025
Zurich's commercial insurance teams manage more than 100,000 active opportunities in Dynamics 365, and switching applications to copy updates from email into the CRM left data at risk of going stale. With Microsoft 365 Copilot for Sales, 300 users create and update contacts and link emails to opportunities from Outlook, get summaries of relationships and long email threads, and draft emails. Zurich estimates about 14,000 hours saved over the next year; that is an estimate, not a measured result. The story also says user feedback indicates the tool improves Zurich's sales and retention ratios, without figures.
- Users served: 300
"Copilot for Sales has quickly become a critical productivity tool for Zurich’s 300 Copilot users, who find even more benefits as they continue to work with it in Outlook."
Claimed by: vendor
Nsure.com
United States · Insurance · 2024
Nsure.com is a Florida based digital insurance agency that lets consumers compare home and auto quotes from more than 50 insurers and buy online. Generative AI in Power Automate cut its service representatives' manual processing time by more than 60%, for example by triaging the shared inboxes and either preparing an automated response or routing each email to an agent. It replaced a third party chatbot with a Copilot Studio copilot, Friendly John, that helps customers submit payments, review renewal offers and request discounts, with an interactive voice response option and after hours support. Its VP of AI and Automation says it handles around 60% of customer questions, and the company plans to use copilots for new policy sales and cross selling.
No outcome disclosed.
How do you implement it?
A model agnostic playbook: what to prepare, the order to build in, and what goes wrong.
Data you need
- Expiring policy data and renewal submissions per account
- Payment, contact and engagement history for lapse prediction
- Renewal pricing rules and fair value assessments
- Consent and preference data for outreach
Systems to integrate
- Policy administration and renewal processing
- Underwriting workbench
- Billing and payment systems
- CRM and contact centre for outreach and handover
Complexity: Medium
Renewal digitization reuses submission intake technology; lapse models are standard machine learning. The difficult part is governance: keeping retention work separate from price optimization that regulators restrict, and proving the effect with a control group.
- 1
Separate the two problems
Treat commercial renewal processing (underwriting efficiency) and personal lines retention (customer outreach) as separate projects with different owners and controls.
- 2
Start with renewal intake in commercial lines
Reuse submission extraction on renewal documents and show underwriters what changed against the expiring terms, starting with lines that have high renewal volumes.
- 3
Build lapse prediction with outreach, not pricing
Use lapse scores only to decide who gets a service call, a payment reminder or a policy review, never to adjust the renewal price.
- 4
Test with a control group
Hold out a random share of flagged customers to measure the true retention effect before scaling outreach.
- 5
Review for fair value
Have pricing and compliance confirm that nothing in the retention process changes renewal price by tenure or propensity to shop around where rules forbid it.
Guardrails
- Lapse and churn scores never feed renewal pricing
- Renewal prices only from governed pricing rules, checked against renewal pricing requirements
- Outreach limited to customers who consented to contact, with vulnerability checks
- Automated renewals only for low complexity risks within written rules
- Explanations of premium changes drawn from the actual renewal documents
KPIs to instrument
- Share of renewals reviewed before the renewal date
- Retention rate for flagged customers versus a random control group
- Underwriting time per renewal
- Complaints about renewal prices and communications
- Fair value and renewal pricing test results
Human in the loop
Underwriters review every renewal outside the automated cohort and every material change in risk. Service staff make retention calls with AI prepared context, and pricing and compliance own the rules that separate retention outreach from price setting.
Common failure modes
- Retention models become price optimization
- Scores that predict who will not shop around drift into pricing decisions. Keep the systems and teams separate and audit the data flows.
- Rolled over risks nobody looked at
- Automated renewal processes a risk that changed materially. Compare against the expiring policy and route changes to an underwriter.
- Retention effect that was never there
- Outreach goes to customers who would have renewed anyway. Measure against a control group.
- Pushy outreach to vulnerable customers
- Retention calls pressure customers in financial difficulty. Check vulnerability signals first.
What are the risks and rules?
EU AI Act
Depends on design
Renewal intake for commercial lines and outreach are not listed in Annex III. Renewal risk assessment or pricing for life or health insurance of natural persons is high risk under point 5(c), and so is a lapse score that feeds those decisions; a lapse score used only to decide who gets a service call is not listed. Customer facing renewal assistants carry the Article 50(1) duty to tell people they are interacting with an AI system, unless that is obvious from the context.
Guidance
- PS21/5: General insurance pricing practices market study, feedback to CP20/19 and final rules (Financial Conduct Authority, Europe). Final rules requiring firms to offer UK home and motor insurance renewal prices no greater than the equivalent new business price, however the price is set.
- Annex III, high risk AI systems referred to in Article 6(2) (European Union, Europe). Point 5(c) covers AI systems for risk assessment and pricing in relation to natural persons in life and health insurance, which includes those decisions when they are made at renewal.
- Opinion on Artificial Intelligence governance and risk management (European Insurance and Occupational Pensions Authority, Europe). Published August 2025 for national supervisors. Asks insurers to treat customers fairly across the AI lifecycle and value chain, monitor outcomes with fairness and non discrimination metrics, and points to EIOPA's 2023 Supervisory Statement on Differential Pricing Practices.
Controls to put in place
- Documented separation between retention scoring and pricing
- Control group design and results for every retention programme
- Fair value and renewal pricing tests signed off by pricing and compliance
- Vulnerability screening before outreach
- Audit trail of automated renewals and the rules applied
When it went wrong elsewhere
- Suckers List: How Allstate's Secret Auto Insurance Algorithm Squeezes Big Spenders. The Markup and Consumer Reports analysed a "retention model" Allstate proposed in Maryland and found it would have charged big spenders more rather than following risk; Maryland rejected it as discriminatory and Allstate called the reporting inaccurate. It shows why retention signals must stay out of pricing.
Frequently asked questions
- Can AI improve insurance retention without breaking pricing rules?
- Yes, if it works on service rather than price: earlier outreach, fixing payment problems, explaining changes clearly and processing renewals on time. In the UK, renewal prices for home and motor may not exceed the equivalent new business price, so retention models must stay out of pricing.
- Are there published results?
- Few so far. Hiscox's generative AI lead underwriting model started with renewals of existing sabotage and terrorism risks, Nsure.com's copilot helps customers review renewal offers, and Microsoft's customer story on Zurich says user feedback shows its sales copilot improves sales and retention ratios, but none publish a measured retention effect.
- Is AI in renewals high risk under the EU AI Act?
- Only where it assesses risk or sets prices for life or health insurance of individuals, which is high risk under Annex III point 5(c). Commercial renewal processing and service outreach are not listed, but a customer facing renewal assistant must tell people they are talking to an AI unless that is obvious from the context (Article 50(1)).
How to cite this page
Blits.ai AI Use Case Library, "AI for insurance renewal processing and customer retention", last verified 26 September 2026, https://www.blits.ai/ai-use-cases/insurance-renewal-and-retention. Licensed under CC BY 4.0. Method: how we verify use cases.
Changelog
- 27 September 2026: First published