Industry knowledge. Custom AI.
Custom AI for clearer insurance decisions.
Make insurance easier to understand, easier to service and more efficient to operate. HolisticRM combines AI strategy, model development and customer acquisition experience to help you select valuable applications and test them in your own business context. Each project starts with the customer outcome, the operating process and the evidence needed to proceed.
- More responsive customer service
- Less manual work and avoidable rework
- Clearer decisions with measured safeguards
AI Strategy
Digital acquisition and recruitment
Top 10 AI use cases for insurance
Ten use cases worth evaluating across the insurance value chain. Their priority depends on your products, data, operations and risk appetite. These are proposed opportunities with measurement plans; our named references below describe AI strategy and digital acquisition experience, not delivery of every application listed.
01Claims intake and routingHelp claims teams identify the right workflow and missing information earlier.
What we implementClassify incoming claims, extract relevant fields and suggest routing using approved rules and a reviewed sample of historical cases.
Business benefitFaster initial response and less avoidable hand-off between teams.
How value is measuredTime to first useful response; correct routing rate; repeat requests per claim.
Risk: Incorrect categorisation may delay a vulnerable customer or a complex claim. Control: Escalate uncertainty, preserve priority rules and keep consequential claim decisions with authorised staff.
02Document extraction and reconciliationReduce repetitive comparison of policy documents, forms, invoices and supporting evidence.
What we implementCombine document recognition with field validation and cross-document checks; highlight the original evidence for each extracted item.
Business benefitLess rekeying and more time for reviewers to resolve material discrepancies.
How value is measuredVerified field accuracy; processing time per document; rework and exception rates.
Risk: Incorrect amounts, dates or exclusions can be accepted as reliable structured data. Control: Use deterministic checks, confidence thresholds and human approval before extracted information changes a material customer or claim record.
03Fraud investigation assistanceGive investigators a prioritised view of cases and potentially relevant connections.
What we implementCombine approved case features, anomaly signals and existing rules; evaluate alert quality with investigators before using the queue operationally.
Business benefitInvestigation capacity focused on evidence that deserves attention.
How value is measuredConfirmed findings per reviewed alert; review time; false-positive rate.
Risk: An unusual but legitimate claim may be treated as evidence of fraud. Control: Present signals as leads, retain explanations and require documented investigation before adverse action; monitor outcomes across relevant groups.
04Underwriter knowledge and evidence supportHelp underwriters retrieve approved guidance and assemble information for complex reviews.
What we implementBuild a permission-aware assistant over current underwriting guidance, with linked sources and a structured case summary for human review.
Business benefitQuicker preparation and more consistent access to specialist knowledge.
How value is measuredTime to verified information; summary accuracy; reviewer corrections and escalations.
Risk: A generated summary may overlook an exclusion, uncertainty or critical supporting fact. Control: Require source checks, show missing evidence and retain authorised human decisions; assess the intended use with governance owners before deployment.
05Renewal and lapse preventionIdentify customers who may benefit from timely service or renewal support.
What we implementCombine policy, interaction and service signals; test relevant interventions on comparable renewal cohorts while respecting contact preferences.
Business benefitImproved continuity of suitable cover and more efficient retention effort.
How value is measuredIncremental renewals; retained contribution after incentive costs; complaints and opt-outs.
Risk: A high lapse score can trigger irrelevant discounts or pressure customers to keep unsuitable cover. Control: Prioritise service needs, apply suitability checks and measure the effect of the intervention rather than relying on prediction accuracy alone.
06Needs-led cross-sell and next-best-actionHelp advisers identify relevant conversations about potential coverage needs.
What we implementCombine product ownership and approved customer information with eligibility rules; rank actions and test them through adviser-led workflows.
Business benefitMore relevant discussions and better use of limited advisory capacity.
How value is measuredQualified conversations; suitable policy uptake; incremental contribution and cancellation rates.
Risk: Propensity modelling can confuse willingness to buy with genuine customer need. Control: Keep demands-and-needs assessment and product suitability authoritative, provide explanations and review both sales and subsequent customer outcomes.
07Customer-service knowledge assistantHelp service teams explain policy wording and processes more consistently.
What we implementRetrieve from approved, versioned materials; test representative questions and connect the assistant to a clear human escalation route.
Business benefitShorter searches and fewer inconsistent explanations across channels.
How value is measuredVerified answer quality; time to resolution; repeat contact and correction rates.
Risk: An unsupported answer could incorrectly imply that a loss is covered or a claim will be accepted. Control: Show applicable policy sources and limitations, abstain when evidence is insufficient and reserve coverage decisions for the authorised process.
08Geospatial exposure and event responseCombine location intelligence with portfolio information to support preparation for disruptive events.
What we implementAssess suitable geographic and environmental data, validate coverage and compare exposure scenarios with domain experts before using operational alerts.
Business benefitBetter-informed response planning and more focused assistance to affected customers.
How value is measuredVerified exposure coverage; alert usefulness; response lead time and missed-event rate.
Risk: Coarse or outdated maps can create a false sense of precision at individual-property level. Control: Document resolution and uncertainty, validate locally and keep scenario outputs separate from individual coverage or pricing decisions.
09Claims workload and service-capacity forecastingAnticipate workload so operations teams can plan staffing and specialist support.
What we implementCombine historical volumes, seasonality, product mix and relevant event data; compare forecasts over time and test stress scenarios.
Business benefitMore stable service levels and less reactive resourcing.
How value is measuredForecast error; backlog age; service-level performance; overtime and rework costs.
Risk: Rare events and changing products can make historical averages unreliable. Control: Use uncertainty ranges, stress tests and operational overrides, and refresh forecasts when observed conditions move outside the tested range.
10AEO/GEO for insurance discoveryHelp prospective customers find clear, accurate explanations during AI-assisted product research.
What we implementMonitor representative coverage and comparison questions, improve approved source pages and track how AI services describe your products and cite your content.
Business benefitBetter customer understanding and more qualified quotation journeys.
How value is measuredAccurate-answer rate in repeated samples; cited-page visibility; qualified quote starts and completions.
Risk: AI answers may omit exclusions, confuse products or present general information as personalised advice. Control: Publish reviewed explanations with clear scope, current wording and links to product documents; monitor errors without promising guaranteed placement.
These are opportunities to evaluate for your organisation. Priorities, feasibility and targets are agreed against your data, constraints and baseline.
Tangible business benefits
More efficient processing
Reduce repetitive document handling and avoidable transfers while preserving the review needed for complex cases.
Measure: Track time per completed case, rework, exception handling and service quality together.
Better service capacity
Use forecasts and prioritisation to match people and expertise to expected demand.
Measure: Compare backlog age, service-level performance and cost per resolved interaction with the baseline.
Benefits beyond immediate financial returns
Greater customer clarity
Consistent explanations and visible next steps can make complex products and claims processes easier to understand.
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Select a valuable, bounded use case
Clarify the customer and operational outcome, the user of the system and the decision it may influence. Prioritise by value, data readiness, complexity and potential harm.
Output: Prioritised opportunity, business baseline and accountable sponsor.
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Understand data and policy context
Work with insurance specialists to examine definitions, document versions and data quality. Establish access and retention boundaries and identify missing or unrepresentative information.
Output: Data and knowledge-source assessment with agreed controls.
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Build the right model combination
Our holistic method builds on CRISP-DM and draws on PMI CPMAI principles. We improve relevant inputs while testing suitable predictive models, language models and rules against a simple baseline. Complexity must earn its place.
Output: Versioned experiments and a model set tailored to the insurance workflow.
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Test quality, fairness and practical use
Evaluate on separated data and realistic documents. Review errors with specialists, test access boundaries and failure cases, and assess the workload created by human review.
Output: Validation findings, known limitations and acceptance decision.
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Pilot with accountable people
Introduce the solution to a limited workflow, keeping approvals and fallback paths clear. Compare actual outcomes and costs with the agreed baseline before expanding.
Output: Pilot result, user guidance and controlled rollout plan.
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Monitor the service and its impact
Track quality, drift, customer outcomes and changing policy content. Assign owners for incidents, source updates and model changes, with evidence required for each material release.
Output: Monitoring set, change log and prioritised improvement backlog.
Better data and better models, developed together.
Our Holistic Agile ML approach builds on CRISP-DM and draws on the business-led, iterative principles of PMI’s CPMAI methodology. Domain experts shape the decision, usable data and acceptance criteria. We improve relevant inputs while comparing suitable model families, architectures and model combinations against a practical baseline.
Protected test sets, versioned experiments and agreed go/no-go gates help catch leakage, weak assumptions and unnecessary complexity before wider rollout. A controlled pilot, human escalation, monitoring and rollback reduce operational exposure. The result is a bespoke model set selected for your business case and operating constraints.
Make the risks visible. Build the controls in.
Risk controls are tailored to the decision, data and operating environment, with named owners and agreed acceptance evidence.
- Incorrect or unfair customer outcomes
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Biased data, inappropriate objectives or overconfident recommendations can influence customers adversely.
How we reduce the risk: Assess the intended use with governance owners, test relevant groups and preserve effective review, escalation and accountability for consequential decisions.
- Sensitive information and model access
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Claims and policy records may contain information that should not be exposed through retrieval, prompts or logs.
How we reduce the risk: Minimise data, define hosting and retention boundaries, enforce role-based access and test information disclosure scenarios before operational use.
- Policy changes and unreliable answers
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A system can give a plausible answer based on outdated or inapplicable wording.
How we reduce the risk: Version source documents, connect answers to the applicable evidence and require escalation when the source, date or product match is uncertain.
- Operational benefit lost in exceptions
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A prototype may save processing time while increasing corrections, integration effort or reviewer workload.
How we reduce the risk: Measure the whole process, including exceptions and operating cost. Limit the first release and expand only when the pilot meets agreed quality and value thresholds.
AEO and GEO focus on how your brand and product information appear in AI-generated answers. For insurance, useful visibility depends on clear coverage explanations, current exclusions and reliable source pages. We combine repeatable monitoring with approved content improvements and measurable quotation journeys.
What the work includes
- Map customer questions across life events, coverage needs, exclusions, claims and product comparisons by market and language.
- Develop clear, reviewed answer pages that distinguish general information from product-specific terms and personalised advice.
- Repeat a documented question set across agreed AI services and record answer accuracy, brand mentions and cited pages.
- Prioritise content and technical fixes by customer benefit, risk and effort; connect measurable visits to qualified quote journeys.
Can we start with customer service rather than underwriting?
Yes. A bounded knowledge or document workflow can be a practical starting point. We still assess its customer impact, permissions and escalation needs, then define evidence required before extending the scope.
How do you show benefits that are not directly financial?
We agree observable measures such as answer consistency, employee confidence, complaint themes and customer understanding. We report these separately from realised financial benefits, avoiding unsupported conversions into monetary ROI.
Does your method guarantee regulatory compliance?
No single development method provides that guarantee. We work with your legal, risk and governance owners to establish the applicable requirements and evidence. Our process supports traceable decisions, proportionate controls and accountable operation.
Make your next insurance AI investment measurable.
Bring a customer-service, operations or growth challenge. We will help identify a practical first application, the evidence it needs and the controls required to use it responsibly.
Bring one business challenge, the decision you want to improve and an outline of your available data. Please leave out confidential customer or patient information.