Designing a High-Value Insurtech Proof of Concept
A new insurtech solution can promise faster claims, stronger underwriting, cleaner reporting, or a better customer experience. Yet a persuasive demonstration is not the same as a useful proof of concept. A successful POC gives an insurer enough evidence to decide whether a technology deserves further investment, operational adoption, or a controlled exit.
The strongest pilots connect business priorities with measurable outcomes. They also account for data quality, security, regulatory expectations, finance requirements, workflow changes, and the practical concerns of employees who will use the solution. A narrowly defined experiment can therefore reveal more value than a broad technology showcase.
For insurance executives and project sponsors, the goal is to reduce uncertainty. The POC should make assumptions visible, establish a fair test, involve the right stakeholders, and produce evidence that can withstand scrutiny from finance, risk, compliance, operations, and executive leadership.
Start With A Business Decision
A POC should begin with a decision that leadership needs to make. “Test artificial intelligence” is too vague to guide design. “Determine whether automated document classification can reduce commercial claims intake time without increasing referral errors” gives the team a clear purpose, user group, process, and outcome.
Define the operational problem in specific terms. Identify where delays, manual rework, leakage, inconsistent decisions, or poor visibility currently occur. Document the existing workflow, including handoffs between underwriting, claims, accounting, customer administration, information technology, and external partners. This baseline becomes the reference point for judging the new solution.
The business case should also identify what will happen after the POC. A successful result may lead to a larger pilot, a procurement process, integration planning, or investment approval. A weak result may prevent unnecessary spending. Both outcomes are valuable when the decision criteria are agreed upon before testing begins.
Keep The Scope Narrow And Representative
The ideal POC is small enough to manage and realistic enough to matter. Select one process, product line, geography, or user group rather than attempting to transform an entire operating model. For example, a carrier might test an underwriting assistant on a defined set of small commercial submissions or evaluate a billing automation tool within one regional business unit.
Scope should include explicit boundaries. State which systems will be connected, which data sources will be used, how many transactions will be processed, and which activities will remain manual. Exclude complicated edge cases at first if they would prevent a reliable initial test, but record them for later evaluation.
A representative sample is essential. Testing only clean, recently formatted data can make an insurtech product appear more effective than it will be in production. Include ordinary exceptions, incomplete records, multiple document formats, and realistic transaction volumes. The sample should reflect the conditions that employees and customers actually encounter.
Establish Data, Security, And Compliance Controls
Data preparation often consumes more time than expected. Before the pilot starts, confirm data ownership, access permissions, retention rules, field definitions, and quality standards. Decide whether personally identifiable information, health information, financial records, or confidential policy data will be used. Where possible, use masked or synthetic data for early testing, then introduce controlled production-like records when the evaluation requires them.
Security review should cover authentication, encryption, hosting location, subcontractors, application programming interfaces, logging, incident response, and the vendor’s rights to use customer data. The POC must clarify whether information is used to train a vendor model, retained after the test, or transferred to another service provider. These questions are easier to resolve before integration than after a successful demonstration.
Regulatory and tax considerations also belong in the design. A solution that changes underwriting decisions, claims handling, customer communications, financial reporting, or data processing may create obligations across jurisdictions. Teams tracking regulatory developments can use that context to shape documentation, explainability, human oversight, and recordkeeping requirements from the beginning.
Define Measures That Prove Value
A credible POC combines operational, financial, technical, and user-centered measures. Speed may matter, but faster processing is not valuable if accuracy falls or employees spend more time correcting errors. Similarly, a lower vendor fee does not create savings if integration, training, monitoring, and support costs are ignored.
Create a baseline before the experiment. Useful measures may include average handling time, cycle time, straight-through processing, referral rates, error frequency, reconciliation effort, customer response time, employee adoption, and cost per transaction. Establish how each measure will be calculated, who owns the data, and how often results will be reviewed.
Set thresholds rather than relying on general impressions. A POC might require a 20% reduction in processing time, a classification accuracy above 95%, no material increase in complaints, and a defined level of user acceptance. Include a “stop” condition for security incidents, unacceptable bias, material control failures, or data quality problems.
| Evaluation Area | Example Measure | Evidence Source | Decision Threshold |
|---|---|---|---|
| Operational efficiency | Average handling time | Workflow timestamps | At least 20% improvement |
| Quality | Accuracy and rework rate | Quality review sample | Accuracy remains above baseline |
| Financial impact | Cost per transaction | Finance model | Positive value after implementation costs |
| User adoption | Weekly active users and feedback | Usage data and surveys | Sustained use by target group |
| Risk and control | Exceptions, incidents, audit findings | Control logs and review | No unresolved material issue |
| Technical performance | Availability and response time | Platform monitoring | Meets agreed service levels |
Give Governance A Practical Shape
A POC needs an accountable sponsor, a delivery lead, a product owner, a technology representative, and subject matter experts from the affected business area. Include finance and accounting when the solution influences reserves, commissions, payments, reporting, or cost allocation. Risk, legal, compliance, privacy, and information security should participate according to the solution’s exposure.
Use a simple governance rhythm. A weekly working session can review progress, data issues, user feedback, and upcoming decisions. A steering group can meet at agreed milestones to approve scope changes, resolve ownership questions, and determine whether the POC should continue. Keep a decision log so that assumptions and trade-offs remain visible.
Vendor collaboration deserves structure as well. Require a named technical contact, response commitments, product documentation, implementation estimates, and access to relevant logs. Clarify which capabilities are available now and which appear only on a roadmap. A promising roadmap is not evidence for the current investment decision.
A useful evaluation should include a cost model that looks beyond subscription fees. Include implementation, integration, data preparation, security review, model monitoring, training, process redesign, support, and eventual exit costs. For insurers assessing investments connected with sustainability initiatives, broader tax considerations may also affect the financial case and should be reviewed by the appropriate specialists.
Test The Experience As Well As The Technology
Employees should use the solution in a workflow that resembles their normal work. Demonstrations led entirely by the vendor can hide confusing screens, slow response times, unclear alerts, or awkward exception handling. Give representative users realistic tasks and observe where they hesitate, override results, or create workarounds.
Human judgment must remain visible in areas where decisions carry customer, financial, or regulatory consequences. Test whether users understand why a recommendation was made, what evidence supports it, and how to challenge or correct it. Capture overrides as data rather than treating them as failure; repeated overrides may reveal a model weakness or a poorly designed process.
Customer impact should be assessed even when customers do not interact directly with the technology. A back-office tool can change response times, documentation, pricing consistency, or claims communication. Include service, complaints, accessibility, and fairness considerations in user acceptance testing.
Checks Before The First Live Test
A launch checklist helps prevent enthusiasm from replacing discipline. Before processing real or production-like transactions, confirm that:
- The business problem, scope, baseline, and decision owner are documented.
- Data access, privacy safeguards, retention rules, and security approvals are complete.
- Success thresholds, stop conditions, and reporting responsibilities are agreed upon.
- Users have received training and know when human review is mandatory.
- The vendor has supplied support contacts, technical documentation, and an exit plan.
Analyze Results And Plan The Next Step
At the end of the POC, compare results with the baseline and agreed thresholds. Separate measured outcomes from opinions, and explain any gaps in the sample, data, system integration, or user participation. A result that misses the target may still reveal that the technology works in a narrower process or requires a different configuration.
Calculate the economics at realistic scale. Translate time savings into capacity carefully; fewer minutes per transaction do not automatically produce cash savings unless staffing, workload, or growth assumptions support that conclusion. Include recurring controls, model validation, vendor management, and change management in the projected operating cost.
The final recommendation should have three possible paths: proceed, refine and retest, or stop. Proceeding may require a phased rollout with additional controls. Refining may involve better data, a narrower use case, or another vendor. Stopping is a sound result when the solution cannot meet risk, value, or usability requirements.
Turn Evidence Into A Scalable Investment
Scaling requires a new plan rather than an automatic extension of the POC. Establish production architecture, service levels, ownership, monitoring, training, incident management, and review intervals. Define how performance will be checked after launch, since models and business conditions can change over time.
Executives should receive a concise decision package: the original problem, tested scope, baseline, results, risks, total cost view, user feedback, and recommended path. This format supports transparent discussion among operations, finance, technology, risk, and accounting leaders. It also creates a reusable method for evaluating future insurtech opportunities.
IASA Conference participants can apply this approach across insurance accounting, finance, technology, risk management, tax, and customer administration. Bring a current technology idea to the next internal review with a defined decision, a representative sample, and measurable thresholds. When the evidence is clear, secure the right sponsorship for the next stage—or confidently direct investment elsewhere.