How to assess the materiality of emerging technology risks for insurers

Emerging technology can strengthen underwriting, claims management, pricing, fraud detection and customer service. It can also create exposures that are difficult to quantify until a system fails, a model produces biased outcomes, or a critical supplier becomes unavailable. For insurers, the central task is to determine which technology risks could affect solvency, profitability, regulatory compliance, policyholders or confidence in the business.

Materiality is therefore broader than the value of a single failed project. A cloud outage affecting a modest application may interrupt claims payments across Australia. An artificial intelligence model may have a small direct cost but trigger remediation, litigation, regulatory scrutiny and reputational damage. A disciplined assessment connects technology exposure to the insurer’s financial statements, critical operations, risk appetite and obligations under the Australian regulatory framework.

Define materiality in an insurance context

Materiality begins with the potential effect on the insurer, its customers and the wider market. Financial thresholds matter, including impacts on earnings, capital, liquidity, reserves, expenses and reinsurance recoveries. However, a risk can be material even when its immediate dollar value is below a reporting threshold if it disrupts a critical service, affects vulnerable customers or breaches a legal requirement.

For an Australian insurer, the assessment should reflect the expectations of APRA, ASIC, the Australian Competition and Consumer Commission, the Office of the Australian Information Commissioner and relevant state regulators. APRA’s operational risk requirements place strong emphasis on critical operations, tolerance levels, service provider oversight and business continuity. Materiality should therefore be tested against the insurer’s ability to keep paying valid claims, processing policies and meeting regulatory commitments.

A useful definition combines impact, duration, scope and recoverability. A ten-minute outage may be tolerable for an internal analytics dashboard but unacceptable during a major hailstorm when thousands of customers are lodging claims. A technology risk becomes more significant when its consequences spread across products, brands, jurisdictions or distribution channels.

Map technology to critical business services

Risk teams should begin with services rather than applications. Map the customer and operational journeys that matter: quote and bind, premium collection, policy administration, claims lodgement, loss assessment, payments, complaints, renewals and regulatory reporting. Then identify the platforms, data stores, interfaces, models, people and third parties supporting each journey.

This approach exposes hidden dependencies. A claims platform may rely on an identity provider, a payment gateway, a geospatial data feed, a cloud region and an external loss-adjusting network. Each component may appear manageable in isolation, while the combined dependency creates a single point of failure. Mapping should include manual workarounds, recovery time objectives and the volume of transactions that staff could realistically process without automation.

Australian conditions make service mapping especially practical. A cyclone in Queensland, a bushfire near Adelaide or flooding in New South Wales can produce concentrated demand at short notice. Insurers should test whether systems, contact centres, assessors and suppliers can operate under surge conditions, including when telecommunications or transport links are disrupted. The location of data centres and outsourced teams also deserves attention when regional disasters affect access or staffing.

Evaluate financial and capital consequences

The finance function has a central role in judging materiality. Technology incidents can alter claim costs, expense ratios, premium recognition, reserving assumptions, investment operations and capital requirements. A corrupted pricing model may understate risk for months. An unavailable claims platform may cause payment delays, manual processing costs and a backlog that distorts operational metrics.

Assessment should include direct and indirect losses. Direct costs include restoration, forensic investigation, legal advice, customer compensation and vendor replacement. Indirect effects may include higher reinsurance costs, lost renewals, remediation programmes, regulatory penalties, delayed reporting and reduced productivity. For life insurers and health insurers, errors in calculations, eligibility decisions or customer communications can create long-tail liabilities that are harder to estimate.

Scenario analysis can translate uncertainty into useful ranges. Finance and risk teams might model a ransomware event lasting three days, an algorithmic pricing error affecting one product for a quarter, or a supplier outage during peak catastrophe claims. Scenarios should show best-case, expected and severe outcomes, with assumptions documented. The question is not whether a precise forecast is possible; it is whether decision-makers understand the plausible downside and have enough capital, liquidity and contingency capacity.

Scrutinise artificial intelligence and data risks

Artificial intelligence introduces model, data, conduct and governance risks. A generative AI tool may disclose confidential information, produce inaccurate advice or create records that cannot be readily explained. A machine-learning model used in underwriting may perform well in testing but produce unfair results for certain communities because its training data reflects historical gaps or inconsistent claims practices.

Materiality depends on the use case and the decision affected. Automation supporting document classification may have a limited impact if every output is reviewed. A model influencing pricing, coverage eligibility, claims settlement or fraud referrals carries a higher risk because errors can affect customer outcomes and regulatory obligations. Human oversight must be meaningful, with staff able to understand exceptions, challenge outputs and take over when the model is unreliable.

Data quality is equally important. Insurers should assess lineage, consent, retention, access controls, geographic restrictions and the accuracy of external datasets. Australian privacy requirements and community expectations are particularly relevant when organisations use sensitive information, location data or inferred characteristics. Testing should cover drift, adversarial inputs, unusual weather events and changes in customer behaviour, rather than relying solely on historical performance.

Measure cyber and third-party exposure

Cyber materiality should be assessed through business consequences rather than incident labels. Credential theft, ransomware, data exfiltration, software vulnerabilities and denial-of-service attacks can affect confidentiality, integrity and availability in different ways. An integrity failure in a claims or payment system may be more dangerous than a short outage because incorrect decisions can continue unnoticed.

Third-party concentration is a major consideration. Many insurers depend on a small group of cloud providers, core software vendors, managed security services, payment processors and data suppliers. The risk assessment should examine subcontractors, shared infrastructure, offshore processing, contractual rights, audit access, incident notification and exit arrangements. A supplier’s strong reputation does not remove the insurer’s accountability for customer and prudential outcomes.

Control effectiveness should be supported by evidence. Relevant indicators include privileged-access reviews, patching performance, recovery test results, unresolved audit findings, supplier concentration, model exceptions and the time needed to detect and contain incidents. Board reporting should distinguish between controls that exist on paper and controls that have operated successfully under realistic testing.

Set thresholds, indicators and escalation rules

A practical materiality framework uses several lenses instead of a single dollar limit. Financial impact, customer harm, operational disruption, regulatory breach, data sensitivity, duration, geographic spread and reversibility can each receive a defined rating. The highest rating across these dimensions can determine escalation, even when the financial estimate remains uncertain.

Thresholds should be linked to action. A critical risk may require board notification, executive ownership, enhanced monitoring and a tested contingency plan. A moderate risk might remain within management oversight but require a remediation date and risk acceptance by an authorised person. Emerging technology risks should have triggers for reassessment when a system moves from pilot to production, serves more customers, processes sensitive data or becomes embedded in a critical operation.

Indicators should be reviewed over time. Useful measures include model error rates by customer segment, incidents involving automated decisions, availability of critical services, recovery time performance, vendor concentration and the percentage of technology assets with known owners. The conference schedule can help Australian insurance professionals identify sessions on accounting, finance, technology, risk management and customer administration that support this cross-functional work.

Embed oversight across the three lines

Technology materiality cannot sit solely with the chief information officer or security team. The first line should own risks in product, claims, underwriting and operations. Risk and compliance functions should set methods, challenge assumptions and monitor adherence. Internal audit should test governance, controls, resilience and the reliability of management information.

Finance and accounting teams should be involved where technology affects reserves, reporting, capital or transaction accuracy. Legal, procurement, privacy, customer advocacy and actuarial specialists may also be needed. This group should maintain an inventory of emerging technologies, record risk assessments, identify accountable executives and document decisions to accept, reduce, transfer or avoid exposure.

Board oversight is strongest when reporting explains business consequences in plain language. Instead of listing technical vulnerabilities, management should show which critical services are affected, how long they could be unavailable, which customers may experience harm, what financial range is plausible and whether recovery has been tested. That format supports decisions about investment, risk appetite and strategic adoption.

A mature programme also treats materiality as changeable. A low-risk pilot can become significant after integration with policy administration or claims payments. A vendor acquisition can increase concentration overnight. New guidance, changing privacy expectations or a major catastrophe can alter the consequences of an existing weakness. Regular reassessment keeps the risk profile aligned with how technology is actually used.

Australian insurers can strengthen this discipline by bringing finance, technology, operations, actuarial, compliance and customer teams together before a new solution is deployed. Build a service map, document impact tolerances, run realistic scenarios and require evidence from critical suppliers. Use professional education and industry networking to compare approaches, then convert those insights into board-ready thresholds and tested response plans. A clear view of materiality enables confident innovation while protecting policyholders, capital and trust.