14. InsurTech, AI and the Future of Insurance

Introduction and context

Insurance has always depended on information. Insurers collect information about people, property, health, vehicles, businesses, weather and past claims in order to estimate uncertain future losses. Traditionally, this information was gathered through proposal forms, inspections, intermediaries, medical examinations and claims investigations. Digital technologies are now changing the speed, scale, sources and uses of insurance information.

The term InsurTech refers to the application of technology to insurance activities, including product design, underwriting, distribution, policy administration, claims, fraud detection, risk prevention and customer service. It includes innovations developed by established insurers, technology firms, start-ups, reinsurers and platform businesses. InsurTech is therefore not simply the replacement of paper with software. It may change what is insured, how risk is measured, when premiums are paid, how claims are settled and which organizations participate in the insurance value chain.

Artificial intelligence, telematics, the Internet of Things (IoT), cloud computing, blockchain, embedded insurance, parametric products and automation are particularly important. Their value, however, cannot be assessed only by technical capability. Insurance is a regulated promise involving fairness, solvency, privacy, security, consumer protection and social legitimacy. A highly accurate model may still be unacceptable if it discriminates unfairly, cannot be explained, exposes sensitive data or creates incentives that undermine the purpose of insurance.

This chapter examines the technological transformation of insurance and the managerial choices associated with it. It links innovation with underwriting discipline, claims governance, enterprise risk management, regulation and the future of professional work.

Learning objectives

After studying this chapter, the learner should be able to:

1. Explain the meaning and strategic significance of InsurTech.

2. Describe the insurance applications of telematics, IoT, cloud platforms, blockchain and automation.

3. Distinguish embedded insurance from parametric insurance and conventional indemnity products.

4. Evaluate predictive, generative and decision-support applications of artificial intelligence.

5. Analyse model risk, algorithmic bias, privacy, cyber risk and operational resilience.

6. Assess the implications of climate change and emerging risks for digital insurance.

7. Recommend governance and implementation practices for responsible insurance innovation.

8. Evaluate how technology may alter insurance employment, skills and organizational structures.

From digitisation to intelligent insurance

Insurance technology can be understood through three overlapping stages. Digitisation converts physical records and processes into digital form. A scanned proposal form or an online policy document is an example. Digitalisation uses digital systems to improve an existing process, such as online quotation, automated renewal reminders or electronic claims submission. Digital transformation redesigns the business model and customer proposition, for example by offering insurance automatically at the point of purchasing a vehicle or by pricing motor cover according to observed driving behaviour.

The transformation of insurance is often described as a movement from periodic, document-based assessment to continuous, data-enabled risk management. Under a traditional model, an insurer may price a motor policy mainly from vehicle characteristics, location, age and claims history. With telematics, it may also use acceleration, braking, mileage, time of travel and location patterns. A property insurer may move from annual inspection to continuous monitoring through sensors that detect water leakage, smoke or temperature changes. The insurer becomes not only a compensator of loss but also a partner in preventing or reducing loss.

This shift creates a strategic tension. More granular information can improve risk classification and reduce uncertainty, but it can also make insurance less mutual. If every individual risk is priced with extreme precision, high-risk people may face unaffordable premiums or exclusion. Insurance depends on pooling and solidarity as well as statistical differentiation. Managers must therefore decide which variables are technically available, legally permissible and ethically appropriate.

The digital insurance architecture

A contemporary insurer usually operates through interconnected layers. At the customer layer are mobile applications, portals, chat interfaces, connected devices and partner platforms. The distribution and product layer contains quotation engines, policy administration systems, payment services and product rules. The data layer includes internal policy and claims records, external databases, sensor streams, images, text and third-party data. The analytical layer contains statistical models, machine-learning systems, fraud tools and decision engines. Infrastructure is supplied through networks, application programming interfaces (APIs), cloud computing and cybersecurity controls.

The architecture creates both efficiency and dependency. An API may allow a travel platform to obtain a quotation instantly from an insurer. Cloud computing may provide scalable processing and storage without the insurer owning all the physical infrastructure. Microservices can allow a firm to update a claims component without replacing the entire core system. However, dependence on vendors, common cloud providers and interconnected systems can concentrate operational risk. A disruption in a digital identity provider, payment gateway or cloud region may affect multiple insurers simultaneously.

A sound architecture separates systems according to criticality, protects sensitive data, records changes and supports recovery. It should also preserve human access for customers who cannot or do not wish to use digital channels. Digital convenience must not become digital exclusion.

Telematics and the connected risk environment

Telematics combines telecommunications, sensors and data analysis to monitor an asset or activity. In motor insurance, usage-based insurance may use a device installed in the vehicle, a smartphone application or data generated by the vehicle itself. Common variables include distance, speed, harsh braking, rapid acceleration, cornering, time of travel and, subject to legal and ethical controls, location.

Telematics enables two broad models. In a pay-as-you-drive model, premium is linked mainly to distance or usage. In a pay-how-you-drive model, premium reflects driving behaviour. The benefits may include more individualized pricing, feedback that encourages safer driving, improved accident reconstruction and faster assistance after a collision. Telematics can also support commercial fleet management and maintenance.

The risks are equally significant. A device may malfunction or transmit incomplete data. A driver may change behaviour merely while being monitored, making the data unrepresentative. Location information can reveal work, religious, medical or personal patterns. If the insurer does not explain how scores are calculated, customers may regard pricing as arbitrary. A robust programme therefore needs consent, data minimisation, security, a clear dispute process and an assessment of whether variables are genuinely related to insured risk.

The broader IoT includes connected homes, factories, farms, medical devices, wearables and supply chains. Water sensors can alert a household before a pipe causes extensive damage. Industrial sensors can identify abnormal vibration and support predictive maintenance. Agricultural devices can measure soil moisture and crop conditions. Wearables may assist wellness programmes, but health-related data requires especially careful governance because it can affect privacy, dignity and access to insurance.

IoT changes the economics of claims. Preventing a loss may be cheaper and more beneficial than paying compensation after it occurs. Yet prevention services can blur the boundary between insurance and technology services. The insurer must establish responsibility when a sensor fails, a warning is ignored or an automated intervention causes harm.

Cloud platforms, APIs and blockchain

Cloud computing provides on-demand access to computing resources, storage and software. Its principal insurance benefits include scalability, faster experimentation, remote access, standardised deployment and the possibility of integrating advanced analytical tools. Cloud arrangements may be public, private or hybrid. The relevant managerial question is not whether cloud is inherently safe, but whether the chosen arrangement provides appropriate confidentiality, availability, resilience, auditability and regulatory access.

APIs enable controlled communication between systems. They support open insurance ecosystems in which insurers, banks, brokers, automobile manufacturers, hospitals, retailers and digital platforms exchange information or services. APIs can reduce duplication and improve customer journeys, but poorly secured interfaces can expose policy, payment or health information. Authentication, authorization, encryption, monitoring and rapid revocation of access are essential.

Blockchain is a distributed ledger technology in which transactions are recorded across a network according to agreed rules. Its potential insurance uses include shared records, identity management, reinsurance documentation, claims histories and automated execution through smart contracts. A smart contract is software that performs specified actions when programmed conditions are met.

Blockchain is not a universal solution. It does not automatically guarantee that information entered into the ledger is accurate. An incorrect sensor reading or fraudulent input may remain immutably recorded. Governance is also difficult where several organizations share a ledger: they must agree on access rights, standards, correction procedures and responsibility. A conventional database may be more efficient when a trusted central operator already exists. Blockchain is most defensible where multiple parties need a shared, tamper-evident record and no single party should control the complete history.

Embedded and parametric insurance

Embedded insurance is insurance offered as an integrated feature of another product, service or transaction. Examples include travel cover offered during online ticket purchase, device protection included at the point of sale, or shipment insurance embedded in an e-commerce transaction. Embedded distribution reduces search costs and may reach customers at a moment when the relevant risk is salient. It can also support very simple products with automatic payment and administration.

The risks concern customer understanding, consent and responsibility. A customer may believe that cover is automatic when it is optional, or may not understand exclusions because the insurance is presented as a minor add-on. Distribution through a platform can also create conflicts of interest and dependency on a powerful intermediary. Product governance must ensure that the cover meets a genuine customer need and that disclosures remain intelligible in a highly compressed digital journey.

Parametric insurance pays a predetermined amount when an objectively measured parameter reaches an agreed threshold. A crop policy might pay when rainfall at specified stations falls below a defined level; a catastrophe policy might pay when an earthquake exceeds a stated magnitude and location condition. Unlike indemnity insurance, payment is not based on the policyholder’s measured actual loss.

Parametric products can provide rapid liquidity, reduce claims adjustment costs and cover risks where loss assessment is difficult. They are useful for disaster response, agricultural risks and business interruption linked to measurable events. Their central weakness is basis risk: the difference between the payout triggered by the parameter and the policyholder’s actual loss. A farmer may suffer severe local damage even though the regional rainfall index does not cross the threshold. Clear parameters, reliable data sources, transparent calculations and customer education are therefore essential.

Artificial intelligence across the insurance value chain

Artificial intelligence (AI) refers broadly to computational systems capable of performing tasks associated with human intelligence, such as classification, prediction, language processing, pattern recognition and decision support. Machine learning systems identify relationships in data rather than relying entirely on rules written in advance. Predictive AI estimates likely outcomes, such as claim frequency, severity, lapse or fraud probability. Generative AI creates new text, images, code or other content in response to instructions.

AI applications in insurance include:

• extracting information from proposal forms, medical reports, invoices and correspondence;
• triaging claims according to complexity and urgency;
• identifying unusual patterns that may indicate fraud;
• forecasting demand, catastrophe exposure, lapse and reserve needs;
• supporting underwriting with risk summaries and recommended questions;
• translating communications and providing customer-service assistance;
• generating draft policy explanations, correspondence and internal reports; and
• detecting damage in photographs or video submitted after an event.

The proper role of AI depends on the consequence of the decision. A language model drafting a routine email presents a different risk from a system that declines health coverage or determines a claim payment. Human review should be proportionate to impact, uncertainty and reversibility. Automation is most defensible when rules are clear, data quality is strong, outcomes are monitored and customers have meaningful recourse.

Generative AI creates particular risks. It may produce convincing but false information, disclose confidential material, reproduce biased patterns or fail to distinguish policy wording from general knowledge. It should not be treated as an authoritative source merely because its language is fluent. Insurers need approved models, secure environments, retrieval from controlled documents, output testing, logging and escalation to qualified personnel.

A decision framework for AI adoption

A practical framework for evaluating an insurance AI use case can be organized around six questions.

First, what is the business and customer problem? Technology should not be adopted merely because a tool is fashionable. Second, is the data relevant, lawful, representative and sufficiently accurate? Third, what is the impact of error, and who bears it? Fourth, can the decision be explained and challenged? Fifth, how will performance, drift and unintended effects be monitored? Sixth, can the organization withdraw or replace the system without disrupting critical services?

Model performance should be assessed using measures appropriate to the decision. A fraud model may be evaluated through precision and recall; a pricing model through predictive accuracy and calibration; a claims model through cycle time, leakage and customer outcomes. Aggregate accuracy is not enough. Performance should be tested across relevant groups and changing conditions.

The model life cycle includes problem definition, data preparation, development, validation, deployment, monitoring, change control and retirement. Independent validation is particularly important when the development team has incentives to emphasize benefits. Documentation should record purpose, data sources, assumptions, limitations, human controls and approval authority.

Model risk, bias and fairness

Model risk is the possibility that a model produces adverse consequences because of errors in design, data, implementation, interpretation or use. A model can be mathematically sophisticated and still be unsuitable. Risks include data leakage, overfitting, unstable relationships, incorrect proxies, coding errors and use outside the population for which the model was developed.

Algorithmic bias may arise from historical discrimination, under-representation, measurement differences, proxy variables or unequal access to data. Removing a sensitive attribute such as ethnicity does not necessarily eliminate bias if location, language or purchasing behaviour acts as a proxy. Fairness also has multiple meanings. Equal error rates, equal access, similar treatment and actuarial relevance may not all be achievable simultaneously.

Insurance requires a careful distinction between legitimate risk classification and unjustified discrimination. A variable should have a defensible relationship with the insured risk, comply with law and regulation, and be used in a way consistent with the social purpose of insurance. Governance should include impact assessments, subgroup testing, explainability proportionate to the decision, customer appeal, monitoring after deployment and senior accountability.

Explainability does not necessarily require revealing every line of code. It does require a meaningful account of the principal factors influencing an outcome, the information used, the role of human judgment and the steps available to correct an error. A statement such as “the algorithm rejected your claim” is not an adequate explanation.

Cybersecurity, privacy and operational resilience

Digital insurance expands the attack surface. Threats include ransomware, credential theft, phishing, data exfiltration, denial-of-service attacks, manipulation of claims information, insider misuse and attacks on connected devices. A breach may create financial loss, regulatory sanctions, litigation, reputational damage and harm to individuals whose health or identity data is exposed.

Cybersecurity should be treated as an enterprise risk rather than only an information-technology function. Important controls include identity and access management, least-privilege permissions, encryption, secure software development, network segmentation, vulnerability management, backup testing, incident response and third-party assurance. IoT devices require secure configuration, authenticated updates and a plan for devices that are no longer supported.

Operational resilience concerns the ability to continue important business services during disruption. An insurer should identify critical services, dependencies, maximum tolerable disruption, alternative procedures and recovery priorities. Cloud contracts should address data location, subcontracting, audit rights, service levels, exit arrangements and incident notification. Resilience also requires testing under realistic scenarios rather than relying only on written plans.

Privacy governance includes purpose limitation, data minimisation, retention controls, lawful processing, access rights and secure disposal. Consent alone may not solve every issue: consent can be poorly understood or effectively coerced where customers have no practical alternative. Sensitive data should be used only when necessary and proportionate to a legitimate insurance purpose.

Climate change and digital risk management

Climate change affects insurance through physical risks, transition risks and liability risks. Physical risks include more frequent or severe floods, storms, heat events, wildfire and drought. Transition risks arise from changes in technology, regulation, energy systems and asset values. Liability risks may arise from claims against organizations whose activities contribute to environmental harm or whose disclosures are inadequate.

Digital tools can improve climate risk assessment by combining satellite imagery, geospatial data, weather models, historical claims and asset information. They can support dynamic accumulation monitoring, early warning, resilience advice and parametric protection. However, historical data may not represent future conditions. A model trained on a stable climate can systematically underestimate emerging hazards. Scenario analysis and expert judgment are therefore necessary complements to statistical prediction.

Climate innovation also raises questions of affordability and availability. Better risk differentiation may lead to higher premiums or withdrawal from exposed areas. Insurers, governments, reinsurers and communities may need to combine prevention, building standards, public risk pools, subsidies and innovative covers. Technology can inform this debate but cannot eliminate the underlying physical risk.

An illustrative case: Meridian General’s digital motor transformation

Background

Meridian General, a fictional mid-sized insurer, operates in a competitive motor market. Its traditional pricing model uses proposal information and annual claims history. Acquisition costs are rising, younger drivers are difficult to price accurately, and claims staff spend considerable time on routine low-value losses.

Situation and problem

Management proposes a mobile telematics product linked to a cloud-based pricing engine. It also wants an AI tool to assess photographs of vehicle damage and a generative system to draft customer correspondence. The proposal promises lower costs and more individualized pricing, but consumer groups have raised concerns about location tracking, opaque scores and unequal treatment of drivers with older smartphones.

Analysis

A project team maps the data flows and discovers that the proposed application collects continuous location data even though distance, time and driving events would be sufficient for the intended pricing purpose. Testing shows that the damage-image model performs less reliably on poor-quality photographs and on some vehicle models that are under-represented in the training data. The pricing model gives favourable results to drivers who travel during off-peak hours, which may indirectly disadvantage workers with night or shift employment. The generative tool occasionally invents explanations for claim delays.

Approach and solution

Meridian adopts a staged implementation. It limits collection to necessary driving variables and provides a clear opt-in explanation. Customers can choose a non-telematics product, subject to transparent pricing. The model is tested by vehicle type, image quality and relevant customer groups. Low-confidence claims are routed to trained adjusters rather than automatically settled or rejected. The generative tool is restricted to drafting, uses approved policy content, and cannot send communications without human approval.

The insurer establishes a model-risk committee, an appeal process and monthly monitoring of pricing outcomes, claim cycle times, complaint rates and subgroup performance. Its vendor contracts include audit rights, breach notification duties and data-deletion requirements. A cyber exercise tests the loss of telematics connectivity and the availability of manual claims procedures.

Outcome

The programme produces faster handling of simple claims and more useful driving feedback, but management rejects the original ambition of fully automated decisions. The final business case is based on controlled automation, prevention and improved service rather than on eliminating professional judgment.

Learning points

The case illustrates that innovation is an organizational design problem, not merely a software purchase. Data minimisation can improve both privacy and efficiency. Human oversight is most valuable where uncertainty and customer impact are high. Fairness must be tested in the actual operating environment, and generative systems require stricter controls than their fluent outputs suggest.

Managerial and professional implications

InsurTech requires cooperation between actuaries, underwriters, claims professionals, information-security specialists, data scientists, legal advisers, compliance officers, product managers and customer representatives. Technical teams understand models and systems; insurance professionals understand coverage, exclusions, loss development and customer consequences. Neither perspective is sufficient alone.

Leaders should create governance that allocates accountability rather than allowing responsibility to disappear into a vendor or an algorithm. Important decisions include whether to build, buy or partner; how to integrate legacy systems; how to fund experimentation; and which processes should remain deliberately human. Procurement should evaluate not only cost and functionality but also data rights, resilience, explainability, portability and exit risk.

The workforce will not simply be divided between jobs that disappear and jobs that remain. Routine data entry, document classification and basic status enquiries are likely to become more automated. Demand may increase for professionals who validate models, investigate complex claims, interpret climate scenarios, design customer journeys, manage cyber risk and resolve exceptional cases. Continuous learning in statistics, data literacy, technology governance and communication will become important across the insurance profession.

Emerging directions and responsible innovation

Several developments deserve sustained attention. Real-time risk services may connect insurance with prevention, maintenance and safety. Synthetic data may assist testing where real data is sensitive, although synthetic data can still reproduce bias. Federated learning may permit organizations to train models without centralizing all underlying data, but it introduces technical and governance complexity. Digital identity and verifiable credentials may streamline onboarding. Distributed ledgers and smart contracts may mature in narrowly defined ecosystems rather than becoming universal infrastructure.

Autonomous vehicles, advanced medical technologies, climate volatility, cyber events and platform economies may alter the boundaries of insurable risk. Some risks may become more predictable, reducing traditional uncertainty; others may become correlated, systemic or difficult to model. Insurers will need to reconsider exclusions, accumulation limits, reinsurance, capital requirements and the role of public-private cooperation.

Responsible innovation can be expressed through a few durable principles: solve a genuine customer or risk problem; use only necessary and relevant data; test for disparate effects; maintain accountable human governance; secure the complete data life cycle; communicate in understandable language; monitor systems after deployment; and provide correction and appeal. Innovation should strengthen trust, not merely accelerate transactions.

Chapter Summary

InsurTech is transforming insurance from a periodic, document-centred activity into a connected and increasingly data-driven system. Telematics and IoT support usage-based pricing, prevention and continuous monitoring, while cloud platforms and APIs enable scalable ecosystems. Blockchain may assist shared records and smart contracts in suitable settings, but it does not solve data-quality or governance problems. Embedded insurance integrates cover into another transaction, whereas parametric insurance pays when an agreed measurable event occurs and therefore requires careful management of basis risk.

Predictive AI can support underwriting, claims, fraud detection and forecasting; generative AI can assist communication and document work. Their use must be governed through model validation, monitoring, explainability, human oversight and customer recourse. Model risk, algorithmic bias, privacy breaches, cyberattacks and operational concentration are central concerns. Climate change makes historical data less reliable and increases the need for scenario analysis and resilience planning. The future insurance workforce will combine insurance judgment with data, technology, governance and communication skills. Successful innovation is therefore not unrestricted automation, but controlled technology that improves risk management, fairness, resilience and customer value.

Key Terms

InsurTech: The use of technology to innovate or improve insurance products, processes and business models.

Telematics: The collection and transmission of information about an asset or activity through telecommunications and sensors.

Internet of Things (IoT): A network of connected devices that collect, communicate and sometimes act on data.

Usage-based insurance: Insurance pricing or cover linked to actual usage or observed behaviour.

Cloud computing: On-demand access to computing, storage or software resources through networked infrastructure.

Application programming interface (API): A controlled method through which software systems exchange data or services.

Blockchain: A distributed ledger that records transactions across a network according to agreed protocols.

Smart contract: Software that automatically performs specified actions when programmed conditions are met.

Embedded insurance: Insurance integrated into the purchase or use of another product or service.

Parametric insurance: Cover that pays a predetermined amount when a defined measurable parameter reaches an agreed threshold.

Basis risk: The difference between the loss experienced and the payout generated by a parametric trigger.

Artificial intelligence: Computational capability used to perform tasks associated with human intelligence, such as prediction or language processing.

Machine learning: A method in which systems learn patterns from data to produce predictions or classifications.

Generative AI: AI that creates new content such as text, images, code or summaries.

Model risk: The possibility of harmful outcomes caused by errors or inappropriate use of a model.

Algorithmic bias: Systematic and unfair differences in algorithmic outcomes associated with data, design or implementation.

Operational resilience: The ability to maintain important services during disruption and recover effectively.

Data minimisation: Collecting and retaining only data necessary for a legitimate purpose.

Predictive maintenance: Using data and models to identify likely equipment failure before it occurs.

Explainability: The ability to provide an understandable account of how a model contributed to an outcome.

Review Questions

Short Answer Questions

1. Distinguish digitisation, digitalisation and digital transformation in insurance.

2. What is the difference between pay-as-you-drive and pay-as-you-drive-by-usage approaches to telematics?

3. Define basis risk and explain why it matters in parametric insurance.

4. State two major applications of generative AI in insurance.

5. Why is data minimisation important in connected insurance?

Long Answer Questions

1. Explain how IoT and telematics can change the insurer’s role from loss compensation to loss prevention. Discuss associated risks.

2. Examine the potential and limitations of cloud computing, APIs and blockchain in insurance operations.

3. Discuss the applications of predictive and generative AI across the insurance value chain.

4. Explain model risk and algorithmic bias and propose a governance framework for managing them.

5. Analyse the implications of climate change, cyber risk and operational resilience for the future insurer.

Analytical / Application Questions

1. An insurer wants to use smartphone location data to price motor insurance. Design a governance process addressing relevance, privacy, fairness, security and customer appeal.

2. A parametric flood product pays when rainfall exceeds a threshold recorded at a regional station, but many customers suffer losses outside the station’s measurement area. Analyse the problem and recommend improvements.

3. A claims AI tool is highly accurate overall but produces more errors for older vehicle models and poor-quality photographs. Decide whether it should be deployed and specify the controls required.

22. Multiple Choice Questions with Answers

Question 1

Which example best represents digital transformation rather than simple digitisation?

A. Scanning paper policy files into a database

B. Sending policy documents by email instead of post

C. Embedding automatic travel cover into an online ticket purchase

D. Printing a digitally stored claims record

Correct Answer: C. Embedding automatic travel cover into an online ticket purchase

Question 2

A pay-how-you-drive motor policy is most directly based on which information?

A. Observed braking, acceleration and driving time

B. The insurer’s office rental costs

C. The customer’s preferred payment method

D. The age of the vehicle manufacturer

Correct Answer: A. Observed braking, acceleration and driving time

Question 3

What is the principal concern illustrated by basis risk in parametric insurance?

A. The policy cannot have a predefined payout

B. The trigger-based payout may not match the customer’s actual loss

C. Claims always require a lengthy physical inspection

D. Premiums cannot be collected digitally

Correct Answer: B. The trigger-based payout may not match the customer’s actual loss

Question 4

Which use of generative AI requires the strongest human control?

A. Drafting an internal meeting agenda

B. Summarising a public industry report

C. Suggesting headings for a training document

D. Producing an explanation for rejecting a customer’s claim

Correct Answer: D. Producing an explanation for rejecting a customer’s claim

Question 5

Why may removing ethnicity from an underwriting model fail to eliminate algorithmic bias?

A. Models cannot use any numerical information

B. Other variables may act as proxies for ethnicity

C. All machine-learning models are necessarily random

D. Bias exists only when a model uses blockchain

Correct Answer: B. Other variables may act as proxies for ethnicity

Question 6

Which control most directly supports operational resilience?

A. Increasing the length of marketing messages

B. Testing recovery procedures for critical services

C. Collecting continuous location data from every customer

D. Replacing all human claims staff with software

Correct Answer: B. Testing recovery procedures for critical services

Question 7

What is a significant limitation of blockchain in insurance?

A. It guarantees that information entered is factually correct

B. It prevents all cyberattacks automatically

C. It cannot correct inaccurate input merely because the record is tamper-evident

D. It can be used only for paper-based transactions

Correct Answer: C. It cannot correct inaccurate input merely because the record is tamper-evident

Question 8

An insurer’s climate model is trained entirely on historical loss data from a stable climate. What is the main analytical concern?

A. Historical data may not represent future climate conditions

B. Climate data cannot be stored in the cloud

C. Parametric products never use climate information

D. IoT devices eliminate the need for scenario analysis

Correct Answer: A. Historical data may not represent future climate conditions

Question 9

Which approach is most appropriate when an AI claims model has low confidence in an individual case?

A. Automatically reject the claim

B. Automatically pay every claim

C. Route the case for qualified human review

D. Delete the data used by the model

Correct Answer: C. Route the case for qualified human review

Question 10

Why should an insurer assess vendor exit arrangements when adopting a cloud platform?

A. To ensure the insurer can maintain or restore critical services if the relationship ends

B. To guarantee that all customers purchase embedded insurance

C. To eliminate the need for cybersecurity controls

D. To make model explanations unnecessary

Correct Answer: A. To ensure the insurer can maintain or restore critical services if the relationship ends