Smart AI, Smarter Fraud Protection: What P&C Insurers Need to Know

Introduction

Fraud is shifting quickly across the UK, Australia and New Zealand,  and artificial intelligence (AI) is reshaping how insurers detect and prevent it. The opportunity is huge: better accuracy, faster investigations and stronger protection for honest customers. But AI also brings risks that need managing carefully.

This guide breaks down the benefits, the challenges, and what fraud teams can do right now to make AI work for them.

Why AI Is Worth Considering for Fraud Detection

Fraud is a major contributor to rising premiums. Insurers that adopt modern detection tools early gain a cost and service advantage.

1. It boosts detection and improves efficiency
   AI, especially when combined with rules and analyst expertise, helps surface suspicious claims that traditional systems can’t spot. Some insurers in New Zealand saw their detection rates triple during AI-enabled pilots.
2. It helps uncover hidden networks
   Fraud rarely happens in isolation. AI-powered link analysis helps identify connections between people, devices, addresses, claim histories and repair networks.
3. It protects against faked or edited evidence
   From doctored photos to synthetic identities, fraudsters now use digital tools to manipulate evidence. AI-based image and document forensics help insurers stay ahead.
4. Regulators expect modern, well-governed systems
   Across Australia and New Zealand, insurers are being encouraged to use AI transparently and responsibly. In the UK and Europe, rules around automated decision-making and AI governance continue to tighten.
5. It helps insurers compete
   Fraud is a major contributor to rising premiums. Insurers that adopt modern detection tools early gain a cost and service advantage.

The Challenges Insurers Must Manage

1. Bias and fairness
   Models can unintentionally exclude or disadvantage groups of customers. This is increasingly under scrutiny from regulators.
2. Explainability
   Customers and regulators expect clear explanations for decisions – not “black-box” outputs. Any AI-driven referral should come with human-readable reasons.
3. Changing fraud tactics
   Fraud evolves fast. Without strong monitoring and retraining, models degrade and detection accuracy drops.
4. False positives
   An AI model that triggers too many low-value alerts can overwhelm investigators and reduce productivity.
5. Integration challenges
   Legacy systems, siloed data and inconsistent case-management tooling can limit the impact of even the most advanced AI models.
6. Growing use of generative tools by fraudsters
   Criminals are using generative AI to create false evidence, fake documents and convincing synthetic identities.

What Fraud Teams Should Do Differently

Adopting AI is not just a technology shift; it’s a working-practice shift. Fraud teams that get the most value tend to:

1. Use AI as a co-pilot, not a final decision-maker
   Analysts should have supporting context (feature reasons, linked entities, document forensics) to validate each alert.
2. Optimise for business outcomes, not just model accuracy
   Measure real-world impact:
   • Investigator throughput
   • Case resolution time
   • Recovery per case
   • Complaint rates
3. Start every complex case with a network view
   Kick off with entity resolution: people, devices, addresses, brokers and repairers.
4. Build a ‘deepfake and document fraud’ playbook
   Combine image forensics, metadata checks, location/device validation and manual checks for high-risk cases.
5. Create feedback loops with labelled outcomes
   Feed confirmed fraud/not-fraud outcomes directly back into the model training pipeline.
6. Participate in shared-intelligence networks
   In all three markets, collaboration across insurers accelerates the identification of organised fraud.

Governance Essentials for UK, AU and NZ Insurers

To use AI safely and effectively, strong governance is non-negotiable:

• Human-in-the-loop for significant decisions
• Reason codes and explainability for every alert
• Bias and fairness testing
• Stress testing and adversarial checks
• Audit logs, versioning and traceability
• Data minimisation and purpose-limitation
• Drift monitoring and scheduled model retraining
• Regional regulatory alignment (GDPR, AI governance, AU/NZ operational risk expectations)

Why This Matters Now

• Fraud volumes continue to rise across the UK, Australia and NZ
• Deepfake and document manipulation are becoming mainstream risks
• Regulators are increasing scrutiny on AI usage
• Customer trust depends on fair, explainable decisions
• Faster, better detection directly supports lower loss ratios and more competitive pricing

Insurers who delay investment risk falling behind both criminals and competitors.

How kbs Intelligence Helps

kbs Intelligence helps P&C insurers combine advanced analytics, machine learning and flexible rule frameworks so that:

• Analysts receive fewer but higher-quality alerts
• Every referral includes clear evidence and explainable reasoning
• Investigations are faster, more consistent and more accurate
• Organisations in the UK, Australia and NZ stay aligned with regional governance requirements

Our fraud detection software is built to integrate easily with your existing systems, adapt to emerging threats and provide analysts with the insight they need to act quickly and confidently.

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References

• DLA Piper – Using AI to commit insurance fraud (2025).
• KPMG New Zealand – Advancing AI across insurance (2024/11).
• Óskarsdóttir et al. – Network analytics for supervised fraud detection in insurance (2020).
• The Payments Association – AI and fraud prevention: False positives and black-box risks (2025).
• EY New Zealand – Building AI trust in financial services (2025).