What's in this resource?
- How AI is transforming fraud detection and prevention in insurance, from deepfakes to anomaly detection
- Why fraud prevention is now a strategic priority, not just a back-office function
- The latest UK regulatory developments, including “Failure-to-Prevent-Fraud” obligations and IFB’s Connected to Protect strategy
- Case study: How NashTech helped insurers improve quote-to-sale ratios and reduce cost per quote
- The role of automation in boosting investigator efficiency and reducing false positives
- Governance essentials for AI in fraud prevention, including model risk, explainability and compliance controls
We guide you through the following
- How to build a layered fraud detection architecture using rules, machine learning, graph analytics and identity verification
- Practical steps to modernise your data infrastructure for real-time fraud scoring and decisioning
- How to detect synthetic identities, manipulated documents and voice-cloned calls using AI-powered tools
- A 12–18 month implementation roadmap with measurable outcomes and KPIs
