AI Governance

Automated claims assessment systems may incorrectly approve or deny insurance claims due to model hallucinations, bias, or inadequate training data, leading to customer dissatisfaction and regulatory violations.

AI-powered customer service agents may provide incorrect policy information, misadvise customers on coverage, or fail to properly escalate sensitive issues, creating liability exposure.

AI models used for risk assessment and premium calculation may perpetuate or amplify historical biases, leading to discriminatory pricing or coverage decisions that violate equality laws.

AI-based fraud detection systems may incorrectly flag legitimate claims as fraudulent, causing delays, customer frustration, and potential legal challenges.

AI models trained on customer data (health records, financial information, personal details) may memorise and leak sensitive information, violating GDPR and customer trust.

AI models deployed in production may gradually become less accurate as customer behaviour, market conditions, or claim patterns change, without obvious warning signs.

The company uses AI solutions from external vendors (claims automation, fraud detection, customer service). Vendor failures, security breaches, or service discontinuation create operational risk.

Complex AI models may produce decisions that cannot be adequately explained to regulators, customers, or internal stakeholders, violating transparency requirements.

Attackers use AI to create highly convincing phishing attacks, impersonation attempts, or fraudulent claims targeting employees and customers.