Where InsurTech platforms break
Insurance logic is fundamentally different from payment logic. The attack surface centers on policy lifecycle manipulation, claims fraud automation, and the complex relationships between policyholders, agents, and underwriters.
Claims processing exploitation
Automated claims approval systems are high-value targets. We test whether claims amounts can be inflated via API manipulation, whether supporting documentation validation actually works, and whether timing attacks can bypass manual review thresholds.
Premium & pricing manipulation
Dynamic pricing models often accept client-supplied risk parameters. We test whether users can manipulate age, location, vehicle type, or health data in API requests to artificially lower their premiums while maintaining full coverage.
Underwriting engine bypass
If the underwriting decision engine trusts client data without server-side validation, attackers can spoof risk profiles to guarantee approval for policies that should be declined — or manipulate coverage amounts beyond intended limits.
Agent & broker commission fraud
Agent networks in Nigerian insurance create referral and commission structures that can be exploited. We test whether agents can self-refer, whether commission calculations can be manipulated, and whether agent accounts have excessive permissions over policyholder data.
Automated claims approval bypass
An InsurTech platform auto-approved claims under ₦50,000 without manual review. The claims amount field was validated on the frontend but not on the API. An attacker submitted claims at ₦49,999 (avoiding review) with inflated line items that actually totaled ₦200,000+. The system processed the amount from the line items, not the header amount. Fix priority: immediate.
Regulatory context
With NAICOM modernizing insurance regulation and the NDPA placing strict requirements on personal data handling, InsurTech platforms face dual compliance obligations. Our testing is designed to satisfy both security and data protection requirements, providing evidence of appropriate technical measures for regulators.
Find the logic flaws in your insurance platform before a claims fraud spike does.
Get a Quick Security CheckRelated services and resources
InsurTech security testing combines penetration testing of the claims and underwriting logic with API security testing on policy management endpoints and authentication security reviews for agent and policyholder portals. For data protection compliance, see our NDPA compliance guide. Related industries: digital banking and lending platforms.
Frequently asked questions
What security risks are unique to InsurTech platforms?
InsurTech platforms face risks that standard web applications don't — automated claims approval bypasses, premium calculation manipulation through API parameter tampering, underwriting engine exploitation via spoofed customer data, and agent/broker commission fraud through referral code manipulation. These are business logic flaws that scanners can't detect.
Does NAICOM require security testing for InsurTech companies?
NAICOM (National Insurance Commission) guidelines increasingly emphasize data protection and cybersecurity controls for licensed insurers and InsurTech intermediaries. While specific penetration testing mandates are evolving, having independent security testing evidence demonstrates compliance with the 'appropriate technical measures' standard under both NAICOM guidelines and the NDPA.
How do you test automated claims processing for fraud vulnerabilities?
We analyze the end-to-end claims flow — from submission through verification to payout. We test whether claims can be artificially inflated, whether supporting documentation validation can be bypassed, whether the same incident can generate duplicate claims across linked policies, and whether approval thresholds can be manipulated to avoid manual review.
What policyholder data protection risks do InsurTech platforms face?
InsurTech platforms collect highly sensitive data — medical histories, property details, financial information, next-of-kin data, and identity documents. We test for BOLA/IDOR flaws that could expose one policyholder's data to another, insecure API endpoints that over-expose sensitive fields, and admin dashboard access control weaknesses.